Description: The study area consisted of 2,651 watersheds encompassing 144 counties and parishes adjacent to the Gulf of Mexico. There were a few watersheds where flood claims information was not available and they were excluded from the analysis.
Name: Watersheds- High likelihood of flood damages
Display Field: Name
Type: Feature Layer
Geometry Type: esriGeometryPolygon
Description: The final model had an overall accuracy of 85% and we used this model to identify watersheds with a high probability of flood damage. 730 watersheds were classified as high flooding potential, meaning the model predicts a 70 percent or higher chance of flood claims in the future.
Description: 1168 watersheds were classified as high conservation opportunity, meaning 20% or more of the watershed area is undeveloped, unprotected and within the Partnership for Gulf Coast Land Conservation vision footprint.
Description: Our analysis identified 421 watersheds along the Gulf of Mexico that have both high likelihood of flood damages and high conservation opportunity.
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).
Description: AbstractIn this study, the U.S. Geological Survey, in cooperation with the U.S. Fish and Wildlife Service, quantified the potential for landward migration of tidal saline wetlands along the U.S. Gulf of Mexico coast under alternative future sea-level rise and urbanization scenarios. Our analyses focused exclusively on tidal saline wetlands (that is, mangrove forests, salt marshes, and salt flats), and we combined these diverse tidal saline wetland ecosystems into a single grouping, “tidal saline wetland.” Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by tidal saline wetlands. The primary product of this work is a public dataset that identifies locations where landward migration of tidal saline wetlands is expected to occur under alternative future sea-level rise and urbanization scenarios. In addition to identifying areas where landward migration of tidal saline wetlands is possible because of the absence of barriers, these data also identify locations where landward migration of these wetlands could be prevented by barriers associated with current urbanization, future urbanization, and levees.MethodsStudy areasOur study included U.S. lands along the northern Gulf of Mexico coast (that is, the area between the south Texas-Mexico border and the Florida Keys). This area includes the coastlines of the following five U.S. States: Texas, Louisiana, Mississippi, Alabama, and Florida. We developed two study areas for this research: a smaller, low-elevation study area and a larger, county-level study area. The smaller study area was developed explicitly for modeling TSW landward migration. The inland extent of this smaller study area was defined by a generalized 5-m elevation contour line, which was created from the USGS National Elevation Dataset 1/3-arc-second (10-m) elevation data (referenced to the North American Vertical Datum of 1988 [NAVD 88]), accessed in July 2012. The southeastern edge of the smaller study area (that is, in south Florida) was defined by the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014), which was used to exclude the Atlantic coastal zone from our analyses. For data analyses and model development, we divided the smaller study area into subunits based on tidal datum modeling regions. Our analyses incorporated spatially explicit and VDatum-region specific tidal datum transformations, which are available through the National Oceanic and Atmospheric Administration’s (NOAA) VDatum software tool version 3.1 (Myers and others, 2005). Within each VDatum region, we created a grid of cells that were set to match the registration and resolution of the elevation data used for each region (that is, 15-m cells for Florida and Louisiana, 10-m cells for Texas and Mississippi, and 5-m cells for Alabama). The larger study area was developed to assess and illustrate land use and future urban growth projected for large coastal cities in the region. This larger study area was developed by using coastal county boundaries except for in a part of south Florida, where the Everglades Level IV Omernik ecoregion boundary (Omernik, 1987; Omernik and Griffith, 2014) was used.Elevation dataThe resolution and quality of the elevation data used in this study varied by State. For Florida, we used a 15-m horizontal resolution bare-earth digital elevation model (DEM) created by the University of Florida GeoPlan Center. For Louisiana, we used a 15-m bare-earth DEM developed from the USGS 3D Elevation Program Coastal National Elevation Dataset topobathymetric model of the northern Gulf of Mexico. For Texas and Mississippi, we used 10-m bare-earth DEMs developed by NOAA (Marcy and others, 2011). For Alabama, we used a 5-m bare-earth DEM developed by NOAA (Marcy and others, 2011). All of the DEMs were hydroflattened (that is, for waterbodies, elevations were set to a constant value such as -0.5 m). For Louisiana and the Chenier Plain of Texas, we hydroflattened areas identified as stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the USGS National Hydrography Dataset (U.S. Geological Survey, 2001), as well as areas mapped as water in a recent marsh vegetation type land cover classification (Enwright and others, 2015). Although the DEMs we used contained data from multiple elevation sources with varying vertical accuracies, the data predominantly were from airborne light detection and ranging (lidar) acquisitions targeted for floodplain mapping by the Federal Emergency Management Agency (FEMA). We assumed all DEMs to have a vertical accuracy of plus or minus (±) 18-centimeter root mean square error (RMSE), which is the accuracy specified for lidar acquisitions by FEMA.Levee dataIn some coastal landscapes, dikes, levees, and other water control structures are commonly used to prevent or manage inundation. Often, these narrow linear features can be difficult to detect even with high spatial resolution DEMs (Lindsay, 2006). We obtained published levee data from the U.S. Army Corps of Engineers (USACE) National Levee Database (U.S. Army Corp of Engineers, 2015), as well as unpublished levee data from the South Florida Water Management District and the Louisiana Coastal Protection and Restoration Authority. We used these data to designate areas as leveed if they were protected from tidal inundation through a continuous levee (such as New Orleans, La.) or through levees that had only minor breaks that corresponded with a topographic high (such as Golden Meadow, La., and Freeport, Tex.). Our analyses included only readily available levee data (that is, publicly managed levees), and our criteria for designation as leveed excluded noncontinuous levees. This process produced similar results to the leveed area data layer in the USACE National Levee Database (U.S. Army Corp of Engineers, 2015). Although some of these leveed areas surround urban areas (such as parts of New Orleans and Freeport and Port Arthur, Tex.), other leveed areas surround undeveloped areas or agricultural areas (such as parts of south Florida and south Louisiana).Tidal datum data We used VDatum 3.1 to transform the vertical datum for DEMs from an orthometric datum (specifically, NAVD 88) to a local tidal datum (specifically, mean higher high water [MHHW]) (Marcy and others, 2011; Cooper and Chen, 2013; Murdukhayeva and others, 2013; Schmid and others, 2014); that is, we used the DEM and tidal datum data to identify the vertical position of each cell relative to local MHHW. A downside of transforming DEMs from an orthometric datum to a locally relevant tidal datum is the introduction of additional uncertainty associated with the tidal datum data. We determined the cumulative vertical uncertainty by using an approach similar to those described in several recent studies (Marcy and others, 2011; Mitsova and others, 2012; Cooper and Chen, 2013; Gesch, 2013; Schmid and others, 2014). We used the errors associated with the tidal datum transformation data and DEM data to calculate a 95-percent confidence interval for each cell. First, a cumulative vertical RMSE was calculated by adding the individual RMSEs from the DEM data and tidal datum data. This cumulative vertical RMSE was then multiplied by 1.96 to calculate the linear error at 95-percent confidence (LE95) (Federal Geodetic Data Committee, 1998; Gesch, 2013). For each VDatum region, we created two additional DEMs by adding or subtracting the LE95 from the original transformed and tidal datum-referenced DEM. These upper and lower vertical estimates illustrate the 95-percent confidence interval associated with the vertical position of the cell relative to MHHW. We refer to the unadjusted vertical estimate as “unadjusted” and to these upper and lower vertical estimates as “maximum” and “minimum,” respectively.Coastal wetland dataThe TSW ecosystems along the northern Gulf of Mexico coast are diverse and include salt marshes, mangrove forests, and tidal salt flats (West, 1977; Odum and others, 1982; Withers, 2002). At the region scale, this diversity is driven primarily by climatic drivers (that is, air temperature and rainfall regimes; Osland and others, 2013, 2014, 2015, in press). Salt marshes are dominant in the cold and wet climatic zones (that is, upper Texas, Louisiana, Mississippi, Alabama, and northwest Florida). Mangrove forests are dominant in the hot and wet climatic zones (that is, south Florida). Salt flats are dominant in the hot and dry climatic zones (that is, south Texas). For the sake of simplicity in communication and because of the lack of an adequate dataset that distinguishes between these types of wetlands in a consistent manner across the entire northern Gulf of Mexico, we combined these different types of wetland ecosystems into a single TSW category for our analyses. Our TSW category was developed from habitat data available through the USFWS National Wetlands Inventory (NWI). The USFWS NWI program has produced detailed maps of wetlands since the mid-1970s. We used USFWS NWI data for our wetland coverage data because it is vector-based habitat data developed through an intensive manual process of expert photointerpretation of high-resolution aerial photography, which makes the data better suited for identifying linkages to higher resolution (that is, 5- to 15-m resolution) DEMs in comparison to raster-based wetland coverage datasets (such as NOAA Coastal Change Assessment Program) developed through automated processes from moderate resolution (that is, 30-m) satellite imagery. One negative aspect of USFWS NWI data is that the data are not temporally synchronized across the entire study area. For each cell, we used the best available USFWS NWI data. For areas near Brazoria, Tex.; part of the Texas Chenier Plain; and much of Louisiana, we used preliminary USFWS NWI data (that is, data recently finalized by the USGS and currently under review by the USFWS). The USFWS NWI habitat classes (Cowardin and others, 1979) were used to determine whether TSW was present or absent within cells. Presence of TSW was defined as cells that contained the estuarine intertidal wetland USFWS NWI classes. Absence of TSW (that is, not tidal saline wetlands [NTSW]) included upland areas (that is, areas not mapped as wetlands in USFWS NWI), as well as areas designated as palustrine wetlands by USFWS NWI. Cells containing estuarine subtidal, marine, riverine, or lacustrine wetland USFWS NWI classes were excluded from subsequent analyses.Identifying current landward tidal saline wetland boundariesWithin each VDatum region, we used the elevation relative to MHHW data and the current TSW presence/absence (TSW/NTSW) data to develop VDatum region-specific thresholds for the current landward TSW boundary. Within the context of the TSW boundary, current refers to the most recent tidal epoch (1983–2001) for which the tidal datum data was obtained. For these analyses, we restricted our sampling extent to areas with elevations less than or equal to 2 m above MHHW. For most VDatum regions, we sampled 10 million cells stratified by the area covered by TSW or NTSW. The data from these 10 million cells were used to identify the current TSW boundary within each region. Elevation thresholds for the landward TSW boundary were determined by using a recursive partitioning approach where the first node of a classification tree was determined as the threshold (Qian and others, 2003). Recursive partitioning was conducted in R (http://cran.r-project.org) by using the Rpart package (Therneau and Atkinson, 1997). All of the remaining cells within a region (that is, all cells not used for threshold identification) were used to validate the model results (mean = 15 million validation cells; range = 0.8 to 45 million validation cells). The TSW elevation thresholds were evaluated within each VDatum region by using Cohen’s kappa statistic (Cohen, 1960).There were a few exceptions to this general description of our sampling design and data analyses. Because of the coarser spatial resolution of DEMs in Florida (that is, 15-m resolution, which results in fewer cells per region), we were not able to obtain a sample of 10 million cells for the Pensacola, Fla., and west Florida regions. For these two regions, we sampled 70 percent of the total cells stratified by the area covered by TSW or NTSW (that is, roughly 1.9 and 5.9 million cells for these two regions, respectively). Within the Louisiana Mississippi River Delta region and the Texas and Louisiana Chenier Plain region, leveed areas were excluded from sampling to avoid the effects that the numerous low-lying leveed and nonwetland cells would have on the TSW boundary determination. Within the Texas and Louisiana Chenier Plain region, we excluded an agricultural zone to the north of the cheniers and east of the Calcasieu River from our threshold identification analyses. This area contains extensive privately owned low-lying and leveed rice fields (that is, areas that are NTSW and not identified as leveed within the USACE or Louisiana Coastal Protection and Restoration Authority levee databases). For the Mobile-Tensaw River Delta region, there were large differences in the TSW boundaries within and outside of the delta; hence, we created two subregions (that is, a delta area and a nondelta area) and identified separate thresholds for these subregions.Sea-level rise scenariosFor modeling future TSW landward migration, we used five SLR scenarios: 0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m increase in sea level by 2100. Three of these SLR scenarios (0.5-, 1.2-, and 2.0-m SLR) were identified within an interagency U.S. governmental guidance document (Parris and others, 2012) produced for the 2013 U.S. National Climate Assessment (Melillo and others, 2014). One of the objectives of the guidance document produced by Parris and others (2012) was to provide coastal managers with a set of plausible SLR trajectories that could be used to assess vulnerability, impacts, and adaptation strategies. From this guidance document, we selected the “Intermediate-Low,” “Intermediate-High,” and “Highest” scenarios identified by Parris and others (2012). These three scenarios represent a 0.5-, 1.2-, and 2.0-m SLR by 2100, respectively. See Horton and others (2014) for a discussion of how the three selected scenarios from Parris and others (2012) compare to the Intergovernmental Panel on Climate Change AR5 scenarios (Church and others, 2013), as well as to an expert elicitation-based evaluation of potential SLR scenarios. For each of the five scenarios, we modeled the following five time steps: 2030, 2040, 2050, 2060, and 2100. The SLR at each time step-scenario combination was determined by using the following equation:y= a×(t1-t0 )+b×(t1-t0)2 (1)where y represents the SLR by a given year, a represents the rate of historical SLR, t1 represents the end year, t0 represents the initial year, and b represents an acceleration constant that is specific to each SLR scenario. The rate of historical SLR (that is, a) used for these calculations was 1.7 millimeters per year, which represents the rate of SLR for the 20th century (Church and White, 2011). The initial year (that is, t0) was 1992, which is the middle of the most recent tidal epoch (1983–2001) for which the tidal datum data used in our data analyses was determined.Identifying future tidal saline wetlandsWe used the VDatum region-specific TSW elevation thresholds in combination with the five alternative future SLR scenarios to identify future TSW landward migration. We assumed that contemporary elevation thresholds relative to MHHW would be similar in the future. For each VDatum region, we identified future TSWs by adding the SLR increment of interest to the VDatum-specific TSW elevation threshold. For future scenarios, cells with elevations that fell below the VDatum-specific TSW elevation threshold were coded to be TSW. We removed low-elevation cells that were not hydrologically connected by removing isolated cells that lacked neighboring cells that also fell below the TSW elevation threshold (that is, we used an 8-side rule, which includes cardinal and diagonal directions [Poulter and Halpin, 2008]). To depict uncertainty associated with the elevation and tidal transformation data, we also produced a maximum and minimum TSW boundary estimate by using the upper and lower 95-percent confidence limits for the vertical position relation to MHHW (Gesch, 2013; Nielsen and Dudley, 2013).To create a seamless 30-m resolution dataset for the northern Gulf of Mexico, we resampled future TSW model results from the varying resolutions used at the State and VDatum levels (that is, 5 m to 15 m). We then reprojected the data to Albers Equal-Area Conic and set the registration to match that of the U.S. National Land Cover Dataset (NLCD [Jin and others, 2013]). From these data, we removed areas that were classified as current TSW, water bodies (such as ocean, rivers, and lakes), subtidal wetlands, or marine wetlands. These areas were removed by using a mask that included areas mapped as the following: (1) subtidal and marine wetlands in USFWS NWI; (2) contemporary TSW in USFWS NWI; and (3) stream/river, canal/ditch, lake/pond, reservoir, or sea/ocean in the high-resolution version of the National Hydrography Dataset (U.S. Geological Survey, 2001). The mask was converted from native vector formats to a 30-m raster dataset with registration set to match the NLCD. We created presence/absence raster layers (that is, layers that indicated whether cells are TSW and NTSW) for minimum future TSW, future TSW, and maximum future TSW. We then applied a minimum mapping unit of about 8,100 square meters to all TSW migration areas.Urban areasWe assessed potential TSW migration barriers associated with future projected and current urban areas. For the future projected urban extent, we created a presence/absence grid, which identified future urban areas as those with a 95-percent probability of becoming urban by 2100 as determined by Terando and others (2014). By using the SLEUTH urban growth model (Clarke and Gaydos, 1998), Terando and others (2014) developed future urban sprawl simulations for the southeastern United States. We resampled these SLEUTH data from 60 to 30 m to match the spatial resolution and registration of the NLCD. For the current urban extent, we used a combination of the SLEUTH current urban data (Terando and others, 2014) and the NLCD 2011 data. Areas defined as “Developed” in NLCD 2011 (that is, low, medium, and high intensity developed areas and “Developed, Open Space”) were considered to be currently urban. We set the current urban area extents to be the maximum extent of both the SLEUTH and NLCD current urban datasets.Data ProductsOne of the primary products of this work is a public dataset that identifies locations where TSW landward migration is expected to occur under alternative future SLR and urbanization scenarios. In addition to identifying areas where TSW landward migration is possible because of the absence of barriers, these data also identify locations where TSW landward migration could be prevented by barriers associated with current urbanization, future urbanization, and levees. The dataset includes five time steps (2030, 2040, 2050, 2060, and 2100), five SLR scenarios (0.5-, 1.0-, 1.2-, 1.5-, and 2.0-m SLR by 2100), and three vertical uncertainty categories (unadjusted, minimum, and maximum), which equates to 102 total files because of the presentation of the data in two formats, as well as the various time step/scenario-uncertainty combinations. These data, including the accompanying metadata, have been made available through ScienceBase at https://www.sciencebase.gov/catalog/item/55f742a8e4b0477df11c0a2b. The DOI for these data is 0.5066/F7NK3C3D. The data files are organized into two groups: (1) migration with barrier group and (2) migration without barrier group.The migration with barrier group includes a total of 75 raster files (that is, five SLR scenarios × five time steps × three uncertainty categories). Each file includes the following categories: (1) current TSW; (2) current urban; (3) future TSW; (4) future urban; (5) future TSW/current urban; (6) future TSW/future urban; (7) leveed; (8) future TSW/leveed; and (9) current TSW/future urban. The various data sources and analyses used to create these categories are described in the “Methods” subsections.The migration without barrier group includes 25 raster files (that is, five SLR scenarios × five time steps). These files identify areas where, based on elevation data, TSW landward migration is expected. Note that, unlike the migration with barrier group data, the files within this group do not identify the various barriers that could prevent landward migration as separate categories (such as urban areas or leveed areas). For visualization purposes, we have also included a polygon feature class of barriers (that is, current urban and leveed areas) with these data. Each file within this group includes data from the three uncertainty categories described in the “Methods” subsections. Because cells in the rasters must be mutually exclusive (that is, they cannot belong to multiple uncertainty categories), users of this data group should treat these data as cumulative when interested in categories above the minimum wetland landward migration.Data LimitationsDue to the large study area and the region-based objectives associated with this study, we used a relatively simple model and the best available data to identify areas where TSW landward migration may occur across the U.S. Gulf of Mexico coast. Our results are dependent upon the quality and availability of elevation, tidal datum, and TSW data. For local-scale decisions, where data quality is poor and (or) where additional variables play an important role, higher resolution data and (or) a more complex model may be needed. There are many different models available for assessing TSW responses to SLR, and the appropriate models depend upon the questions of interest. Prior to using these data, we recommend that individuals carefully consider which models are most appropriate for their objectives. For more background information regarding models of TSW response to SLR, the following resources serve as a good starting point: Perillo and others (2009), Fagherazzi and others (2012), Doyle and others (2015), and Passeri and others (2015). Please note that our data products do not assess or illustrate TSW loss or the ability of current TSWs to keep pace with SLR via local vertical movement. Although current TSW data are included in some of the data products, our representation does not indicate that those TSWs will be able to keep pace with SLR. The focus of this project was solely on TSW landward migration, and we did not evaluate the potential for TSW loss or local adaptation to SLR via vertical movement. The ability of current TSWs to keep pace with SLR is a much different question and beyond the scope of this project.SummaryIn this study, we identified areas along the U.S. Gulf of Mexico coast where tidal saline wetland (TSW) landward migration is expected to occur under alternative future sea-level rise (SLR) and urbanization scenarios. In addition to identifying areas where topographic conditions are expected to enable TSW migration, our analyses also identify areas where barriers (that is, barriers associated with current urban, future urban, and leveed lands) are expected to impede TSW landward migration. The primary product of this study is a dataset that identifies these barriers and opportunities for TSW landward migration. Collectively, our approach and findings can provide useful information for scientists and environmental planners working to develop future-focused adaptation strategies for conserving coastal landscapes and the ecosystem goods and services provided by TSWs.References CitedBarbier, E.B., Hacker, S.D., Kennedy, Chris, Koch, E.W., Stier, A.C., and Silliman, B.R., 2011, The value of estuarine and coastal ecosystem services: Ecological Monographs, v. 81, no. 2, p. 169–193. [Also available at http://dx.doi.org/10.1890/10-1510.1.]Church, J.A., Clark, P.U., Cazenave, Anny, Gregory, J.M., Jevrejeva, Svetlana, Levermann, Anders, Merrifield, M.A., Milne, G.A., Nerem, R.S., Nunn, P.D., Payne, A.J., Pfeffer, W.T., Stammer, Detlef, and Unnikrishnan, A.S., 2013, Sea level change, in Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, p. 1137–1216.Church, J.A., and White, N.J., 2011, Sea-level rise from the late 19th to the early 21st century: Surveys in Geophysics, v. 32, no. 4, p. 585–602. [Also available at http://dx.doi.org/10.1007/s10712-011-9119-1.]Clarke, K.C., and Gaydos, L.J., 1998, Loose-coupling a cellular automaton model and GIS—Long-term urban growth prediction for San Francisco and Washington/Baltimore: International Journal of Geographical Information Science, v. 12, no. 7, p. 699–714. [Also available at http://dx.doi.org/10.1080/136588198241617.]Cohen, Jacob, 1960, A coefficient of agreement for nominal scales: Educational and Psychological Measurement, v. 20, no. 1, p. 37–46. [Also available at http://dx.doi.org/10.1177/001316446002000104.]Cooper, H.M., and Chen, Qi, 2013, Incorporating uncertainty of future sea-level rise estimates into vulnerability assessment—A case study in Kahului, Maui: Climatic Change, v. 121, no. 4, p. 635–647. [Also available at http://dx.doi.org/10.1007/s10584-013-0987-x.]Cowardin, L.M., Carter, Virginia, Golet, F.C., and LaRoe, E.T., 1979, Classification of wetlands and deepwater habitats of the United States: Washington, U.S. Department of the Interior, Fish and Wildlife Service, Report FWS/OBS–79/31, 131 p. [Also available at http://www.fws.gov/wetlands/documents/classification-of-wetlands-and-deepwater-habitats-of-the-united-states.pdf.]Doyle, T.W., Chivoiu, Bogdan, and Enwright, N.M., 2015, Sea-level rise modeling handbook—Resource guide for coastal land managers, engineers, and scientists: U.S. Geological Survey Professional Paper 1815, 76 p. [Also available at http://dx.doi.org/10.3133/pp1815.]Doyle, T.W., Krauss, K.W., Conner, W.H., and From, A.S., 2010, Predicting the retreat and migration of tidal forests along the northern Gulf of Mexico under sea-level rise: Forest Ecology and Management, v. 259, no. 4, p. 770–777. [Also available at http://dx.doi.org/10.1016/j.foreco.2009.10.023.]Engle, V.D., 2011, Estimating the provision of ecosystem services by Gulf of Mexico coastal wetlands: Wetlands, v. 31, no. 1, p. 179–193. [Also available at http://dx.doi.org/10.1007/s13157-010-0132-9.]Enwright, N.M., Hartley, S.B., Couvillion, B.R., Brasher, M.G., Visser, J.M., Mitchell, M.K., Ballard, B.M., Parr, M.W., and Wilson, B.C., 2015, Delineation of marsh types from Corpus Christi Bay, Texas, to Perdido Bay, Alabama, in 2010: U.S. Geological Survey Scientific Investigations Map 3336, 1 sheet, scale 1:750,000. [Also available at http://dx.doi.org/10.3133/sim3336.]Fagherazzi, S., Kirwan, M.L., Mudd, S.M., Guntenspergen, G.R., Temmerman, S., D’Alpaos, A., van de Koppel, J., Rybczyk, J.M., Reyes, E., Craft, C., Clough, J., 2012, Numerical models of salt marsh evolution: Ecological, geomorphic, and climatic factors, vol. 50, RG1002, p. 1–28. [Also available at http://onlinelibrary.wiley.com/doi/10.1029/2011RG000359/full.] Federal Geodetic Data Committee, 1998, Geospatial positioning accuracy standards, part 3—National standard for spatial data accuracy: Washington, D.C., Federal Geodetic Data Committee, FGDC–STD–007.3–1998, 28 p. Field, D.W., Reyer, A.J., Genovese, P.V., and Shearer, B.D., 1991, Coastal wetlands of the United States—An accounting of a valuable national resource: Silver Spring, Md., National Oceanic and Atmospheric Administration, 131 p.Gesch, D.B., 2013, Consideration of vertical uncertainty in elevation-based sea-level rise assessments—Mobile Bay, Alabama case study: Journal of Coastal Research, v. 63, p. 197–210. [Also available at http://dx.doi.org/10.2112/SI63-016.1.]Horton, B.P., Rahmstorf, Stefan, Engelhart, S.E., and Kemp, A.C., 2014, Expert assessment of sea-level rise by AD 2100 and AD 2300: Quaternary Science Reviews, v. 84, p. 1–6. [Also available at http://dx.doi.org/10.1016/j.quascirev.2013.11.002.]Jin, Suming, Yang, Limin, Danielson, Patrick, Homer, Collin, Fry, Joyce, and Xian, George, 2013, A comprehensive change detection method for updating the National Land Cover Database to circa 2011: Remote Sensing of Environment, v. 132, p. 159–175. [Also available at http://dx.doi.org/10.1016/j.rse.2013.01.012.]Lindsay, J.B., 2006, Sensitivity of channel mapping techniques to uncertainty in digital elevation data: International Journal of Geographical Information Science, v. 20, no. 6, p. 669–692. [Also available at http://dx.doi.org/10.1080/13658810600661433.]Marcy, Doug, Herold, Nate, Waters, Kirk, Brooks, William, Hadley, Brian, Pendleton, Matt, Schmid, Keil, Sutherland, Mike, Dragonov, Kyle, McCombs, John, and Ryan, Sean, 2011, New mapping tool and techniques for visualizing sea level rise and coastal flooding impacts, in Wallendorf, L.A., Jones, Chris, Ewing, Lesley, and Battalio, Bob, eds., Solutions to coastal disasters conference: Anchorage, Alaska, American Society of Civil Engineers, p. 474–490. [Also available at http://dx.doi.org/10.1061/41185(417)42.]Melillo, J.M., Richmond, T.C., and Yohe, G.W., eds., 2014, Climate change impacts in the United States—The third national climate assessment: Washington, U.S. Global Change Research Program, 841 p.Millennium Ecosystem Assessment, 2005, Ecosystems and human well-being—Synthesis: Washington, Island Press, 137 p. [Also available at http://www.millenniumassessment.org/documents/document.356.aspx.pdf.]Mitsova, Diana, Esnard, A.M., and Li, Yanmei, 2012, Using enhanced dasymetric mapping techniques to improve the spatial accuracy of sea level rise vulnerability assessments: Journal of Coastal Conservation, v. 16, no. 3, p. 355–372. [Also available at http://dx.doi.org/10.1007/s11852-012-0206-3.]Murdukhayeva, Angelica, August, Peter, Bradley, Michael, LaBash, Charles, and Shaw, Nigel, 2013, Assessment of inundation risk from sea level rise and storm surge in northeastern coastal national parks: Journal of Coastal Research, v. 291, no. 6a, p. 1–16. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-12-00196.1.]Myers, Edward, Wong, Adeline, Hess, Kurt, White, Stephen, Spargo, Emily, Feyen, Jesse, Yang, Zhizhang, Richardson, Phil, Auer, Carol, Sellars, Jon, Woolard, Jason, Roman, Daniel, Gill, Stephen, Zervas, Chris, and Tronvig, Kristen, 2005, Development of a National VDatum and its application to sea level rise in North Carolina: San Diego, Calif., U.S. Hydrographic Conference. [Also available at http://vdatum.noaa.gov/download/publications/2005_myers_USHydro.pdf.]Nielsen, M.G., and Dudley, R.W., 2013, Estimates of future inundation of salt marshes in response to sea-level rise in and around Acadia National Park, Maine: U.S. Geological Survey Scientific Investigations Report 2012–5290, 20 p. [Also available at http://pubs.usgs.gov/sir/2012/5290/.]Odum, W.E., McIvor, C.C., and Smith, T.J., III, 1982, The ecology of mangroves of south Florida—A community profile: Washington, U.S. Fish and Wildlife Service, Office of Biological Services, FWS/OBS–81/24, 144 p. [Also available at http://www.nwrc.usgs.gov/techrpt/81-24.pdf.]Omernik, J.M., 1987, Map supplement: Ecoregions of the conterminous United States: Annals of the Association of American Geographers, v. 77, no. 1, p. 118-125. [Also available at http://www.tandfonline.com/doi/abs/10.1111/j.1467-8306.1987.tb00149.x.]Omernik, J.M., and Griffith, G.E., 2014, Ecoregions of the conterminous United States—Evolution of a hierarchical spatial framework: Environmental Management, v. 54, no. 6, p. 1249-1266. [Also available at http://link.springer.com/article/10.1007%2Fs00267-014-0364-1#page-1.]Osland, M.J., Day, R.H., From, A.S., McCoy, M.L., McLeod, J.L., and Kelleway, J.J., 2015, Life stage influences the resistance and resilience of black mangrove forests to winter climate extremes: Ecosphere, v. 6, no. 9, article 160, 15 p. [Also available at http://www.esajournals.org/doi/pdf/10.1890/ES15-00042.1.]Osland, M.J., Enwright, N.M., Day, R.H., and Doyle, T.W., 2013, Winter climate change and coastal wetland foundation species—Salt marshes versus mangrove forests in the southeastern United States: Global Change Biology, v. 19, no. 5, p. 1482–1494.Osland, M.J., Enwright, N.M., Day, R.H., Gabler, C.A., Stagg, C.L., Grace, J.B., in press, Beyond just sea-level rise—Considering macroclimatic drivers within coastal wetland vulnerability assessments to climate change: Global Change Biology, DOI: 10.1111/gcb.13084. [Also available at http://onlinelibrary.wiley.com/doi/10.1111/gcb.13084/abstract.]Osland, M.J., Enwright, N.M., and Stagg, C.L., 2014, Freshwater availability and coastal wetland foundation species—Ecological transitions along a rainfall gradient: Ecology, v. 95, no. 10, p. 2789–2802. [Also available at http://dx.doi.org/10.1890/13-1269.1.]Pace, N.L., 2010, Wetlands or seawalls?—Adapting shoreline regulation to address sea level rise and wetland preservation in the Gulf of Mexico: Journal of Land Use & Environmental Law, v. 26, p. 327–363.Parris, Adam, Bromirski, Peter, Burkett, Virginia, Cayan, Dan, Culver, Mary, Hall, John, Horton, Radley, Knuuti, Kevin, Moss, Richard, Obeysekera, Jayantha, Sallenger, Abby, and Weiss, Jeremy, 2012, Global sea level rise scenarios for the United States National Climate Assessment: Silver Spring, Md., National Oceanic and Atmospheric Administration, Technical Report OAR CPO–1, 37 p. [Also available at http://scenarios.globalchange.gov/sites/default/files/NOAA_SLR_r3_0.pdf.]Passeri, D.V., Hagen, S.C., Medeiros, S.C., Bilskie, M.V., Alizad, K., Wang, D., 2015. The dynamic effects of sea level rise on low-gradient coastal landscapes: A review: Earth’s Future, v. 3, p. 159-181. [Also available at http://onlinelibrary.wiley.com/doi/10.1002/2015EF000298/full.] Perillo, G.M.E., Wolanski, E., Cahoon, D.R., and Brinson, M.M., eds., 2009, Coastal wetlands—An integrated ecosystem approach: Amsterdam, Netherlands, Elsevier, 941 p.Poulter, B., and Halpin, P.N., 2008, Raster modelling of coastal flooding from sea‐level rise: International Journal of Geographical Information Science, v. 22, no. 2, p. 167–182. [Also available at http://dx.doi.org/10.1080/13658810701371858.]Qian, S.S., King, R.S., and Richardson, C.J., 2003, Two statistical methods for the detection of environmental thresholds: Ecological Modelling, v. 166, no. 1–2, p. 87–97. [Also available at http://dx.doi.org/10.1016/S0304-3800(03)00097-8.]Schmid, Keil, Hadley, Brian, and Waters, Kirk, 2014, Mapping and portraying inundation uncertainty of bathtub-type models: Journal of Coastal Research, v. 30, no. 3, p. 548–561. [Also available at http://dx.doi.org/10.2112/JCOASTRES-D-13-00118.1.]Stocker, T.F., Qin, Dahe, Plattner, G.K., Tignor, M.M.B., Allen, S.K., Boschung, Judith, Nauels, Alexander, Xia, Yu, Bex, Vincent, and Midgley, P.M., eds., 2013, Climate change 2013—The physical science basis—Working group I contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change: Cambridge, United Kingdom, Cambridge University Press, 1,535 p.Terando, A.J., Costanza, Jennifer, Belyea, Curtis, Dunn, R.R., McKerrow, Alexa, and Collazo, J.A., 2014, The southern megalopolis—Using the past to predict the future of urban sprawl in the southeast U.S.: PLoS ONE, v. 9, no. 7, 8 p. [Also available at http://dx.doi.org/10.1371/journal.pone.0102261.]Therneau, T.M., and Atkinson, E.J., 1997, An introduction to recursive partitioning using the RPART routines: Rochester, N.Y., Mayo Clinic Foundation, Technical Report 61, 67 p.Titus, J.G., Hudgens, D.E., Trescott, D.L., Craghan, M., Nuckols, W.H., Hershner, C.H., Kassakian, J.M., Linn, C.J., Merritt, P.G., McCue, T.M., O'Connell, J.F., and Tanski, J., 2009, State and local governments plan for development of most land vulnerable to rising sea level along the US Atlantic coast: Environmental Research Letters, v. 4, 7 p. [Also available at http://dx.doi.org/10.1088/1748-9326/4/4/044008.]U.S. Army Corps of Engineers, 2015, National levee database: Washington, D.C., U.S. Army Corps of Engineers, accessed January 6, 2015, at http://nld.usace.army.mil/egis/f?p=471:1:0.U.S. Geological Survey, 2001, National Hydrography Dataset: U.S. Geological Survey Unnumbered Series Report. [Also available at http://pubs.er.usgs.gov/publication/70046927.]West, R.C., 1977, Tidal salt-marsh and mangal formations of Middle and South America, in Chapman, V.J., ed., Wet coastal ecosystems: Amsterdam, Netherlands, Elsevier, p. 193–213.Withers, Kim, 2002, Wind-tidal flats, in Tunnell, J.W., and Judd, F.W., eds., The Laguna Madre of Texas and Tamaulipas: College Station, Tex., Texas A&M University Press, p. 114–126.
Copyright Text: The work described here was conducted by Nicholas M. Enwright, Kereen T. Griffith, and Michael J. Osland, at the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC). Acknowledgement of these authors and the U.S. Geological Survey (USGS), Wetland and Aquatic Research Center (WARC) as a data source would be appreciated in products developed from these data. Such acknowledgement as is standard for citation and legal practices for data sources is expected by users of these data.
This project was funded by the U.S. Fish and Wildlife Service and the U.S. Geological Survey Ecosystem Mission Area. It is a Multi-Landscape Conservation Cooperative (LCC) project which includes the four Gulf coast LCCs (i.e., the Gulf Coast Prairie, Gulf Coastal Plains and Ozarks, South Atlantic, and Peninsular Florida LCCs). We thank National Oceanic and Atmospheric Administration (NOAA) for contributing regional VDatum DEM's and the VDatum transformation software tool version 3.1. We also thank Adam Terando for providing the urban growth data (Terando and others, 2014).