Patrick L. Barnard Li H. Erikson Kees Nederhoff Kai A. Parker Jennifer A. Thomas Amy C. Foxgrover Andrea C. O’Neill Norberto C. Nadal-Caraballo Chris Massey Madison C. Yawn Anita C. Engelstad 20241122 geoTIFF data release DOI:10.5066/P9W91314 Pacific Coastal and Marine Science Center, Santa Cruz, CA U.S. Geological Survey https://doi.org/10.5066/P9W91314 Patrick L. Barnard Kevin Befus Jeffrey J. Danielson Anita C. Engelstad Li H. Erikson Amy C. Foxgrover Matthew W. Hardy Daniel J. Hoover Tim Leijnse Patrick W. Limber Chris Massey Robert McCall Norberto C. Nadal-Caraballo Kees Nederhoff Leonard Ohenhen Andrea C. O’Neill Kai A. Parker Manoocher Shirzaei Xin Su Jennifer A. Thomas Maarten van Ormondt Sean F. Vitousek Kilian D. Vos Madison C. Yawn 2023 data release DOI:10.5066/P9W91314 Pacific Coastal and Marine Science Center, Santa Cruz, CA U.S. Geological Survey https://doi.org/10.5066/P9W91314 Projected water elevations from compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are shown for North Carolina and South Carolina. As described by Nederhoff and others (2024), projections were made using a system of numerical models driven by output from Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) and a tropical cyclone database from U.S. Army Corps of Engineers. The resulting data are elevations of projected flood hazards along the North Carolina and South Carolina coast due to sea level rise and plausible future storm conditions that consider the changing climate, hurricanes, and natural variability. The resulting data products include water elevations that are consistent with coastal flood projections, also available in this dataset (Barnard, and others, 2023). In addition to sea-level rise, flood simulations run by these numerical models included dynamic contributions from tide, storm surge, wind, waves, river discharge, precipitation, and seasonal sea-level fluctuations. Outputs include impacts from combinations of SLR scenarios (0, 0.25, 0.5, 1.0, 1.5, 2.0, and 3.0 m), storm conditions including 1-year, 20-year, and 100-year return interval storms, and a background condition (no storm - astronomic tide and average atmospheric conditions). These data are intended for policy makers, resource managers, science researchers, students, and the general public. These projections for future sea-level rise scenarios provide emergency responders and coastal planners with critical hazards information that can be used as a screening tool to increase public safety, mitigate physical damages, and more effectively manage and allocate resources within complex coastal settings. These data can be used with geographic information systems or other software to identify and assess possible areas of vulnerability. These data are not intended to be used for navigation. Work was funded by the Additional Supplemental Appropriations for Disaster Relief Act of 2019 (H.R. 2157) for North Carolina and South Carolina. This work is part of ongoing modeling efforts for the United States. For more information on coastal storm modeling, see https://www.usgs.gov/centers/pcmsc/science/coastal-storm-modeling-system-cosmos. Although this Federal Geographic Data Committee-compliant metadata file is intended to document the data set in nonproprietary form, as well as in Esri format, this metadata file may include some Esri-specific terminology. 2024 publication year Complete None planned -81.41555 -75.44948 36.55215 32.03543 USGS Metadata Identifier USGS:0c7f4296-83ba-44fa-a364-c63cebe52924 Data Categories for Marine Planning Physical Habitats and Geomorphology Global Change Master Directory (GCMD) Hazards Planning Ocean Waves Ocean Winds Beaches Erosion Sea Level Rise Storm Surge Extreme Weather Floods Water Elevation USGS Thesaurus Climate Change Storms Wind Floods Sea-level Change mathematical modeling effects of climate change earth sciences ISO 19115 Topic Category Oceans ClimatologyMeteorologyAtmosphere Marine Realms Information Bank (MRIB) keywords sea level change waves floods coastal erosion None U.S. Geological Survey USGS Coastal and Marine Hazards and Resources Program CMHRP Pacific Coastal and Marine Science Center PCMSC Geographic Names Information System (GNIS) State of North Carolina State of South Carolina No access constraints USGS-authored or produced data and information are in the public domain from the U.S. Government and are freely redistributable with proper metadata and source attribution. Please recognize and acknowledge the U.S. Geological Survey as the originator of the dataset and in products derived from these data. U.S. Geological Survey, Pacific Coastal and Marine Science Center PCMSC Science Data Coordinator mailing and physical

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Santa Cruz CA 95060 831-427-4747 pcmsc_data@usgs.gov Projections_WaterElevation_NC_SC.png Map showing area of modelled projections of water elevation for North and South Carolina. PNG This data release was funded by the Additional Supplemental Appropriations for Disaster Relief Act of 2019 (H.R. 2157) for North Carolina and South Carolina. The authors would like to acknowledge the following important contributions: Liv Herdman for help with understanding and accessing the National Water Model (NWM) data; Richard Signell and Daniel Nowacki for crucial python code and troubleshooting help in downloading National Water Model data hosted on Amazon Web Services (AWS); Fernando Salas for sharing route link files for NWM that were crucial in establishing watershed information; Brian Cosgrove and Anthony Guerriero for connecting the authors to Fernando Salas; and Malcolm Roberts for help navigating the CMIP6 tropical cyclone tracking products, providing additional information and access to them, and helpful discussions on research. Additionally, authors would like to extend special thanks to USGS colleagues for a detailed review of the projections: Amy Farris, Rachel Henderson, Kathy Weber, Justin Birchler, Alex Seymour, Sharifa Karwandyar, Matt Hardy, and Josh Pardun. The datasets were created in a Windows 11 Operating system, using Matlab v2020, ArcGIS 10.8.1 and 10.8.8, and python 3.7. Results were output and saved as vector shapefiles. K. Nederhoff T. Leijnse K.A. Parker J.A. Thomas A.C. O'Neill M. van Ormondt R. McCall L.H. Erikson P.L. Barnard A.C. Foxgrover W. Klessens N.C. Nadal-Caraballo C. Massey 2024 Nederhoff, K., Leijnse, T., Parker, K.A., Thomas, J.A., O'Neill, A.C., van Ormondt, M., McCall, R., Erikson, L.H., Barnard, P.L., Foxgrover, A.C., Klessens W., Nadal-Caraballo, N.C., and Massey, C., 2024, Tropical cyclones or extratropical storms: what drives the compound flood hazard, impact and risk for the United States Southeast Atlantic coast?: Natural Hazards, https://doi.org/10.1007/s11069-024-06552-x. https://doi.org/10.1007/s11069-024-06552-x N.C. Nadal-Caraballo M.O. Campbell V.M. Gonzalez M.J. Torres J.A. Melby A.A. Taflanidis 2020 Nadal-Caraballo, N. C., Campbell, M. O., Gonzalez, V. M., Torres, M. J., Melby, J. A., and Taflanidis, A. A., 2020, Coastal Hazards System: A Probabilistic Coastal Hazard Analysis Framework, Journal of Coastal Research, 95, 1211, https://doi.org/10.2112/SI95-235.1 https://doi.org/10.2112/SI95-235.1 R.J. Haarsma M.J. Roberts P.L. Vidale C.A. Senior A. Bellucci Q. Bao P. Chang S. Corti N.S. Fučkar V. Guemas J. von Hardenberg W. Hazeleger C. Kodama T. Koenigk L. R. Leung J. Lu J.J. Luo J. Mao M.S. Mizielinski R. Mizuta P. Nobre M. Satoh E. Scoccimarro T. Semmler J. Small J.S. von Storch 2016 Haarsma, R.J., Roberts, M.J., Vidale, P.L., Senior, C.A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N.S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J. J., Mao, J., Mizielinski, M.S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.S., 2016, High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geoscientific Model Development, 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016. https://doi.org/10.5194/gmd-9-4185-2016 Natural Resources Conservation Service 1985 Natural Resources Conservation Service, 1985, Hydrology, in, Natural Resources Conservation Service, 1985, National Engineering Handbook: U.S. Dept. of Agriculture, Soil Conservation Service https://www.nrcs.usda.gov/wps/portal/nrcs/detailfull/national/water/manage/hydrology/?cid=stelprdb1043063 Patrick L. Barnard Li H. Erikson Kees Nederhoff Kai A. Parker Jennifer A. Thomas Amy C. Foxgrover Andrea C. O’Neill Norberto C. Nadal-Caraballo Chris Massey Madison C. Yawn Anita C. Engelstad 2023 Barnard, P. L., Erikson, L. H., Nederhoff, K., Parker, K. A., Thomas, J. A., Foxgrover, A. C., O’Neill, A. C., Nadal-Caraballo, N. C., Massey, C., Yawn, M., and Engelstad, A. C., 2023, Projections of coastal flood hazards and flood potential for North Carolina and South Carolina, in Barnard, P.L., Befus, K., Nadal-Caraballo, N.C., Danielson, J., Engelstad, A., Erikson, L.H., Foxgrover, A.C., Hardy, M., Hoover, D., Leijnse, T., Massey, C., McCall, R., Nederhoff, K., Ohenhen, L., O'Neill, A.C., Parker, K., Shirzaei, M., Su, X., Thompson, J., van Ormondt, M., Vitousek, S., Vos, K., and Yawn, M.C., 2023, Future coastal hazards along North Carolina and South Carolina coasts: U.S. Geological Survey data release, https://doi.org/10.5066/P9W91314 https://doi.org/10.5066/P9W91314 T. Leijnse M. van Ormondt K. Nederhoff A. van Dongeren 2021 Leijnse, T., van Ormondt, M., Nederhoff, K., and van Dongeren, A., 2021, Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes: Coastal Engineering, v. 163, https://doi.org/10.1016/j.coastaleng.2020.103796 https://doi.org/10.1016/j.coastaleng.2020.103796 Attribute values are model-derived water elevations due to plausible sea-level rise and future storm conditions and therefore cannot be validated against observations. The projections were generated using the latest downscaled climate projections from the Coupled Model Intercomparison Project (CMIP6). Data have undergone quality checks and meet standards. Dataset is considered complete for the information presented. Data are concurrent with topobathymetric DEM locations. Model-derived data are accurate within published uncertainty bounds (see flood potential in the Projections of coastal flood hazards and flood potential for North Carolina and South Carolina dataset, also available in this data release), indicative of total uncertainty from elevation data sources, model processes and contributing data, and vertical land motion. This value is spatially variable and dependent on scenario. See Process Steps for details on total contributions to uncertainty. Malcolm Roberts 2019 netCDF files online Earth System Grid Federation http://doi.org/10.22033/ESGF/CMIP6.5982 online database 2019 publication date HadGEM3-GC31-HH Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model. Malcolm Roberts 2019 netCDF files online Earth System Grid Federation http://doi.org/10.22033/ESGF/CMIP6.5984 online database 2019 publication date HadGEM3-GC31-HM Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model. Malcolm Roberts 2017 netCDF files online Earth System Grid Federation http://doi.org/10.22033/ESGF/CMIP6.6024 online database 2017 publication date HadGEM3-GC31-HM-SST Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model. EC-Earth Consortium 2019 netCDF files online Earth System Grid Federation http://doi.org/10.22033/ESGF/CMIP6.4912 online database 2019 publication date EC-Earth3P-HR Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model. Aurore Voldoire 2019 netCDF files online Earth System Grid Federation http://doi.org/10.22033/ESGF/CMIP6.4225 online database 2019 publication date CNRM-CM6-1-HR Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model. Huan Guo Jasmin G. John Chris Blanton Colleen McHugh Serguei Nikonov Aparna Radhakrishnan Kristopher Rand Niki T. Zadeh V. Balaji Jeff Durachta Christopher Dupuis Raymond Menzel Thomas Robinson Seth Underwood Hans Vahlenkamp Krista A. Dunne Paul P.G. Gauthier Paul Ginoux Stephen M. Griffies Robert Hallberg Matthew Harrison William Hurlin Pu Lin Sergey Malyshev Vaishali Naik Fabien Paulot David J. Paynter Jeffrey Ploshay Daniel M. Schwarzkopf Charles J. Seman Andrew Shao Levi Silvers Bruce Wyman Xiaoqin Yan Yujin Zeng Alistair Adcroft John P. Dunne Isaac M. Held John P. Krasting Larry W. Horowitz Chris Milly Elena Shevliakova Michael Winton Ming Zhao Rong Zhang 2018 netCDF files online Earth System Grid Federation http://doi.org/10.22033/ESGF/CMIP6.9268 online database 2018 publication date GFDL-CMC4C192 Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model. Enrico Scoccimarro Alessio Bellucci Daniele Peano 2017 netCDF files online Earth System Grid Federation https://doi.org/10.22033/ESGF/CMIP6.1367 online database 2017 publication date 1 CMCC-CM2-VHR4 Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model. Sanne Muis Maialen I. Apecechea José A. Álvarez Martin Verlaan Kun Yan Job Dullaart Jeroen Aerts Trang Duong Rosh Ranasinghe Dewi le Bars Rein Haarsma Malcolm Roberts 2021 netCDF files online Copernicus Climate Change Service (C3S) Climate Data Store (CDS) https://cds-dev.copernicus-climate.eu/cdsapp#!/dataset/sis-water-level-change-cmip6-indicators?tab=overview online database 2021 publication date GTSM obtained nearshore water levels for SFINCS input National Oceanic and Atmospheric Administration (NOAA) 2021 csv online NOAA https://tidesandcurrents.noaa.gov/stations.html?type=Historic+Water+Levels online database 20210101 date data were accessed historical NOAA water levels model testing D. J. Tyler W.M. Cushing Jeff J. Danielson S. Poppenga S. Beverly R. Shogib 2022 raster online U.S. Geological Survey https://doi.org/10.5066/P9MPA8K0 digital dataset 1851 2020 ground condition DEM1 digital elevation data used for model input NOAA Office for Coastal Management 2016 raster online NOAA https://chs.coast.noaa.gov/htdata/raster2/elevation/Chesapeake_Coned_update_DEM_2016_8656/ digital dataset 1859 2015 ground condition DEM2 digital elevation data used for model input Soil Survey Staff, Natural Resources Conservation Service 2022 NetCDF online United States Department of Agriculture https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/geo/?cid=nrcs142p2_053629 online database 2022 publication date NRCS soil infiltration rates for precipitation U.S. Geological Survey 20210604 geoTIFF online Multi-Resolution Land Characteristics Consortium https://www.mrlc.gov/data/nlcd-2016-land-cover-conus online database 2021 publication date 6 NLCD 2016 land cover Li Erikson Liv Herdman Chris Flanary Anita Engelstad Prasad Pusuluri Patrick Barnard Curt Storlazzi Mike Beck Borja Reguero 2022 NetCDF online U.S. Geological Survey https://doi.org/10.5066/P9KR0RFM online database 2022 publication date WW3 projected wave data Kai A. Parker Li Erikson Jennifer A. Thomas Kees Nederhoff Tim Leijnse 2023 csv files online United States Geological Survey https://doi.org/10.5066/P9W91314 online database 2023 publication date waveSetup_hindc provided wave setup for the hindcast period Kai A. Parker Li Erikson Jennifer A. Thomas Kees Nederhoff Tim Leijnse 2023 csv files online United States Geological Survey https://doi.org/10.5066/P9W91314 online database 2023 publication date waveSetup_proj provided wave setup for the projection period Kai A. Parker Li Erikson Jennifer A. Thomas Kees Nederhoff Tim Leijnse 2023 csv files online United States Geological Survey https://doi.org/10.5066/P9W91314 online database 2023 publication date waterLevel_hindc provided water levels, tides, and non-tidal residuals for the hindcast period Kai A. Parker Li Erikson Jennifer A. Thomas Kees Nederhoff Tim Leijnse 2023 csv files online United States Geological Survey https://doi.org/10.5066/P9W91314 online database 2023 publication date waterLevel_proj provided water levels, tides, and non-tidal residuals for the projection period Y. Xia M. Mitchell J. Ek B. Sheffield E. Cosgrove L. Wood C. Luo H. Alonge J. Wei B. Meng D. Livneh V. Lettenmaier Q. Koren K. Mo Duan Y. Fan D. Mocko 2009 GRIB files online Goddard Earth Sciences Data and Information Services Center (GES DISC) https://10.5067/6J5LHHOHZHN4 online database 2009 publication date NLDAS historic precipitation used to compare to NWM streamflow Yan Y. Liu David R. Maidment David G. Tarboton Xing Zheng Ahmet Yildirim, Nazmus S. Sazib Shaowen Wang 2016 shapefiles online University of Texas https://web.corral.tacc.utexas.edu/nfiedata/ online database 20201007 time when data were accessed NFIE shapefiles providing stream reach ID locations National Oceanic and Atmospheric Administration (NOAA) 2020 zarr online aws https://registry.opendata.aws/nwm-archive online database 20201231 date data were accessed NWM used to establish projected river/fluvial discharge Manoocher Shirzaei Leonard Ohenhen Matthew W. Hardy 2023 csv files online United States Geological Survey https://doi.org/10.5066/P9W91314 online database 2023 publication date VLM provided vertical land motion for uncertainty calculations All processes and methods are outlined in Nederhoff and others (2024); please refer to that for more information beyond the summary in this document. To generate time-series of forcings for coastal flooding models in order to map future coastal flooding hazards along the south Atlantic United States coast due to sea level rise and plausible future storm conditions that consider the changing climate, hurricanes, and natural variability, we gathered available atmospheric forcing data (specifically precipitation, sea-level pressure, and near-surface wind for this study) from CMIP6 Global Climate Models (GCM). At the time of this study, only products for Representative Concentration Pathway 8.5 for the projected time-period 2020-2050 were available and used. Output was gathered for specific High-Resolution Model Intercomparison Project (HighResMIP) experiments: HadGEM3-GC31, EC-Earth3P-HR, CNRM-CM6-1-HR, GFDL-CMC4C192 and CMCC-CM2-VHR4 HadGEM3-GC31-HH, HadGEM3-GC31-HM, HadGEM3-GC31-HM-SST, EC-Earth3P-HR, CNRM-CM6-1-HR, GFDL-CMC4C192 and CMCC-CM2-VHR4 20200501 We analyzed multi-model trends in future (2020-2050) tropical cyclone climatology depicted in GCMs throughout the study area (Nederhoff and others, 2024). This included detailed comparisons to historical runs in probability functions of tropical cyclone sea-level pressure, propagation speed and maximum wind speed throughout the study area, to highlight future changes in tropical cyclone characteristics by geographical position. HadGEM3-GC31-HH, HadGEM3-GC31-HM, HadGEM3-GC31-HM-SST, EC-Earth3P-HR, CNRM-CM6-1-HR, GFDL-CMC4C192 and CMCC-CM2-VHR4 20201215 We obtained Global Surge and Tide Model (GTSM) output (run for all aforementioned CMIP6 experiments’ sea-level pressure and wind) for nearshore water levels for projected period 2016-2050, and historical period (1976-2015). As described in Nederhoff and others (2024), we conducted initial comparisons of datasets and analysis of extreme water level changes, before preparing data for use in following process steps. GTSM 20201215 As described by Nederhoff and others (2024), we tested the Super-Fast Inundation of CoastS model (SFINCS; Leijnse and others, 2021) resolutions and computational efficiency and determined that running the SFINCS at 200-m spatial resolution, with sub-gridding, was optimum for this study, providing balance between fast simulations and accuracy of coastal water levels (tested for Hurricane Florence,14 September 2018, with historical NOAA water levels). The study area was covered by three rectilinear SFINCS domains, aligned shore-normal for each respective area, with the offshore boundary as the nearshore GTSM output locations. Model boundaries extend outside the study area to encompass and include necessary hydrodynamics. Elevation for the SFINCS domains was extracted from the corresponding DEMs in the region and resampled from 1-meter resolution to the SFINCS model's computational grid. SFINCS simulations were run with soil infiltration rates derived using the Curve Number Method (U.S. Dept. of Agriculture, Soil Conservation Service, 1985) to capture absorption/run-off of precipitation in the model. Curve Numbers were derived using the National Land Classification Dataset (NLCD 2016) and the Digital General Soil Map of the United States (NRCS). historical NOAA water levels, DEM1, DEM2, NLCD 2016, NRCS 20210115 We conducted initial comparisons of WW3 data for projections (run with wind conditions for all aforementioned CMIP6 experiments) at the 15-20 m isobath and analysis of extreme nearshore wave changes, before preparing data for use in following process steps. WW3 20210228 Hindcasted water levels were compared to NOAA tide station observations and were used to guide any necessary bias corrections (see the Nearshore water level, tide and non-tidal residual hindcasts (1979-2016) for North Carolina and South Carolina coasts dataset, also available in this data release). Bias corrections were applied to the projected water levels. See Nederhoff and others (2024) for more details. waterLevel_hindc, waterLevel_proj 20210301 In collaboration with U.S. Army Corps of Engineers (USACE), we used a synthetic database available from Nadal-Caraballo and others (2020) of approximately 1,200 tropical cyclone events to establish a baseline of boundary conditions for tropical storms. As described in Nederhoff and others (2024), changes in tropical storm parameters, computed from the previous tropical cyclone analysis comparing GCM data for historical to future periods, were used to shift the hazard curves to represent future cyclone conditions and changes in frequency of occurrence and magnitude. 20210531 We derived future time-series data of river/fluvial discharge through the study area for 48 rivers, using the relationship between historical NLDAS precipitation and NWM reanalysis data and applying it to future GCM precipitation output (Nederhoff and others, 2024). The upstream watershed of each of the 48 rivers was identified from the network of river reach IDs used by the NWM (NFIE). Historical precipitation (1993-2018) over each individual watershed was used for each respective river. Future discharge was then estimated by applying future GCM precipitation data (2020-2050) over watersheds and using the established relationships between historical precipitation/pluvial rates and discharge. When no precipitation was projected in data, baseline river discharge rates (from NWM historical periods) were used. An additional river time series consisted solely of its historical baseline discharge, due to its watershed being too small for this process. NLDAS, NWM, NFIE 20211101 Using the GTSM output and computed wave setup, we identified extreme water levels along the open coast and associated fluvial inputs and precipitation for extreme coastal water elevation events. As described by Nederhoff and others (2024), the largest coastal storm events (from GTSM storm tide and wave setup) of each GCM were identified, equivalent to an average of the largest 5 storms per year. The overland flow model (SFINCS) was run for all anomalously high-water level events (top 150 from each contributing GCM, plus all tropical cyclone events from USACE) with each event’s commensurate GTSM coastal water levels, wave setup, SLR, point-source river discharge (at each river), and precipitation data fields included as forcing for the simulation. GTSM, waterLevel_proj, waterLevel_hindc, waveSetup_hindc, waveSetup_proj 20210615 Detailed quality control was conducted for test outputs from the model system. After identifying initial sources of error, all simulations were rerun. 20211101 Return period (RP) statistics (1/20/100-year storm, or no storm/daily average conditions) were calculated per grid cell for each SLR scenario to yield a composited raster of water levels for each SLR and storm combination (Nederhoff and others, 2024). With each composited raster, by RP and SLR, a depth threshold of 5 cm (at native 200-m scale of SFINCS computational grid) was used to preserve legitimate flood projections in high-relief areas. Raster outputs were run through an iterative function (in Matlab) to identify cells connected to coastally driven flooding (such as, physically connected to contiguous coastal flood surface and ocean). For cells not connected to coastal flooding, output was labeled "ponding", to signify vulnerability to flood hazards driven by river discharge or precipitation. Water levels/elevations in each cell were then depth-differenced to underlying DEM data (sub-sampled to horizontal resolution of 10 m) to resolve fine-scale features in coastal flood hazards and ponding areas. Water elevations were only output for areas identified as coastal flooding (see the flood hazard layers contained in Projections of coastal flood hazards and flood potential for North Carolina and South Carolina, also available in this data release), as that was the focus of the study. Uncertainty was calculated as a sum of contributions, including DEM uncertainty (35 cm), projected vertical land motion (VLM) based on SLR (spatially variable per SLR scenario), and uncertainty with the model and model processes (spatially variable, derived from water level return-period curves at each grid point, dependent on scenario). This total uncertainty is applied to the final water elevation and extrapolated outward to depict the maximum and minimum potential flood area considering total uncertainty (labeled as ‘flood potential’). Water elevations are accurate within these bounds. VLM, DEM1, DEM2 20220115 Data from all domains were merged to make geoTIFFs of the originating rasters. The geoTIFFs were exported from ArcMap for all combinations of seven SLRs (0, 0.25, 0.5, 1.0, 1.5, 2.0 and 3.0 m), 3 storms (1-year, 20-year, and 100-year return period coastal events), and the non-storm condition for a total of 28 scenarios. Final geoTIFFs (at 10 m horizontal resolution) were separated by state (Projections_WaterElevation_*STATE*.zip) for file-size considerations. Data are further organized by storm scenario (’RP’) and SLR amount. 20220330 Raster Pixel Universal Transverse Mercator 17 0.9996 -81.00000 0.00000 500000.0 0.00 row and column 10 10 Meters GCS WGS 1984 Geodetic Reference System 80 6378137.00 298.257223563 North American Vertical Datum of 1988 0.01 meters Implicit coordinate water elevation projections [Projections_WaterElevation_*STATE*.zip] geoTIFF files contain projections of flood-hazard water elevations. Producer defined water_elev water elevation (referenced to NAVD88) associated with corresponding flood extent of sea-level rise (SLR) and return period (RP) indicated model-derived -0.13 30.58 meters 0.01 The data contain water elevations (elevations of water surface associated with coincident flood hazards). Return periods cover average conditions (RP000), once-a-year storms (RP001), every 20 (RP20) and every 100 years (RP100) storms. File names reflect the geographic area of the projection (state), the attribute (water_elevation), the sea-level rise (SLR) scenario, and the return period (RP) of storm conditions. SLR scenarios are listed in centimeters and range from no SLR (SLR000) to a SLR of 300 cm (SLR300). For example, NC_water_elev_SLR200_RP100 contains the water elevation for a sea level rise of 200 cm (2 m) during a hundred-year storm in North Carolina. Data are spatially consistent for coastal flood hazards of the same scenario (see the flood hazard layers contained in Projections of coastal flood hazards and flood potential for North Carolina and South Carolina, also available in this data release). none U.S. Geological Survey - CMGDS mailing and physical

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Santa Cruz CA 95060 831-427-4747 pcmsc_data@usgs.gov These data are available as zip files with a filename of [Projections_WaterElevation_*STATE*.zip], where *STATE* can be either North Carolina (NC) or South Carolina (SC). Unless otherwise stated, all data, metadata and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. GeoTIFF Zip file contains the geoTIFF files for North Carolina WinZip 2185 https://doi.org/10.5066/P9W91314 Data can be downloaded using the Network_Resource_Name link then scrolling down to the Simulation Data section. GeoTIFF Zip file contains the geoTIFF files for South Carolina WinZip 1007 https://doi.org/10.5066/P9W91314 Data can be downloaded using the Network_Resource_Name link then scrolling down to the Simulation Data section. None 20241122 U.S. Geological Survey, Pacific Coastal and Marine Science Center PCMSC Science Data Coordinator mailing and physical

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Santa Cruz CA 95060 831-427-4747 pcmsc_data@usgs.gov Content Standard for Digital Geospatial Metadata FGDC-STD-001-1998