Kai A. Parker Kees Nederhoff Li H. Erikson Anita C. Engelstad Maya K. Hayden Patrick L. Barnard Eric E. Grossman 20251126 GeoTIFF data release DOI: 10.5066/P13HYXKY Pacific Coastal and Marine Science Center, CA U.S. Geological Survey https://doi.org/10.5066/P13HYXKY Kai A. Parker Kees Nederhoff Li H. Erikson Anita C. Engelstad Maya K. Hayden Patrick L. Barnard Eric E. Grossman 2025 data release DOI: 10.5066/P13HYXKY Pacific Coastal and Marine Science Center, CA U.S. Geological Survey Suggested Citation: Parker, K., Nederhoff, K., Erikson L.H., Engelstad, A.C., Hayden, M.K., Barnard, P.L., and Grossman, E.E., 2025, CoSMoS-Puget Sound Modeled Flood Hazards at King County - Washington: U.S. Geological Survey data release, https://doi.org/10.5066/P13HYXKY. https://doi.org/10.5066/P13HYXKY Flood duration (maximum hours per years that occurs on average once every 'rp' years) associated with coincident compound coastal hazards—specifically sea-level rise (SLR), projected coastal storms, and streamflow—are provided for King County, Washington. The flood duration products are consistent with other data in this release (for example, flood extent and water elevation), supporting integrated coastal hazard assessments for Washington communities. The data are provided as gridded rasters (GeoTIFFs) for 42 storm and SLR combinations (SLR scenarios 0, 0.25, 0.5, 1.0, 1.5, 2.0, and 3.0 meters (m) combined with 1-year, 10-year, 20-year, 50-year, and 100-year storm return periods, as well as the background, no storm, conditions). This product was created to fulfill the Bureau-wide vision of an integrated, predictive science capability to support Washington communities in building resiliency from natural disasters, sovereignty and self-determination, and emergency response and planning capacity. These data are intended for policy makers, resource managers, science researchers, technical users, students, and the general public. These data are not intended to be used for navigation. This work is one portion of ongoing national modeling efforts. The Coastal Storm Modeling system (CoSMoS) uses several tiers of numerical models to make detailed predictions (meter-scale) of flooding and erosion over large geographic scales. For more information on CoSMoS implementation, see https://www.usgs.gov/centers/pcmsc/science/coastal-storm-modeling-system-cosmos 2025 publication year Complete None planned -122.583957 -122.024762 47.819631 47.206286 USGS Metadata Identifier USGS:67673aa7-fbb5-422b-a60a-40d85df77f8a Data Categories for Marine Planning Physical Habitats and Geomorphology Global Change Master Directory (GCMD) Hazards Planning Ocean Waves Ocean Winds Beaches 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 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 Washington King County 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. These data are marked with a Creative Common CC0 1.0 Universal License. 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 This work was funded by the United States Geological Survey (USGS) Coastal and Marine Hazards and Resources Program, King County Department of Natural Resources under Statement of Work 21ZPCOLL210705, and the United States Environmental Protection Agency (EPA) under Statement of Work DW-014-92578201/ EPA-CF-0000007695 to the USGS. The contents of this document do not necessarily reflect the views and policies of the EPA, nor does mention of trade names or commercial products constitute endorsement or recommendation for use. Computation resources were supported by the USGS Advanced Research Computing (Falgout and others, 2024). The datasets were created in a Windows 11 Operating system, using python 3.12.9. 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 Eric E. Grossman Babak Tehranirad Kees Nederhoff Sean C. Crosby Andrew W. Stevens Nathan R. Van Arendonk Daniel J. Nowacki Li H. Erikson Patrick L. Barnard 2023 Grossman, E. E., Tehranirad, B., Nederhoff, C. M., Crosby, S. C., Stevens, A. W., Van Arendonk, N. R., Nowacki D. J., Erikson, L. H., and Barnard, P. L. (2023). Modeling Extreme Water Levels in the Salish Sea: The Importance of Including Remote Sea Level Anomalies for Application in Hydrodynamic Simulations. Water, 15(23), 4167. https://doi.org/10.3390/w15234167 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 Sean C. Crosby Kees Nederhoff Nathan R. VanArendonk Eric E. Grossman 2023 Crosby, S. C., Nederhoff, K., VanArendonk, N.R., and Grossman, E.E., 2023, Efficient modeling of wave generation and propagation in a semi-enclosed estuary: Ocean Modeling, https://doi.org/10.1016/j.ocemod.2023.102231. https://doi.org/10.1016/j.ocemod.2023.102231 Kees Nederhoff Kai Parker Eric E. Grossman 2025 Nederhoff, K., Parker, K., and Grossman, E., 2025, Beyond the 100-Year Flood: Probabilistic Flood Hazard Assessment for King and Pierce Counties under Future Climate Scenarios: EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-4909. https://doi.org/10.5194/egusphere-2025-4909 Joost Buitink Brendan Dalmijn Kai Parker Kees Nederhoff 2025 Buitink, J., Dalmijn, B., Parker, K.A., Nederhoff, K. and Grossman, E., 2025, Wetter Winters, Drier Summers: Quantifying the change in hydrological response around the Puget Sound area using the wflow_sbm hydrological model and CMIP6 projections: EarthArXiv [preprint], https://doi.org/10.31223/X58R0G. https://doi.org/10.31223/X58R0G Attribute values are model-derived duration of innundation 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 digital elevation model (DEM) locations. Model-derived data are accurate within published uncertainty bounds, indicative of total uncertainty from elevation data sources and model processes. See Nederhoff and others, 2025 for additional information on vertical accuracy and uncertainty layer development. D.J. Tyler Jeff J. Danielson Eric E. Grossman R.J. Hockenberry 2020 GeoTIFF online U.S. Geological Survey https://doi.org/10.5066/P95N6CIT online database 2020 publication date DEM DEM 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 2.0 NetCDF online U.S. Geological Survey https://doi.org/10.5066/P9KR0RFM online database 2022 publication date CMIP6 Waves CMIP6 Waves Eric E. Grossman Babak Tehranirad Andrew W. Stevens Nathan R. VanArendonk Sean Crosby Kees Nederhoff 2023 online U.S. Geological Survey https://doi.org/10.5066/P946SC3L online database 2023 publication date Hydrodynamic Model Hydrodynamic Model Hans Hersbach Bill Bell Paul Berrisford Shoji Hirahara András Horányi Joaquín Muñoz-Sabater Julien Nicolas Carole Peubey Raluca Radu Dinand Schepers Adrian Simmons Cornel Soci Saleh Abdalla Xavier Abellan Gianpaolo Balsamo Peter Bechtold Gionata Biavati Jean Bidlot Massimo Bonavita Giovanna De Chiara Per Dahlgren Dick Dee Michail Diamantakis Rossana Dragani Johannes Flemming Richard Forbes Manuel Fuentes Alan Geer Leo Haimberger Sean Healy Robin J. Hogan Elías Hólm Marta Janisková Sarah Keeley Patrick Laloyaux Philippe Lopez Cristina Lupu Gabor Radnoti Patricia de Rosnay Iryna Rozum Freja Vamborg Sebastien Villaume Jean-Noël Thépaut 2018 NetCDF online Copernicus Climate Change Service (C3S) Climate Data Store (CDS) https://doi.org/10.24381/cds.adbb2d47 online database 2018 publication date ERA5 Reanalysis Forcing Data Regional scale data were created for the full Puget Sound region using a hydrodynamic, wave, and hydrologic model. All regional scale models were run for 3 periods: reanalysis (1940-2023), historic CMIP6 (1950–2015) and future CMIP6(2015-2050). Forcing for the reanalysis period was provided by the ERA5 dataset and by 7 CMIP6 models for the historic and future periods (HighResMIP, Haarsma and others, 2016). The Hydrodynamic Model (delft3d-FM) was used for simulating regional water levels and is detailed in Grossman and others, 2023. Waves were modelled using a SWAN lookup table with linear swell propagation as detailed in Crosby and others, 2023. In addition to winds, swell was added through the Strait of Juan de Fuca via modeled CMIP6 Waves and from data provided by ERA5. Hydrology was provided by a county level hydrological model detailed in Buitink and others, 2025. The hydrodynamic and wave models were additionally run for a variety of SLR positions (0, 25, 50, 100, 150, 200, 300 cm). Outputs were saved as station output across the full Puget Sound region, in the nearshore for wave and water level data and at a variety of elevation contours for hydrologic data. ERA5 Hydrodynamic Model CMIP6 Waves 20240101 A SFINCS model was developed following the methodology detailed in Nederhoff and others, 2025. The model was built using topographic/bathymetry data from a DEM and covered the full extent of King County. Models were forced with continuous oceanographic and hydrologic forcing (provided by the regional model) for 2 periods: a reanalysis period and future projection period. The reanalysis period was used for model validation and the projection period was used for producing CoSMoS hazard outputs. For the future projection period, the model was run continuously for 100 years at 6 SLR scenarios. Outputs of maximum water levels were saved for each simulation year. DEM 20250101 All simulation data were downloaded from the USGS Advanced Research Computing Center’s Hovenweep Supercomputer (Falgout and others, 2024) to be post-processed. For each of the 6 SLR scenarios, the 1-, 10-, 20-, 50-, and 100-year return period hazard level was derived using empirical return intervals from the 100-year time series of annual max water levels. Output data (flood depth, water elevation, maximum depth x velocity, flood extent) were derived from the occurrence of the water level extreme. Flood depth and extent were post-processed with an attenuated bathtub to remove edge effects and therefore do not match the spatial extent of the other products. Flood duration was calculated as the maximum that occurs on average once every 'rp' years. Practically this was calculated by sampling 1000 ‘rp’ year samples, with duration being the duration wet for the sample year in hours per year. For each ‘rp’ year sample, the max duration wet was calculated. Then the average across the 1000 samples was taken. Flood durations were then smoothed using a gaussian filter with a standard deviation of 6 cells. GeoTIFFs were created and bundled by county (Flood_duration_projections-King.zip). Data are organized by storm scenario (’RP’) and SLR magnitude. 20250701 Raster Pixel Universal Transverse Mercator 10 0.9996 -123.0 0.00000 500000.0 0.00 row and column 2 2 Meters NAD83 GRS 1980 6378137.00 298.257222101 North American Vertical Datum of 1988 0.01 meters Implicit coordinate flood duration projections (Flood_duration_projections-King.zip) GeoTIFF files contain projections of flood durations. Producer defined flood duration maximum flood duration associated with corresponding flood extent of indicated sea-level rise (SLR) and return period (RP). Values of -999 = no data. model-derived -999 No Data Producer defined 0 8760 hours per year 1 The data contain flood duration (duration of flooding associated with coincident flood hazards) and are organized by return periods. Return periods include the no storm ‘average’ condition (rp000), and 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 (County, here King County), the attribute, here flood duration (duration), the sea-level rise (slr) scenario, and the return period (rp) of storm conditions. SLR scenarios are listed in centimeters (cm) and range from no SLR (slr000) to a SLR of 300 cm (slr300). For example, King_duration_slr200_rp100 contains flood durations for a sea level rise of 200 cm (2 m) during a projected hundred-year storm in King County, Washington. Data were written out in int32 to represent the valid resolution of 1 hour. 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 filenames of “Flood_duration_projections-King.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 King County, WA WinZip 167.8 https://doi.org/10.5066/P13HYXKY 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 any spatial analysis software (for example, ArcGIS, QGIS, and so on) 20251126 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