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
20230315
Projections of coastal flood depths for the U.S. Atlantic coast
geoTIFF
data release
DOI:10.5066/P9BQQTCI
Pacific Coastal and Marine Science Center, Santa Cruz, CA
U.S. Geological Survey
https://doi.org/10.5066/P9BQQTCI
Patrick L. Barnard
Kevin Befus
Jeffrey J. Danielson
Anita C. Engelstad
Li H. Erikson
Amy C. Foxgrover
Maya K. Hayden
Daniel J. Hoover
Tim Leijnse
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
Madison C. Yawn
2023
Future coastal hazards along the U.S. Atlantic coast
data release
DOI:10.5066/P9BQQTCI
Pacific Coastal and Marine Science Center, Santa Cruz, CA
U.S. Geological Survey
https://doi.org/10.5066/P9BQQTCI
Projected depths from compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are shown for the U.S. Atlantic coast for three states (Florida, Georgia, and Virginia). 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 Corp of Engineers. The resulting data are depths of projected flood hazards along the U.S. Atlantic 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 flood depths that are consistent with coastal flood projections, also available in this dataset (Barnard, and others, 2023); see Nederhoff and others (2023) for a full explanation of data and methods. 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). Similar projections for North Carolina and South Carolina are available from Barnard and others, 2023, at https://doi.org/10.5066/P9W91314
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.
This data release was funded by the USGS Coastal and Marine Hazards and Resources Program. 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.
2023
publication year
None planned
-82.11192
-75.32334
37.45109
26.06347
USGS Metadata Identifier
USGS:62cc0bce-ead2-4e40-96ce-4d6e1caf99df
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 Depth
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 Florida
State of Georgia
State of Virginia
None
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
2885 Mission Street
Santa Cruz
CA
95060
831-427-4747
pcmsc_data@usgs.gov
Projections_WaterDepth_US_Atlantic.png
Image map showing area of modelled projections of water depth for the U.S. Atlantic coast.
PNG
This data release was funded by the USGS Coastal Marine Hazards and Resources Program. 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.
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
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
Madison C. Yawn
2023
Future coastal hazards along the U.S. North and South Carolina coasts
Barnard, P.L., Befus, K., Danielson, J.J., Engelstad, A.C., Erikson, L.H., Foxgrover, A.C., Hardy, M.W., Hoover, D.j., Leijnse, E., Massey, C., McCall, R., Nadal-Caraballo, N., Nederhoff, K., Ohenhen, L., O'Neill, A.C., Parker, K.A., Shirzaei, M., Su, X., Thomas, J.A., van Ormondt, M., Vitousek, S.F., Vos, K. and Yawn, M.C., 2023, Future coastal hazards along the U.S. North and South Carolina coasts: U.S. Geological Survey data release, https://doi.org/10.5066/P9W91314
https://doi.org/10.5066/P9W91314
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
2023
Tropical cyclones or extratropical storms: what drives the compound flood hazard, impact and risk for the US Southeast Atlantic coast?
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., 2023, Tropical cyclones or extratropical storms: what drives the compound flood hazard, impact and risk for the US Southeast Atlantic coast?: in final review at Coastal Engineering, available on EarthArXiv: https://doi.org/10.31223/X56H2
Online_Linkage: https://doi.org/10.31223/X56H2
N.C. Nadal-Caraballo
M.O. Campbell
V.M. Gonzalez
M.J. Torres
J.A. Melby
A.A. Taflanidis
2020
Coastal Hazards System: A Probabilistic Coastal Hazard Analysis Framework
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, vol. 95, p. 1211-1216, 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
High resolution model intercomparison project (HighResMIP v1.0) for CMIP6
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, vol. 9, p. 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
National Engineering Handbook
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 Nadal-Caraballo
Chris Massey
Madison C. Yawn
Anita C. Engelstad
2023
Projections of coastal flood hazards and flood potential for the U.S. Atlantic coast
Barnard, P.L., Erikson, L.H., Nederhoff, K., Parker, K.A., Thomas, J.A., Foxgrover, A.C., O’Neill, A.C., Nadal-Caraballo, N., Massey, C., Yawn, M.C., and Engelstad, A.C., 2023, Projections of coastal flood hazards and flood potential for the U.S. Atlantic coast, in Barnard, P.L., Befus, K., Nadal-Caraballo, N.C., Danielson, J., Engelstad, A., Erikson, L.H., Foxgrover, A.C., Hayden, M.K., 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.F., Vos, K., and Yawn, M.C., 2023, Future coastal hazards along the U.S. Atlantic coast: U.S. Geological Survey data release, https://doi.org/10.5066/P9BQQTCI
https://doi.org/10.5066/P9BQQTCI
T. Leijnse
M. van Ormondt
K. Nederhoff
A. van Dongeren
2021
Modeling compound flooding in coastal systems using a computationally efficient reduced-physics solver: Including fluvial, pluvial, tidal, wind- and wave-driven processes
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 depths 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 the U.S. Atlantic coast 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
MOHC HadGEM3-GC31-HH model output prepared for CMIP6 HighResMIP highres-future
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
MOHC HadGEM3-GC31-HM model output prepared for CMIP6 HighResMIP highres-future
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
MOHC HadGEM3-GC31-HM-SST model output prepared for CMIP6 HighResMIP highresSST-present
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
EC-Earth-Consortium EC-Earth3P-HR model output prepared for CMIP6 HighResMIP highres-future
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
CNRM-CERFACS CNRM-CM6-1-HR model output prepared for CMIP6 ScenarioMIP ssp585
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
NOAA-GFDL GFDL-CM4 model output prepared for CMIP6 ScenarioMIP ssp585
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
CMCC CMCC-CM2-VHR4 model output prepared for CMIP6 HighResMIP
netCDF files
online
Earth System Grid Federation
https://doi.org/10.22033/ESGF/CMIP6.1367
online database
2017
publication date
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
Global water level change indicators from 1950 to 2050 derived from HighResMIP climate projections
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
Historic Water Levels
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
U.S. Geological Survey
2020
Coastal National Elevation Database (CoNED) Project - Topobathymetric Digital Elevation Model (TBDEM) for the Atlantic Coast
raster
online
U.S. Geological Survey
https://topotools.cr.usgs.gov/topobathy_viewer/
online
20210405
Date accessed
TBDEM
Topobathymetric Digital Elevation Model (TBDEM) data for Virginia (Chesapeake Bay), North Carolina, South Carolina, and Georgia used for model input across the entire region. See metadata for each area and read carefully.
Cooperative Institute for Research in Environmental Sciences (CIRES) at the University of Colorado, Boulder
2014
Continuously Updated Digital Elevation Model (CUDEM) - 1/9 Arc-Second Resolution Bathymetric-Topographic Tiles. All Florida subsets
raster
online
NOAA National Centers for Environmental Information
https://doi.org/10.25921/ds9v-ky35
online
20210405
Date accessed
CUDEM
Digital elevation data used for model input in Florida. The most recent data covering Eastern and Southern Florida under 15 m elevation NAVD88 at the time of access were used. Users are advised to read the metadata for this source dataset carefully.
U.S. Geological Survey
2018
USGS one meter for Florida
raster
online
USGS
https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/1m/Projects/FL_Southeast_B1_2018/
digital dataset
20210405
Date accessed
FL one-meter DEM
Digital elevation data used for model input in Florida. The most recent data covering Eastern and Southern Florida under 15 m elevation NAVD88 at the time of access were used to fill gaps/bad areas of CUDEM. Users are advised to read the metadata for this source dataset carefully.
NOAA National Geophysical Data Center
2001
U.S. Coastal Relief Model Vol.3 - Florida and East Gulf of Mexico
raster
online
NOAA National Centers for Environmental Information
https://doi.org/10.7289/V5W66HPP
online
20210405
Date accessed
CRM
Digital elevation data used for model input in Florida. The most recent data covering Eastern and Southern Florida under 15 m elevation NAVD88 at the time of access were used to fill gaps/bad areas of FL one-meter DEM. Users are advised to read the metadata for this source dataset carefully.
Soil Survey Staff, Natural Resources Conservation Service
2022
Web Soil Survey, STATSGO2 Database
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
National Land Cover Database (NLCD) 2016 Land Cover Conterminous United States
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
Ocean wave time-series simulated with a global-scale numerical wave model under the influence of projected CMIP6 wind and sea ice fields
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
Nearshore parametric wave setup hindcast data (1979-2019) for the U.S. Atlantic coast
csv files
online
United States Geological Survey
https://doi.org/10.5066/P9BQQTCI
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
Nearshore parametric wave setup projections (2020-2050) for the U.S. Atlantic coast
csv files
online
United States Geological Survey
https://doi.org/10.5066/P9BQQTCI
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
Nearshore water level, tide and non-tidal residual hindcasts (1979-2016) for the U.S. Atlantic coast
csv files
online
United States Geological Survey
https://doi.org/10.5066/P9BQQTCI
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
Nearshore water level, tide and non-tidal residual projections (2016-2050) for the U.S. Atlantic coast
csv files
online
United States Geological Survey
https://doi.org/10.5066/P9BQQTCI
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
North American Land Data Assimilation System (NLDAS) Primary Forcing Data L4 Hourly 0.125 x 0.125 degree V002
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
NFIE Continental Flood Inundation Mapping - Data Repository
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
The NOAA National Water Model Retrospective dataset, V.2.0
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
Vertical land motion rates for the years 2007 to 2021 for the U.S. Atlantic coast
csv files
online
United States Geological Survey
https://doi.org/10.5066/P9BQQTCI
online database
2023
publication date
VLM
provided vertical land motion for uncertainty calculations
All process steps match those outlined in Barnard and others (2023) for efforts in North Carolina and South Carolina, as the entire region was completed together. Details on processes and methods are in Nederhoff and others (2023); 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, 2023). 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 (2023), 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 (2023), 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 five 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).
NOAA water levels, TBDEM, CUDEM, FL one-meter DEM, CRM
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. We conducted initial comparisons of datasets 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 the U.S. Atlantic coast dataset, also available in this data release). Bias corrections were applied to the projected water levels. See Nederhoff and others (2023) 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 (2023), 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, 2023). 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 (2023), 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. Additionally, all simulations were repeated for seven SLR scenarios: 0, 0.25, 0.50, 1.00, 1.50, 2.00 and 3.00 meters of SLR added compared to baseline water levels in the year 2005.
GTSM, waterLevel_proj, waterLevel_proj, waveSetup_hindc, waveSetup_proj
20210615
Detailed quality control was conducted for test outputs from the model system. After identifying initial sources of error, we reran all simulations.
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, 2023). 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 (see the flood hazard layers contained in Projections of coastal flood hazards and flood potential for the U.S. Atlantic coast, also available in this data release); see Barnard and others (2023) for data in North Carolina and South Carolina. 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.
VLM, TBDEM, CUDEM, FL one-meter DEM, CRM
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_FloodDepth_*STATE*.zip) for file-size considerations. Data are further organized by storm scenario (’RP’) and SLR amount.
Data for North Carolina and South Carolina can be found in Barnard and others (2023). Other U.S. Atlantic coast data are included in this dataset.
20220130
Edits were made to correct spelling in author name. 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)
20230516
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 depth projections [Projections_WaterDepth_*STATE*.zip]
geoTIFF files contain projections of flood-hazard depths.
Producer defined
water_depth
water depth associated with corresponding flood extent of sea-level rise (SLR) and return period (RP) indicated
model-derived
0.05
50.0
meters
0.01
The data contain water depths (depth of water 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_depth), 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, FL_water_depth_SLR200_RP100 contains the water depth for a sea level rise of 200 cm (2 m) during a hundred-year storm in Florida. Data are shown landward of current shoreline locations (not in open ocean) and 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 the U.S. Atlantic coast, also available in this data release).
U.S. Geological Survey
U.S. Geological Survey - CMGDS
mailing and physical
2885 Mission Street
Santa Cruz
CA
95060
831-427-4747
pcmsc_data@usgs.gov
These data are available as zip files with a filename of [Projections_WaterDepth_*STATE*.zip], where *STATE* can be Florida (FL), Georgia (GA), or Virginia (VA).
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 Florida
WinZip
3800
https://doi.org/10.5066/P9BQQTCI
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 Georgia
WinZip
1600
https://doi.org/10.5066/P9BQQTCI
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 Virginia
WinZip
968
https://doi.org/10.5066/P9BQQTCI
Data can be downloaded using the Network_Resource_Name link then scrolling down to the Simulation Data section.
None.
20230516
U.S. Geological Survey, Pacific Coastal and Marine Science Center
PCMSC Science Data Coordinator
mailing and physical
2885 Mission Street
Santa Cruz
CA
95060
831-427-4747
pcmsc_data@usgs.gov
Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998