Li H. Erikson Anita C. Engelstad Kees Nederhoff Alexander L. Nereson Ann E. Gibbs Dee M. Williams Maya K. Hayden Nicole Herman-Mercer 20250912 GeoTIFF data release DOI: 10.5066/P143S6SG Alaska Climate Adaptation Science Center, Anchorage, AK U.S. Geological Survey https://doi.org/10.5066/P143S6SG Li H. Erikson Anita C. Engelstad Kees Nederhoff Alexander L. Nereson Ann E. Gibbs Dee M. Williams Maya K. Hayden Nicole Herman-Mercer 2025 data release DOI: 10.5066/P143S6SG Alaska Climate Adaptation Science Center, Anchorage, AK U.S. Geological Survey Suggested Citation: Erikson, L.H., Engelstad, A.C., Nederhoff, K., Nereson, A.L., Gibbs, A.E., Williams, D.M., Hayden, M.K., and Herman-Mercer, N., 2025, CoSMoS-AK Modeled Flood and Erosion Hazards at Elim - Coastal Alaska: U.S. Geological Survey data release, https://doi.org/10.5066/P143S6SG. https://doi.org/10.5066/P143S6SG Velocity hazards (maximum depth times velocity) from compound coastal hazards—specifically sea-level rise (SLR) and projected coastal storms—are provided for Elim, Alaska. Velocity hazards are a measure of the velocity severity. Categories range from 0 (low hazard) to 4 (extreme hazard) following guidance from the Federal Emergency Management Agency (2020). These products are consistent with other data in this release (for example, flood extent and event-driven erosion; Erikson and others, 2025), supporting integrated coastal hazard assessments for Alaskan communities. The data are provided as gridded maps (GeoTIFFs) for 30 storm and SLR combinations (SLR scenarios 0, 0.5, 1.0, 1.5, 2.0, and 3.0 meters combined with 1-year, 10-year, 20-year, 50-year, and 100-year storm return periods). This product was created to fulfill the Bureau-wide vision of an integrated, predictive science capability to support Alaska Native communities in building resiliency from natural disasters, sovereignty and self-determination, and emergency response and planning capacity. Work was funded by US Geological Survey Climate Adaptation Science Center Project EN05ES9 and partly with funds from Title VII of Division N in the Consolidated Appropriations Act, 2023 (Public Law 117–328) to support direct recovery and rebuilding decisions in the wake of declared disasters related to hurricanes and typhoons in 2022. For more information on these efforts, see Projects - Climate Adaptation Science Centers and https://www.usgs.gov/supplemental-appropriations-for-disaster-recovery-activities/typhoon-merbok-coastal-community. For more information on coastal storm modeling, see https://www.usgs.gov/centers/pcmsc/science/coastal-storm-modeling-system-cosmos. 2025 publication year Complete None planned -162.285098 -162.228122 64.625831 64.601214 USGS Metadata Identifier USGS:fafd6a08-69af-448d-ab2b-c9e794d0c946 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 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 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 Alaska Elim 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. This information is not intended for navigation purposes. 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 Resources supporting this work were provided by USGS Advanced Research Computing (Falgout and others, 2024). The authors would like to acknowledge the following important contributions: Rich Buzard (formerly USGS, now Alaska Native Tribal Health Consortium, ANTHC), Dana Brown (National Oceanic and Atmospheric Administration, NOAA), Amanda Stoltz (University of Delaware), Jacquelyn R Overbeck (ANTHC), Amber Deem (Kawerak, Inc.), Dave Kriebel, Sean McKnight (Kawerak, Inc.), City, Tribe and Corporation of Elim and Unalakleet, Scott Evans and Hina Kilioni, North Slope Burrough, Utqiagvik, and Harper Baldwind (NOAA-Affiliate). The datasets were created in a Windows 11 Operating system, using Matlab v2024. 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, vol. 9, p. 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016. https://doi.org/10.5194/gmd-9-4185-2016 Anita C Engelstad Li H Erikson Borja G Reguero Ann E Gibbs Kees Nederhoff 2024 Engelstad, A.C., Erikson, L.H., Reguero, B.G., Gibbs, A.E., and Nederhoff, K., 2024, Database and time series of nearshore waves along the Alaskan coast from the United States-Canada border to the Bering Sea: U.S. Geological Survey Open-File Report 2023–1094, 23 p., https://doi.org/10.3133/ofr20231094. https://doi.org/10.3133/ofr20231094 J.T. Falgout J. Gordon B. Williams M.J. Davis 2024 Falgout, J.T., Gordon J., Williams B., Davis M. J., USGS Advanced Research Computing, USGS Denali Supercomputer: U.S. Geological Survey, https://doi.org/10.5066/P9PSW367 https://doi.org/10.5066/P9PSW367 Federal Emergency Management Agency (FEMA) 2020 FEMA, 2020, Flood Depth and Analysis Rasters, Guidance Document 14, https://www.fema.gov/sites/default/files/documents/fema_flood-depth-and-analysis-guidance.pdf A.C. O'Neill L.H. Erikson P.L. Barnard P.W. Limber S. Vitousek J. Warrick A.C. Foxgrover J.L. Lovering 2018 O'Neill, A.C., Erikson, L.H., Barnard, P.L., Limber, P.W., Vitousek, S., Warrick, J.A., Foxgrover, A.C., Lovering, J., 2018, Projected 21st Century Coastal Flooding in the Southern California Bight. Part 1: Development of the Third Generation CoSMoS Model: Journal of Marine Science and Engineering, vol. 6, art. 59, https://doi.org/10.3390/jmse6020059. https://doi.org/10.3390/jmse6020059 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, indicative of total uncertainty from elevation data sources, model processes, and vertical land motion. This value is spatially variable and dependent on scenario (return period plus sea level rise). See Process Steps for details on total contributions to uncertainty. Anita C. Engelstad Li H. Erikson Borja G. Reguero Ann E. Gibbs Kees M. Nederhoff 2024 NetCDF online U.S. Geological Survey https://doi.org/10.5066/P931CSO9 online database 2024 publication date downscaled wave data downscaled wave data Li H. Erikson Liv Herdman Chris Flanary Anita C. Engelstad Prasad Pusuluri Patrick L. Barnard Curt D. Storlazzi Michael Beck Borja G. Reguero Kai A. Parker 2022 NetCDF online U.S. Geological Survey https://doi.org/10.5066/P9KR0RFM online database 2022 publication date CMIP6 projected wave data Kees M. Nederhoff Li H. Erikson Anita C. Engelstad 2025 text files online U.S. Geological Survey https://doi.org/10.5066/P143S6SG online database 2025 publication date XBeach model XBeach model Alexander L. Nereson Ann E. Gibbs Li H. Erikson 2025 GeoTIFF online U.S. Geological Survey https://doi.org/10.5066/P13BXTXV online database 2025 publication date TBDEM Topographic and bathymetry elevation data used in models Manoocher Shirzaei Li H. Erikson 2025 comma-delimited text online U.S. Geological Survey https://doi.org/10.5066/P931CSO9 online database 2025 publication date VLM vertical land motion rates Individual surfbeat and non-hydrostatic 2D XBeach models were stood up and tested against known past storm events (models can be found in this release). TBDEM XBeach model 20230201 A set of extreme water level and wave conditions was identified at the approximate open boundaries of each 2D XBeach model. These extreme conditions were derived using wind, atmospheric pressure, and sea ice fields from high-resolution global climate models (GCMs) as forcing inputs to wave and hydrodynamic models. The dynamic downscaling process is described in Erikson and others (2022) and the manuscript of Engelstad and others (2024). Extreme value statistics for 1-, 10-, 20-, 50-, and 100-year return period coastal storms were calculated from both historical (1979–2015) and future projection (2020–2050) GCM runs. The percent change between historical and projected extremes was then computed and applied to similar statistics derived from model runs forced by ERA5 reanalysis wind, pressure, and sea ice data (1979-2022). Percent changes were calculated from the ensemble mean of four GCM simulations: CMCC-CM2-VHR4-r1i1p1f1_gn, CNRM-CM6-1-HR-r1i1p1f2, EC-Earth3P-HR- r1i1p2f1_gr, and HadGEM3-GC31-HM_highresSST-future_r1i1p1f1_gn. CMIP6 downscaled wave data 20241101 2DXBeach production runs: All scenario simulations were done in a two-staged approach whereby XBeach was first run in the surfbeat mode (XB-SB), bed level changes interpolated onto the non-hydrostatic (XB-NH) model grid, and the latter model run. This two-stage approach leverages the strengths of both XBeach modes for a comprehensive simulation of wave dynamics, particularly in the context of swash zone (active beach area) processes and wave runup. The SB mode is computationally efficient and well-suited for capturing long-wave (infragravity) variations and their interactions with the shoreline, including morphodynamic change (erosion and sedimentation). The NH mode provides a detailed, wave-resolved simulation that includes individual wave overtopping. Ran each of the 2DXBeach SB models 30 times for all combinations of 6 SLR scenarios and the 1-, 10-, 20-, 50-, and 100-year return period water level and wave extremes. Interpolated the final bed elevations onto the 2DXBeach NH model grids and ran each of the 2DXBeach NH models 36 times for all combinations of SLR and storms plus the no storm condition. XBeach model 20250301 Post-process and finalize data products: All simulation data were downloaded from the USGS Advanced Research Computing Center’s Hovenweep Supercomputer and post-processed to extract maximum water levels at each grid point over the entire simulation and subsequently interpolated onto the finer resolution (1m x 1m) digital elevation mesh. Interpolations were done using The Mathworks MATLAB linear scatteredinterpolant function which in turn uses a Delaunay triangulation algorithm of the sample points. Significant wave height, erosion and sedimentation, and the velocity-depth product were similarly processed. Maximum water elevation surfaces were depth-differenced to the DEM to separate low-lying vulnerable areas from contiguous coastal flooding. Other output data (water elevation, maximum depth x velocity, sedimentation/erosion, and maximum wave heights) were clipped to the same final flood hazard extent. Additionally, all data below the mean higher high-water line (MHHW) were removed from flood depth projections. Flood potentials (maximum and minimum flood uncertainty) were derived by adding/subtracting estimated model error, DEM uncertainty, 95th percent confidence intervals of the storm event water level, and projected vertical land motion (VLM) for a given SLR to the respective water elevation surface. See O'Neill and others (2018) for a similar approach. Velocity hazard GeoTIFFs were created for all combinations of six SLRs (0, 0.5, 1.0, 1.5, 2.0 and 3.0 m) and 5 storms (1-year, 10-year, 20-year, 50-year, and 100-year return period coastal events), for a total of 30 scenarios. Final GeoTIFFs were separated by community (Velocity_hazard_projections-Elim.zip). Data are organized by storm scenario ('RP') and SLR amount. VLM 20250530 Raster Pixel Universal Transverse Mercator 4 0.9996 -159.00000 0.00000 500000.0 0.00 row and column 1 1 Meters NAD83 GRS 1980 6378137.00 298.257222101 North American Vertical Datum of 1988 0.01 meters Implicit coordinate velocity hazard projections (Velocity_hazard_projections-Elim.zip) GeoTIFF files contain projections of the velocity_hazard_projections Producer defined velocity hazard velocity severity categories (0 indicates low hazard, 4 indicates extreme hazard) associated with corresponding flood extent of indicated sea-level rise (SLR) and return period (RP) model-derived 0 4 none 1 The data contain velocity hazards (maximum depth x velocity with coincident flood hazards) and are organized by return periods. Return periods include statistical once-a-year storms (rp001), every 10 (rp10), every 20 (rp20) and every 100 years (rp100) storms. File names reflect the geographic area of the projection (community, here Elim), the attribute, here velocity hazard (velHzrd), 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, Elim_velHzrd _slr200_rp100 contains velocity hazards for a sea level rise of 200 cm (2 m) during a projected hundred-year storm in Elim, Alaska. U.S. Geological Survey 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 in GeoTIFF format contained in a single zip file with a filename of “Velocity_hazard_projections-Elim.zip” accompanied by CSDGM FGDC-compliant metadata. 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 GeoTIFF files for Elim, AK WinZip 8.3 https://doi.org/10.5066/P143S6SG Data can be downloaded using the Network_Resource_Name link then scrolling down to the Simulation Data section. None. These data can be viewed with ArcGIS or other spatial analysis software. 20250912 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