This dataset was created to represent the land surface elevation at 1:24,000 scale for Florida. The elevation contour lines representing the land surface elevation were digitized from United States Geological survey 1:24,000 (7.5 minute) quadrangles and were compiled by South Florida, South West Florida, St. Johns River and Suwannee River Water Management Districts and FDEP. QA and corrections to the data were supplied by the Florida Department of Environmental Protection's Florida Geological Survey and the Division of Water Resource Management. This data, representing over 1,000 USGS topographic maps, spans a variety of contour intervals including 1 and 2 meter and 5 and 10 foot. The elevation values have been normalized to feet in the final data layer. Attributes for closed topographic depressions were also captured where closed (hautchered) features were identified and the lowest elevation determined using the closest contour line minus one-half the contour interval. This data was derived from the USGS 1:24,000 topographic map series. The data is more than 20 years old and is likely out-of-date in areas of high human activity.

The U.S. Geological Survey, in cooperation with Broward County Environmental Planning and Resilience Division, has developed a groundwater/surface-water model to evaluate the response of the drainage infrastructure and groundwater system in Broward County to increases in sea level and potential changes in precipitation. The model was constructed using a modified version of MODFLOW-NWT, with the surface-water system represented using the Surface-Water Routing process and the Urban Runoff Process. The surface-water drainage system within this newly developed model actively simulates the extensive canal network using level-pool routing and active structures representing gates, weirs, culverts, and pumps. Steady-state and transient simulation results represented historical conditions (2013-17). Simulation results incorporating increased sea level and precipitation were used to evaluate the effects on the surface-water drainage system and wet season groundwater levels. Four future sea-level scenarios were simulated by modifying the historical inputs for both the steady-state and the transient models to represent mean sea levels of 0.5, 2.0, 2.5, and 3.0 ft above the North American Vertical Datum of 1988. This USGS data release contains all of the input and output files for the simulations described in the associated model documentation report. (https://doi.org/10.3133/sir20225074)

IMPORTANT IN THE OPEN DATA PORTAL THERE IS ONE FEATURE CLASS FOR ALL POTENTIOMETRIC SURFACE MAPS. IF YOU WANT JUST ONE TIME PERIOD CLICK ON THE TABLE TAB, THEN CLICK ON THE DATE FIELD. IN THE FILTER BOX ON THE RIGHT ENTER THE MAP YOU WANT (MAY 2000, SEPTEMBER 2015, ETC.). WHEN YOU CLICK THE DOWNLOAD DATASET BUTTON SELECT SPREADSHEET OR KML OR SHAPEFILE UNDER THE FILTERED DATASET OPTION. YOU WILL ONLY GET THE FILTERED DATA FROM THIS DOWNLOAD.Contour lines are created for the potentiometric surface of the upper Floridan aquifer from water level data submitted by the water management districts. The points associated with the water level data are added to Geostatistical Analyst and ordinary kriging is used to interpolate water level elevation values between the points. The Geostatistical Analyst layer is then converted to a grid (using GA Layer to grid tool) and then contour lines (using the Contour tool). Post editing is done to smooth the lines and fix areas that are hydrologically incorrect. The rules established for post editing are: 1) rivers intersecting the UFA follow the rule of V’s; 2) potentiometric surface contour line values don’t exceed the topographic digital elevation model (DEM) in unconfined areas; and 3) potentiometric surface contour lines don’t violate valid measured water level data. Errors are usually located where potentiometric highs are adjacent to potentiometric lows (areas where the gradient is high). Expert knowledge or additional information is used to correct the contour lines in these areas. Some additional data may be river stage values in rivers that intersect the Floridan aquifer or land elevation in unconfined areas. Contour lines created prior to May 2012 may be calculated using a different method. The potentiometric surface is only meant to describe water level elevation based on existing data for the time period measured. The contour interval for the statewide map is 10 feet and is not meant to supersede regional (water management district) or local (city) scale potentiometric surface maps.

This is a tiled collection of the 3D Elevation Program (3DEP) and is one meter resolution. The 3DEP data holdings serve as the elevation layer of The National Map, and provide foundational elevation information for earth science studies and mapping applications in the United States. Scientists and resource managers use 3DEP data for hydrologic modeling, resource monitoring, mapping and visualization, and many other applications. The elevations in this DEM represent the topographic bare-earth surface. USGS standard one-meter DEMs are produced exclusively from high resolution light detection and ranging (lidar) source data of one-meter or higher resolution. One-meter DEM surfaces are seamless within collection projects, but, not necessarily seamless across projects. The spatial reference used for tiles of the one-meter DEM within the conterminous United States (CONUS) is Universal Transverse Mercator (UTM) in units of meters, and in conformance with the North American Datum of 1983 ...

The raster is created from the finalized contour lines for May 2015, An estimated zero contour line is placed along the coast from Bay County to Pinellas County and along Volusia County. This contour aids in making a more reasonable surface along the coast, since water level vaues are less than the 10 foot contour interval shown on the contour map (see potentiometric contour map). Using the Topo to Raster tool, a 500 X 500 meter grid is created by using the May 2015 contour lines, the estimated zero contour line, estimated water value points along the suwannee river and the May 2015 water level data. The interpolated elevation value in each cell is in feet mean sea level. Please reference the metadata for contact information.

The raster is created from the finalized contour lines for May 2017, An estimated zero contour line is placed along the coast from Bay County to Pinellas County and along Volusia County. This contour aids in making a more reasonable surface along the coast, since water level vaues are less than the 10 foot contour interval shown on the contour map (see potentiometric contour map). Using the Topo to Raster tool, a 500 X 500 meter grid is created by using the May 2017 contour lines, the estimated zero contour line, estimated water value points along the suwannee river and the May 2017 water level data. The interpolated elevation value in each cell is in feet mean sea level, datum NGVD29.See Metadata for Contact info.

This downloadable zip file contains an ESRI File Geodatabase (FGDB) that is compatible with most versions of ArcGIS Pro, ArcMap, and AutoCAD Map 3D or Civil 3D. To view the geodatabase’s contents, please download the zip file to a local directory and extract its contents. This zipped geodatabase will require approximately 1.38 GB of disc space (1.49 GB extracted). Due to its size, the zip file may take some time to download.This downloadable file geodataase (FGDB) includes Topographic Countours and Spot Elevations derived from LiDAR collected in spring of 2024 by Dewberry Engineers in coordination with Tallahassee - Leon County GIS. The contours were extracted at a 2 foot interval with index contours every 10 feet. Lidar Acquisition Executive SummaryThe primary purpose of this project was to develop a consistent and accurate surface elevation dataset derived from high-accuracy Light Detection and Ranging (lidar) technology for the Tallahassee Leon County Project Area. The lidar data were processed and classified according to project specifications. Detailed breaklines and bare-earth Digital Elevation Models (DEMs) were produced for the project area. Data was formatted according to tiles with each tile covering an area of 5000 ft by 5000 ft. A total of 876 tiles were produced for the project encompassing an area of approximately 785.55 sq. miles. The dataset was created by TLCGIS from lidar data acquired by a Riegl CQ-1560i lidar system from January 14, 2024 through January 19, 2024.ORIGINAL COORDINATE REFERENCE SYSTEMData produced for the project were delivered in the following reference system.Horizontal Datum: The horizontal datum for the project is North American Datum of 1983 with the 2011 Adjustment (NAD 83 (2011))Vertical Datum: The Vertical datum for the project is North American Vertical Datum of 1988 (NAVD88)Coordinate System: NAD83 (2011) State Plane Florida North (US survey feet)Units: Horizontal units are in U.S. Survey Feet, Vertical units are in U.S. Survey Feet.Geiod Model: Geoid12B (Geoid 12B) was used to convert ellipsoid heights to orthometric heights).

ESCAMBIA: The Light Detection and Ranging (LiDAR) LAS dataset is a survey of select areas within Escambia County, Florida. These data were produced for Dewberry and Davis LLC. The Escambia County LiDAR Survey project area consists of approximately 803 square miles. The LiDAR point cloud was flown at a density sufficient to support a maximum final post spacing of 6 feet for unobscured areas. Lan...

To generate the land surface grid for this project, VIEWLOG was used to re-sample a 100-ft digital elevation model (DEM) of best-available data for the Lower West Coast planning region of the SFWMD. The original DEM was composited in 2013 from multiple sources. The 100 x 100 foot cell size of the DEM was resampled to a grid size of 2000 x 2000 feet (Liebermann and Bedell, 2013). The vertical datum is NGVD29. The contour interval is in feet.The objectives of this study were to create regional hydrogeologic maps including contour maps showing unit surfaces and thicknesses, and cross-sections representative of both the surficial aquifer system (SAS) and intermediate aquifer system (IAS). The maps, source data, and metadata used to generate these products will be archived in a manner suitable for model implementation and regulatory use in a publically accessible format. The results will be incorporated into the forthcoming Lower West Coast Surficial Aquifer System and Intermediate Aquifer System Model (LWCSIM), which will evaluate the potential impact of existing and projected groundwater withdrawals in all SAS and IAS aquifers within the region over the next several decades.For full documentation, please see Technical Publication WS-35, "Hydrogeologic Unit Mapping Update for the Lower West Coast Water Supply Planning Area," dated August 2015 by Elizabeth Geddes, Emily Richardson P.G., and Anne Dodd P.G. , Water Supply Bureau, Water Resources Division, South Florida Water Management District, West Palm Beach, Florida.https://www.sfwmd.gov/sites/default/files/documents/ws-35_lwc_hydrogeologic_mapping_083115.pdf

These are Digital Elevation Model (DEM) data for Sarasota county,as part of the required deliverables for the FL Peninsular FDEM 2018 D19 DRRA Lidar project.Class 2 (ground) lidar points in conjunction with the hydro breaklines were used to create a 2.5 foot hydro-flattened Raster DEM.Dataset Description: FL Peninsular FDEM 2018 D19 DRRA Lidar project called for the Planning, Acquisition, processing and derivative products of lidar data to be collected at a nominal pulse spacing (NPS) of 0.35 meter. Project specifications are based on the U.S. Geological Survey National Geospatial Program Base Lidar Specification, Version 1.3. The data was developed based on a horizontal projection/datum of NAD83 (2011), State Plane, U.S Survey Feet and vertical datum of NAVD88 (GEOID12B), U.S Survey Feet. Sarasota County GIS has subsequently re-projected the data to NAD83 State Plane HARN, U.S. Survey Feet.Raster Cell Size: 2.5 footRequired Vertical Accuracy: The required NVA is: 19.6 cm (0.64 ft)at a 95% confidence level

This raster provides the average groundwater elevations in NAVD 88 for the month of May, based on the results of the U.S. Geological Survey groundwater model for Miami-Dade – Urban Miami-Dade (UMD), used to predict groundwater levels for year 2040, considering sea level rise above the baseline conditions, using NRCIII forecast, which assumes a 1.0 ft sea-level rise increase, from a year 2009 -0.9 ft mean sea-level NAVD88 to a 2040 0.1 ft.

Points spaced 50 feet apart representing ground surface were derived from classified LiDAR flown by Digital Aerial Surveys LLC between May 7, 2018 and March 1, 2019. The Lee County portion was flown May 8 to October 29, 2018.Compressed LAS files (Albers meters) were downloaded from USGS' ftp site. LAZ were decompressed using rapidlasso's LASzip. GeoCue's LP360 for ArcMap tools were utilized to extract point coordinates from the LAS surface using points classes Ground (2), Water (9) and Bridge Deck (17). The points generated from these coordinates were projected to Florida State Plane West, feet using ArcGIS' Project tool and the elevation converted from meters to feet (m * 3.28084). The X and Y coordinates in this dataset are in State Plane feet. Elevation is in feet NAVD 88 vertical datum.The horizontal accuracy is +/-0.783 meters or +/-2.57 feet (at the 95% confidence level) and the vertical accuracy is +/-0.175 meters (+/-0.57 feet) for nonvegetated and +/-0.190 meters (+/- 0.62 feet) for vegetated areas. See the report, LiDAR Project Report 140G0218F0179, FL SOUTHWEST 2018 D18, prepared by Digital Aerial Solutions, LLC for United States Geological Survey, for full accuracy details.Additional information can be found here: https://coast.noaa.gov/htdata/raster2/elevation/USGS_FL_Southwest_2018_9049/2018_swfl_m9049_met_forHumans.html.

The raster is created from the finalized contour lines for May 2016, An estimated zero contour line is placed along the coast from Bay County to Pinellas County and along Volusia County. This contour aids in making a more reasonable surface along the coast, since water level vaues are less than the 10 foot contour interval shown on the contour map (see potentiometric contour map). Using the Topo to Raster tool, a 500 X 500 meter grid is created by using the May 2016 contour lines, the estimated zero contour line, estimated water value points along the suwannee river and the May 2015 water level data. The interpolated elevation value in each cell is in feet mean sea level, datum NGVD29. Please reference the metadata for contact information.

To assess site resilience, we divided the coast into 1,232 individual sites centered around each tidal marsh or complex of tidal habitats. For each site, we estimated the amount of migration space available under four sea-level rise scenarios and we identified the amount of buffer area surrounding the whole tidal complex. We then examined the physical properties and condition characteristics of the site and its features using newly developed analyses as well as previously published and peer-reviewed datasets.Sites vary widely in the amount and suitability of migration space they provide. This is determined by the physical structure of the site and the intactness of processes that facilitate migration. A marsh hemmed in by rocky cliffs will eventually convert to open water, whereas a marsh bordered by low lying wetlands with ample migration space and a sufficient sediment supply will have the option of moving inland. As existing tidal marshes degrade or disappear, the amount of available high-quality migration space becomes an indicator of a site’s potential to support estuarine habitats in the future. The size and shape of a site’s migration space is dependent on the elevation, slope, and substrate of the adjacent land. The condition of the migration space also varies substantially among sites. For some tidal complexes, the migration space contains roads, houses, and other forms of hardened structures that resist conversion to tidal habitats, while the migration space of other complexes consists of intact and connected freshwater wetlands that could convert to tidal habitats.Our aim was to characterize each site’s migration space but not predict its future composition. Towards this end, we measured characteristics of the migration space related to its size, shape, volume, and condition, and we evaluated the options available to the tidal complex to rearrange and adjust to sea level rise. In the future, the area will likely support some combination of salt marsh, brackish marsh and tidal flat, but predictions concerning the abundance and spatial arrangement of the migration space’s future habitats are notoriously difficult to make because nature’s transitions are often non-linear and facilitated by pulses of disturbance and internal competition. For instance, in response to a 1.4 mm increase in the rate of SLR, the landward migration of low marsh cordgrass in some New York marshes appears to be displacing high marsh (Donnelly & Bertness 2001). Thus, our assumption was simply that a tidal complex with a large amount of high quality and heterogeneous migration space will have more options for adaptation, and will be more resilient, than a tidal complex with a small amount of degraded and homogenous migration space.To delineate migration space for the full project area, we requested the latest SLR Viewer (Marcy et al. 2011) marsh migration data, with no accretion rate, for all the NOAA geographic units within the project area, from NOAA (N. Herold, pers. comm., 2018). Specifically, we obtained data for the following states in the project area: Virginia, North Carolina, South Carolina, Georgia, and Florida. As accretion is very location-dependent, we chose not to use one of the three SLR Viewer accretion rates because they were flat rates applied across each geographic unit. For each geography, we combined four SLR scenarios (1.5’, 3’, 4’, and 6.5’) with the baseline scenario to identify pixels that changed from baseline. We only selected cells that transitioned to tidal habitats (unconsolidated shoreline, salt marsh, and transitional / brackish marsh) and not to open water or upland habitat. We combined the results from each of the geographies and projected to NAD83 Albers. The resultant migration space was then resampled to a 30-m grid and snapped to the NOAA 2010 C-CAP land cover grid (NOAA, 2017). The tidal complex grid and the migration space grid were combined to ensure that there were no overlapping pixels. While developed areas were not allowed to be future marsh in NOAA’s SLR Viewer marsh migration model, we still removed all roads and development, as represented in the original 30-m NOAA 2010 C-CAP land cover grid, from the migration space. We took this step as differences in spatial resolution between the underlying elevation and land cover datasets could occasionally result in small amounts of development in our resampled migration space. The remaining migration space was then spatially grouped into contiguous regions using an eight-neighbor rule that defined connected cells as those immediately to the right, left, above, or diagonal to each other. The region-grouped grid was converted to a polygon, and the SLR scenario represented by each migration space footprint was assigned to each polygon. Finally, the migration space scenario polygons that intersected any of the tidal complexes were selected. Because a single migration space polygon could be adjacent to and accessible to more than one tidal complex unit, each migration space polygon was linked to their respective tidal complex units with a unique ID by restructuring and aggregating the output from a one-to-many spatial join in ArcGIS. This linkage enabled the calculation of attributes for each tidal complex such as total migration space acreage, total number of migration space units, and the percent of the tidal complex perimeter that was immediately adjacent to migration space. Similar attributes were calculated for each migration space unit including total tidal complex acreage and number of tidal complex units.This dataset shows additional migration space units in the project area for the 3.0-foot sea level rise scenario. Additional migration space units are migration space units that did not spatially intersect current tidal marshes or were spatially disjunct from the migration space of current tidal marshes. Because additional migration space units were not directly associated with a tidal complex, these units were NOT used in the calculation of a tidal complex’s resilience score. The spatial separation could be due to roads, waterbodies, waterways, oil and gas fields, etc. Depending on local factors and context, the degree to which these features will prevent marshes from accessing the additional migration space areas in the future is unknown and likely varies by site.There were thousands of small and disconnected additional migration space areas, often individual pixels, typically found in urban settings, remote upstream riverine areas, or far from any migration space units or tidal marshes. We did not consider these isolated occurrences as additional migration space because they are unlikely to be important future marsh areas. We identified isolated migration space areas using the following approach. First, for unconfirmed additional migration space areas, an iterative analysis of the Euclidean distance from current tidal marshes and their migration space areas, including confirmed additional migration space, was performed. Next, pixels that did not meet the distance thresholds in the first step but were within 60 meters of a NHDPlus v2 (USEPA & USGS, 2012) streamline were retained as additional migration space. Any remaining pixels less than or equal to two acres in size were then removed from the additional migration space. Finally, visual inspection was used to remove isolated migration space areas that were not identified through the previous steps. We assigned resilience scores to the additional migration space areas using several approaches. First, we spatially allocated resilience scores based on Euclidean distance from tidal marshes or migration space units. While this approach was a good starting point, there were migration space areas whose score assignments had to be done manually or by taking the highest of two equidistant nearby scores. The manual assignment included straightforward cases, but often it was unclear how marshes might move into a migration space area (e.g., will marsh travel through waterways to nearby migration space areas; will marsh use all migration space areas along a waterway or waterbody or only on the same side as the current marsh?). For sites with unclear relationships to current marshes and their migration space, the highest resilience score in the general geographic area of the additional migration space was assigned. Consequently, please interpret the scores of the additional migration space with caution and use local expertise and knowledge as you see fit. REFERENCESChaffee, C, Coastal policy analyst for the R.I. Coastal Resources Management Council. personal communication. April 4, 2017.Donnelly, J.P, & Bertness, M.D. 2001. Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated sea-level rise. PNAS 98(25) www.pnas.org/cgi/doi/10.1073/pnas.251209298Herold, N. 2018. NOAA Sea Level Rise (SLR) Viewer marsh migration data (10-m), with no accretion rate, for all SLR scenarios from 0.5-ft. to 10.0-ft. for VA, NC, SC, GA, and FL. Personal communication Jan. 24, 2018. Lerner, J.A., Curson, D.R., Whitbeck, M., & Meyers, E.J., Blackwater 2100: A strategy for salt marsh persistence in an era of climate change. 2013. The Conservation Fund (Arlington, VA) and Audubon MD-DC (Baltimore, MD).Lucey, K. NH Coastal Program. Personal Communication. April 4, 2017.Maine Natural Areas Program. 2016. Coastal Resiliency Datasets, Schlawin, J and Puryear, K., project leads. http://www.maine.gov/dacf/mnap/assistance/coastal_resiliency.htmlMarcy, D., Herold, N., Waters, K., Brooks, W., Hadley, B., Pendleton, M., Schmid, K., Sutherland, M., Dragonov, K., McCombs, J., Ryan, S. 2011. New Mapping Tool and Techniques For Visualizing Sea Level Rise And Coastal Flooding Impacts. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Originally published in the Proceedings of the 2011 Solutions to Coastal Disasters Conference, American Society of Civil Engineers

This raster provides the average groundwater elevations in NAVD88 for the month of October, based on the results of the U.S. Geological Survey groundwater model for Miami-Dade – Urban Miami-Dade (UMD), used to predict groundwater levels for year 2040, considering sea level rise above the baseline conditions, using NRCIII forecast, which assumes a 1.0 ft sea-level rise increase, from a year 2009 -0.9 ft mean sea-level NAVD88 to a 2040 0.1 ft.

To assess site resilience, we divided the coast into 1,232 individual sites centered around each tidal marsh or complex of tidal habitats. For each site, we estimated the amount of migration space available under four sea-level rise scenarios and we identified the amount of buffer area surrounding the whole tidal complex. We then examined the physical properties and condition characteristics of the site and its features using newly developed analyses as well as previously published and peer-reviewed datasets.Sites vary widely in the amount and suitability of migration space they provide. This is determined by the physical structure of the site and the intactness of processes that facilitate migration. A marsh hemmed in by rocky cliffs will eventually convert to open water, whereas a marsh bordered by low lying wetlands with ample migration space and a sufficient sediment supply will have the option of moving inland. As existing tidal marshes degrade or disappear, the amount of available high-quality migration space becomes an indicator of a site’s potential to support estuarine habitats in the future. The size and shape of a site’s migration space is dependent on the elevation, slope, and substrate of the adjacent land. The condition of the migration space also varies substantially among sites. For some tidal complexes, the migration space contains roads, houses, and other forms of hardened structures that resist conversion to tidal habitats, while the migration space of other complexes consists of intact and connected freshwater wetlands that could convert to tidal habitats.Our aim was to characterize each site’s migration space but not predict its future composition. Towards this end, we measured characteristics of the migration space related to its size, shape, volume, and condition, and we evaluated the options available to the tidal complex to rearrange and adjust to sea level rise. In the future, the area will likely support some combination of salt marsh, brackish marsh and tidal flat, but predictions concerning the abundance and spatial arrangement of the migration space’s future habitats are notoriously difficult to make because nature’s transitions are often non-linear and facilitated by pulses of disturbance and internal competition. For instance, in response to a 1.4 mm increase in the rate of SLR, the landward migration of low marsh cordgrass in some New York marshes appears to be displacing high marsh (Donnelly & Bertness 2001). Thus, our assumption was simply that a tidal complex with a large amount of high quality and heterogeneous migration space will have more options for adaptation, and will be more resilient, than a tidal complex with a small amount of degraded and homogenous migration space.To delineate migration space for the full project area, we requested the latest SLR Viewer (Marcy et al. 2011) marsh migration data, with no accretion rate, for all the NOAA geographic units within the project area, from NOAA (N. Herold, pers. comm., 2018). Specifically, we obtained data for the following states in the project area: Virginia, North Carolina, South Carolina, Georgia, and Florida. As accretion is very location-dependent, we chose not to use one of the three SLR Viewer accretion rates because they were flat rates applied across each geographic unit. For each geography, we combined four SLR scenarios (1.5’, 3’, 4’, and 6.5’) with the baseline scenario to identify pixels that changed from baseline. We only selected cells that transitioned to tidal habitats (unconsolidated shoreline, salt marsh, and transitional / brackish marsh) and not to open water or upland habitat. We combined the results from each of the geographies and projected to NAD83 Albers. The resultant migration space was then resampled to a 30-m grid and snapped to the NOAA 2010 C-CAP land cover grid (NOAA, 2017). The tidal complex grid and the migration space grid were combined to ensure that there were no overlapping pixels. While developed areas were not allowed to be future marsh in NOAA’s SLR Viewer marsh migration model, we still removed all roads and development, as represented in the original 30-m NOAA 2010 C-CAP land cover grid, from the migration space. We took this step as differences in spatial resolution between the underlying elevation and land cover datasets could occasionally result in small amounts of development in our resampled migration space. The remaining migration space was then spatially grouped into contiguous regions using an eight-neighbor rule that defined connected cells as those immediately to the right, left, above, or diagonal to each other. The region-grouped grid was converted to a polygon, and the SLR scenario represented by each migration space footprint was assigned to each polygon. Finally, the migration space scenario polygons that intersected any of the tidal complexes were selected. Because a single migration space polygon could be adjacent to and accessible to more than one tidal complex unit, each migration space polygon was linked to their respective tidal complex units with a unique ID by restructuring and aggregating the output from a one-to-many spatial join in ArcGIS. This linkage enabled the calculation of attributes for each tidal complex such as total migration space acreage, total number of migration space units, and the percent of the tidal complex perimeter that was immediately adjacent to migration space. Similar attributes were calculated for each migration space unit including total tidal complex acreage and number of tidal complex units.This dataset shows additional migration space units in the project area for the 6.5-foot sea level rise scenario. Additional migration space units are migration space units that did not spatially intersect current tidal marshes or were spatially disjunct from the migration space of current tidal marshes. Because additional migration space units were not directly associated with a tidal complex, these units were NOT used in the calculation of a tidal complex’s resilience score. The spatial separation could be due to roads, waterbodies, waterways, oil and gas fields, etc. Depending on local factors and context, the degree to which these features will prevent marshes from accessing the additional migration space areas in the future is unknown and likely varies by site.There were thousands of small and disconnected additional migration space areas, often individual pixels, typically found in urban settings, remote upstream riverine areas, or far from any migration space units or tidal marshes. We did not consider these isolated occurrences as additional migration space because they are unlikely to be important future marsh areas. We identified isolated migration space areas using the following approach. First, for unconfirmed additional migration space areas, an iterative analysis of the Euclidean distance from current tidal marshes and their migration space areas, including confirmed additional migration space, was performed. Next, pixels that did not meet the distance thresholds in the first step but were within 60 meters of a NHDPlus v2 (USEPA & USGS, 2012) streamline were retained as additional migration space. Any remaining pixels less than or equal to two acres in size were then removed from the additional migration space. Finally, visual inspection was used to remove isolated migration space areas that were not identified through the previous steps. We assigned resilience scores to the additional migration space areas using several approaches. First, we spatially allocated resilience scores based on Euclidean distance from tidal marshes or migration space units. While this approach was a good starting point, there were migration space areas whose score assignments had to be done manually or by taking the highest of two equidistant nearby scores. The manual assignment included straightforward cases, but often it was unclear how marshes might move into a migration space area (e.g., will marsh travel through waterways to nearby migration space areas; will marsh use all migration space areas along a waterway or waterbody or only on the same side as the current marsh?). For sites with unclear relationships to current marshes and their migration space, the highest resilience score in the general geographic area of the additional migration space was assigned. Consequently, please interpret the scores of the additional migration space with caution and use local expertise and knowledge as you see fit. REFERENCESChaffee, C, Coastal policy analyst for the R.I. Coastal Resources Management Council. personal communication. April 4, 2017.Donnelly, J.P, & Bertness, M.D. 2001. Rapid shoreward encroachment of salt marsh cordgrass in response to accelerated sea-level rise. PNAS 98(25) www.pnas.org/cgi/doi/10.1073/pnas.251209298Herold, N. 2018. NOAA Sea Level Rise (SLR) Viewer marsh migration data (10-m), with no accretion rate, for all SLR scenarios from 0.5-ft. to 10.0-ft. for VA, NC, SC, GA, and FL. Personal communication Jan. 24, 2018. Lerner, J.A., Curson, D.R., Whitbeck, M., & Meyers, E.J., Blackwater 2100: A strategy for salt marsh persistence in an era of climate change. 2013. The Conservation Fund (Arlington, VA) and Audubon MD-DC (Baltimore, MD).Lucey, K. NH Coastal Program. Personal Communication. April 4, 2017.Maine Natural Areas Program. 2016. Coastal Resiliency Datasets, Schlawin, J and Puryear, K., project leads. http://www.maine.gov/dacf/mnap/assistance/coastal_resiliency.htmlMarcy, D., Herold, N., Waters, K., Brooks, W., Hadley, B., Pendleton, M., Schmid, K., Sutherland, M., Dragonov, K., McCombs, J., Ryan, S. 2011. New Mapping Tool and Techniques For Visualizing Sea Level Rise And Coastal Flooding Impacts. National Oceanic and Atmospheric Administration (NOAA) Coastal Services Center. Originally published in the Proceedings of the 2011 Solutions to Coastal Disasters Conference, American Society of Civil Engineers