Projections of coastal flood hazards and flood potential for North Carolina and South Carolina

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What does this data set describe?

Title:
Projections of coastal flood hazards and flood potential for North Carolina and South Carolina
Abstract:
Projected impacts by compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are shown for North Carolina and South Carolina. Accompanying uncertainty for each SLR and storm scenario, indicating total uncertainty from model processes and contributing datasets, are illustrated in maximum and minimum flood potential. 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 US Army Corp of Engineers. The resulting data products include detailed flood-hazard maps 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. 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).
Supplemental_Information:
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.
  1. How might this data set be cited?
    Barnard, Patrick L., Erikson, Li H., Nederhoff, Kees, Parker, Kai A., Thomas, Jennifer A., Foxgrover, Amy C., O’Neill, Andrea C., Nadal-Caraballo, Norberto C., Massey, Chris, Yawn, Madison C., and Engelstad, Anita C., 20230128, Projections of coastal flood hazards and flood potential for North Carolina and South Carolina: data release DOI:10.5066/P9W91314, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

    Online Links:

    This is part of the following larger work.

    Barnard, Patrick L., Befus, Kevin, Danielson, Jeffrey J., Engelstad, Anita C., Erikson, Li H., Foxgrover, Amy C., Hardy, Matthew W., Hoover, Daniel J., Leijnse, Tim, Limber, Patrick W., Massey, Chris, McCall, Robert, Nadal-Caraballo, Norberto C., Nederhoff, Kees, Ohenhen, Leonard, O’Neill, Andrea C., Parker, Kai A., Shirzaei, Manoocher, Su, Xin, Thomas, Jennifer A., Ormondt, Maarten van, Vitousek, Sean F., Vos, Kilian D., and Yawn, Madison C., 2023, Future coastal hazards along the U.S. North and South Carolina coasts: data release DOI:10.5066/P9W91314, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -81.41555
    East_Bounding_Coordinate: -75.44948
    North_Bounding_Coordinate: 36.55215
    South_Bounding_Coordinate: 32.03543
  3. What does it look like?
    Projections_FloodHazard_NC_SC.png (PNG)
    Map showing area of modelled projections of flood hazards and flood potential for North and South Carolina.
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2023
    Currentness_Reference:
    publication year
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: vector digital data
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Vector data set.
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      Universal_Transverse_Mercator:
      UTM_Zone_Number: 17
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -81.00000
      Latitude_of_Projection_Origin: 0.00000
      False_Easting: 500000.0
      False_Northing: 0.00
      Planar coordinates are encoded using row and column
      Abscissae (x-coordinates) are specified to the nearest 10
      Ordinates (y-coordinates) are specified to the nearest 10
      Planar coordinates are specified in Meters
      The horizontal datum used is GCS WGS 1984.
      The ellipsoid used is Geodetic Reference System 80.
      The semi-major axis of the ellipsoid used is 6378137.00.
      The flattening of the ellipsoid used is 1/298.257223563.
      Vertical_Coordinate_System_Definition:
      Depth_System_Definition:
      Depth_Datum_Name: North American Vertical Datum of 1988
      Depth_Resolution: 0.01
      Depth_Distance_Units: meters
      Depth_Encoding_Method: Implicit coordinate
  7. How does the data set describe geographic features?
    Entity_and_Attribute_Overview:
    Zip files contained in this part of the data release include projected flood hazards [Projections_FloodHazards_*STATE*.zip] and boundaries of study area [FloodHaz_ModelOutput_Boundary_NC.zip and FloodHaz_ModelOutput_Boundary_SC.zip] shapefiles containing single-polygon projected extents of flood hazard, ponding, maximum flooding potential, minimum flooding potential, and boundaries of North and South Carolina study area The data contain projections of coastally driven flood extent (‘flood_hazards’), areas vulnerable to flooding but not hydrologically connected to coastal flooding (‘ponding’), and the minimum and maximum potential flood extent given total uncertainty (‘min_potential’ and ‘max_potential’). Shapefiles represent the given hazard associated with the sea-level rise and storm condition indicated. Storm condition return periods cover background conditions (RP000), once-a-year on average storms (RP001), every 20 years on average (RP20) and every 100 years on average (RP100) storms. Shapefile names reflect the area of the projection (state), the attribute of the shapefile, the sea level rise (SLR) scenario (in centimeters) and the return period (RP) of storm conditions. SLR scenarios range from no SLR (SLR000) to a SLR of 300 cm (SLR300). Files are grouped by state, containing all SLR, RP and output files. For example, Projections_FloodHazards_NC.zip, contains all output for North Carolina, within which NC_flood_hazard_SLR200_RP100 illustrates the flood extents for a sea level rise of 200 cm (2 m) during a 100-year storm in the state.
    Entity_and_Attribute_Detail_Citation: none

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • 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
  2. Who also contributed to the data set?
    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.
  3. To whom should users address questions about the data?
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Attn: PCMSC Science Data Coordinator
    2885 Mission Street
    Santa Cruz, CA

    831-427-4747 (voice)
    pcmsc_data@usgs.gov

Why was the data set created?

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.

How was the data set created?

  1. From what previous works were the data drawn?
    HadGEM3-GC31-HH (source 1 of 22)
    Roberts, Malcolm, 2019, MOHC HadGEM3-GC31-HH model output prepared for CMIP6 HighResMIP highres-future: Earth System Grid Federation, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model.
    HadGEM3-GC31-HM (source 2 of 22)
    Roberts, Malcolm, 2019, MOHC HadGEM3-GC31-HM model output prepared for CMIP6 HighResMIP highres-future: Earth System Grid Federation, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model.
    HadGEM3-GC31-HM-SST (source 3 of 22)
    Roberts, Malcolm, 2017, MOHC HadGEM3-GC31-HM-SST model output prepared for CMIP6 HighResMIP highresSST-present: Earth System Grid Federation, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    The SST variant of HadGEM, wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model.
    EC-Earth3P-HR (source 4 of 22)
    Consortium, EC-Earth, 2019, EC-Earth-Consortium EC-Earth3P-HR model output prepared for CMIP6 HighResMIP highres-future: Earth System Grid Federation, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model.
    CNRM-CM6-1-HR (source 5 of 22)
    Voldoire, Aurore, 2019, CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 ScenarioMIP ssp585: Earth System Grid Federation, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model.
    GFDL-CMC4C192 (source 6 of 22)
    Guo, Huan, John, Jasmin G., Blanton, Chris, McHugh, Colleen, Nikonov, Serguei, Radhakrishnan, Aparna, Rand, Kristopher, Zadeh, Niki T., Balaji, V., Durachta, Jeff, Dupuis, Christopher, Menzel, Raymond, Robinson, Thomas, Underwood, Seth, Vahlenkamp, Hans, Dunne, Krista A., Gauthier, Paul P.G., Ginoux, Paul, Griffies, Stephen M., Hallberg, Robert, Harrison, Matthew, Hurlin, William, Lin, Pu, Malyshev, Sergey, Naik, Vaishali, Paulot, Fabien, Paynter, David J., Ploshay, Jeffrey, Schwarzkopf, Daniel M., Seman, Charles J., Shao, Andrew, Silvers, Levi, Wyman, Bruce, Yan, Xiaoqin, Zeng, Yujin, Adcroft, Alistair, Dunne, John P., Held, Isaac M., Krasting, John P., Horowitz, Larry W., Milly, Chris, Shevliakova, Elena, Winton, Michael, Zhao, Ming, and Zhang, Rong, 2018, National Oceanic and Atmospheric Administration (NOAA) NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP ssp585: Earth System Grid Federation, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model.
    CMCC-CM2-VHR4 (source 7 of 22)
    Scoccimarro, Enrico, Bellucci, Alessio, and Peano, Daniele, 2017, CMCC CMCC-CM2-VHR4 model output prepared for CMIP6 HighResMIP: Earth System Grid Federation, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    Wind velocities, sea level pressure, and precipitation output were used as boundary conditions for the SFINCS model.
    GTSM (source 8 of 22)
    Muis, Sanne, Apecechea, Maialen I., Álvarez, José A., Verlaan, Martin, Yan, Kun, Dullaart, Job, Aerts, Jeroen, Duong, Trang, Ranasinghe, Rosh, Bars, Dewi le, Haarsma, Rein, and Roberts, Malcolm, 2021, Global water level change indicators from 1950 to 2050 derived from HighResMIP climate projections: Copernicus Climate Change Service (C3S) Climate Data Store (CDS), online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: obtained nearshore water levels for SFINCS input
    historical NOAA water levels (source 9 of 22)
    National Oceanic and Atmospheric Administration (NOAA), 2021, Historic Water Levels: NOAA, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: model testing
    DEM1 (source 10 of 22)
    Tyler, D. J., Cushing, W.M., Danielson, Jeff J., Poppenga, S., Beverly, S., and Shogib, R., 2022, Topobathymetric Model of the Coastal Carolinas, 1851 to 2020: U.S. Geological Survey, online.

    Online Links:

    Type_of_Source_Media: digital dataset
    Source_Contribution: digital elevation data used for model input
    DEM2 (source 11 of 22)
    NOAA Office for Coastal Management, 2016, 2016 USGS Coastal National Elevation Database (CoNED) Topobathymetric Model (1859-2015): Chesapeake Bay: NOAA, online.

    Online Links:

    Type_of_Source_Media: digital dataset
    Source_Contribution: digital elevation data used for model input
    NRCS (source 12 of 22)
    Soil Survey Staff, Natural Resources Conservation Service, 2022, Web Soil Survey, STATSGO2 Database: United States Department of Agriculture, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: soil infiltration rates for precipitation
    NLCD 2016 (source 13 of 22)
    U.S. Geological Survey, 20210604, National Land Cover Database (NLCD) 2016 Land Cover Conterminous United States: Multi-Resolution Land Characteristics Consortium, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: land cover
    WW3 (source 14 of 22)
    Erikson, Li, Herdman, Liv, Flanary, Chris, Engelstad, Anita, Pusuluri, Prasad, Barnard, Patrick, Storlazzi, Curt, Beck, Mike, and Reguero, Borja, 2022, Ocean wave time-series simulated with a global-scale numerical wave model under the influence of projected CMIP6 wind and sea ice fields: U.S. Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: projected wave data
    waveSetup_hindc (source 15 of 22)
    Parker, Kai A., Erikson, Li, Thomas, Jennifer A., Nederhoff, Kees, and Leijnse, Tim, 2023, Nearshore parametric wave setup hindcast data (1979-2019) for North Carolina and South Carolina coasts: United States Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: provided wave setup for the hindcast period
    waveSetup_proj (source 16 of 22)
    Parker, Kai A., Erikson, Li, Thomas, Jennifer A., Nederhoff, Kees, and Leijnse, Tim, 2023, Nearshore parametric wave setup future projections (2020-2050) for North Carolina and South Carolina coasts: United States Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: provided wave setup for the projection period
    waterLevel_hindc (source 17 of 22)
    Parker, Kai A., Erikson, Li, Thomas, Jennifer A., Nederhoff, Kees, and Leijnse, Tim, 2023, Nearshore water level, tide and non-tidal residual hindcasts (1979-2016) for North Carolina and South Carolina coasts: United States Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    provided water levels, tides, and non-tidal residuals for the hindcast period
    waterLevel_proj (source 18 of 22)
    Parker, Kai A., Erikson, Li, Thomas, Jennifer A., Nederhoff, Kees, and Leijnse, Tim, 2023, Nearshore water level, tide and non-tidal residual projections (2016-2050) for North Carolina and South Carolina coasts: United States Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    provided water levels, tides, and non-tidal residuals for the projection period
    NLDAS (source 19 of 22)
    Xia, Y., Mitchell, M., Ek, J., Sheffield, B., Cosgrove, E., Wood, L., Luo, C., Alonge, H., Wei, J., Meng, B., Livneh, D., Lettenmaier, V., Koren, Q., Duan, K. Mo, Fan, Y., and Mocko, D., 2009, North American Land Data Assimilation System (NLDAS) Primary Forcing Data L4 Hourly 0.125 x 0.125 degree V002: Goddard Earth Sciences Data and Information Services Center (GES DISC), online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: historic precipitation used to compare to NWM streamflow
    NFIE (source 20 of 22)
    Liu, Yan Y., Maidment, David R., Tarboton, David G., Zheng, Xing, Yildirim, Ahmet, Sazib, Nazmus S., and Wang, Shaowen, 2016, NFIE Continental Flood Inundation Mapping - Data Repository: University of Texas, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: shapefiles providing stream reach ID locations
    NWM (source 21 of 22)
    NOAA, 2020, The NOAA National Water Model Retrospective dataset, V.2.0: aws, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: used to establish projected river/fluvial discharge
    VLM (source 22 of 22)
    Shirzaei, Manoocher, Ohenhen, Leonard, and Hardy, Matthew W., 2023, Vertical land motion rates for the years 2007 to 2021 for North Carolina and South Carolina coasts: United States Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution: provided vertical land motion for uncertainty calculations
  2. How were the data generated, processed, and modified?
    Date: 01-May-2020 (process 1 of 15)
    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 Data sources used in this process:
    • HadGEM3-GC31-HH, HadGEM3-GC31-HM, HadGEM3-GC31-HM-SST, EC-Earth3P-HR, CNRM-CM6-1-HR, GFDL-CMC4C192 and CMCC-CM2-VHR4
    Date: 15-Dec-2020 (process 2 of 15)
    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 Data sources used in this process:
    • HadGEM3-GC31-HH, HadGEM3-GC31-HM, HadGEM3-GC31-HM-SST, EC-Earth3P-HR, CNRM-CM6-1-HR, GFDL-CMC4C192 and CMCC-CM2-VHR4
    Date: 15-Dec-2020 (process 3 of 15)
    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. Data sources used in this process:
    • GTSM
    Date: 15-Jan-2021 (process 4 of 15)
    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. Determined 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). Data sources used in this process:
    • NOAA water levels, DEM1, DEM2
    Date: 28-Feb-2021 (process 5 of 15)
    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. Data sources used in this process:
    • WW3
    Date: 01-Mar-2021 (process 6 of 15)
    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 projections (2016-2050) and hindcasts (1979-2016) for North Carolina and South Carolina datasets, also available in this data release). Bias corrections were applied to the projected water levels. See Nederhoff and others (2024) for more details. Data sources used in this process:
    • waterLevel_hindc, waterLevel_proj
    Date: 31-May-2021 (process 7 of 15)
    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.
    Date: 01-Nov-2021 (process 8 of 15)
    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. Data sources used in this process:
    • NLDAS, NWM, NFIE
    Date: 15-Jun-2021 (process 9 of 15)
    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 Data sources used in this process:
    • GTSM, waterLevel_proj, waterLevel_proj, waveSetup_hindc, waveSetup_proj
    Date: 01-Nov-2021 (process 10 of 15)
    Detailed quality control was conducted for test outputs from the model system. After identifying initial sources of error, all simulations were rerun.
    Date: 15-Jan-2022 (process 11 of 15)
    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, as well as return corresponding water depth information. Water depths were only calculated for areas identified as coastal flooding (not ponding), 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 depths are accurate within these bounds. Data sources used in this process:
    • VLM, DEM1, DEM2
    Date: 26-Jan-2022 (process 12 of 15)
    Data from all domains were merged to make geoTIFFs of the originating rasters for each data layer (coastal flood hazard, ponding, and maximum/minimum flood potential). The geoTIFFs were exported as shapefiles 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 shapefiles were separated by state (Projections_FloodHazards_*STATE*.zip) for file-size considerations. Shapefiles are further organized by storm scenario (’RP’), with flood hazards and ponding under one directory, and flood potential in another directory. Shapefiles depicting the study-area boundary are also included (FloodHazard_boundaries_NC_SC.zip).
    Date: 13-Mar-2023 (process 13 of 15)
    Metadata was modified to include doi# and full citation for Nederhoff and others (2024) Cross Reference. No data information was changed. The metadata available from a harvester may supersede metadata bundled within a download file. Users are advised to compare the metadata dates to determine which metadata file is most recent. Person who carried out this activity:
    Susan A Cochran
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    Geologist
    2885 Mission Street
    Santa Cruz, CA
    USA

    (831) 460-7545 (voice)
    scochran@usgs.gov
    Date: 16-May-2023 (process 14 of 15)
    Edits were made to correct spelling in authors names and to add final citation information to a Cross_Reference. No data were changed. The metadata available from a harvester may supersede metadata bundled within a download file. Users are advised to compare the metadata date of this file to any similar file to ensure they are using the most recent version. (scochran@usgs.gov)
    Date: 22-May-2024 (process 15 of 15)
    Metadata was modified to include updated doi# and full citation for Nederhoff and others (2024) Cross Reference. No data information was changed. The metadata available from a harvester may supersede metadata bundled within a download file. Users are advised to compare the metadata dates to determine which metadata file is most recent. (pcmsc_data@usgs.gov)
  3. What similar or related data should the user be aware of?
    Nederhoff, K., Leijnse, T., Parker, K.A., Thomas, J.A., O'Neill, A.C., Ormondt, M. van, 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?.

    Online Links:

    Other_Citation_Details:
    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.
    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.

    Online Links:

    Other_Citation_Details:
    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
    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., Hardenberg, J. von, 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 Storch, J.S. von, 2016, High resolution model intercomparison project (HighResMIP v1.0) for CMIP6.

    Online Links:

    Other_Citation_Details:
    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.
    Natural Resources Conservation Service, 1985, National Engineering Handbook.

    Online Links:

    Other_Citation_Details:
    Natural Resources Conservation Service, 1985, Hydrology, in, Natural Resources Conservation Service, 1985, National Engineering Handbook: U.S. Dept. of Agriculture, Soil Conservation Service.
    Leijnse, Tim, Ormondt, Maarten van, Nederhoff, Kees, and Dongeren, Ap van, 2021, Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes.

    Online Links:

    Other_Citation_Details:
    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

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    Attribute values are model-derived extents of flood projections, ponding areas, and maximum/minimum flood potential (flood uncertainty) 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).
  2. How accurate are the geographic locations?
    Data are concurrent with topobathymetric DEM locations.
  3. How accurate are the heights or depths?
    Model-derived data are accurate within the flood potential layers (uncertainty bounds), 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.
  4. Where are the gaps in the data? What is missing?
    Dataset is considered complete for the information presented (as described in the abstract).
  5. How consistent are the relationships among the observations, including topology?
    Data have undergone quality checks and meet standards.

How can someone get a copy of the data set?

Are there legal restrictions on access or use of the data?
Access_Constraints None
Use_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.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - CMGDS
    2885 Mission Street
    Santa Cruz, CA

    831-427-4747 (voice)
    pcmsc_data@usgs.gov
  2. What's the catalog number I need to order this data set? These data are available as zip files by state for which [FloodHazards_*STATE*.zip] is the filename, where *STATE* can be either North Carolina (NC) or South Carolina (SC).
  3. What legal disclaimers am I supposed to read?
    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, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  4. How can I download or order the data?
    • Availability in digital form:
      Data format: Zip file contains the flood hazard polygon shapefiles for North Carolina in format Shapefile (version ArcGIS 10.8.1) Esri polygon shapefile Size: 11000.00
      Network links: https://doi.org/10.5066/P9W91314
      Data format: Zip file contains the flood hazard polygon shapefiles for South Carolina in format Shapefile (version ArcGIS 10.8.1) Esri polygon shapefile Size: 594.8
      Network links: https://doi.org/10.5066/P9W91314
      Data format: Zip file contains the flood hazard study area boundary shapefile for NC in format Shapefile (version ArcGIS 10.8.1) Esri polygon shapefile Size: 0.2
      Network links: https://doi.org/10.5066/P9W91314
      Data format: Zip file contains the flood hazard study area boundary shapefile for SC in format Shapefile (version ArcGIS 10.8.1) Esri polygon shapefile Size: 0.1
      Network links: https://doi.org/10.5066/P9W91314
    • Cost to order the data: None

Who wrote the metadata?

Dates:
Last modified: 23-May-2024
Metadata author:
U.S. Geological Survey, Pacific Coastal and Marine Science Center
Attn: PCMSC Science Data Coordinator
2885 Mission Street
Santa Cruz, CA

831-427-4747 (voice)
pcmsc_data@usgs.gov
Metadata standard:
Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)
This page is <https://cmgds.marine.usgs.gov/catalog/pcmsc/DataReleases/CMGDS_DR_tool/DR_P9W91314/Projections_FloodHazards_metadata.faq.html>
Generated by mp version 2.9.51 on Fri May 24 14:13:04 2024