Projections of coastal flood velocities for Whatcom County, Northwest Washington State coast (2015-2100)

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What does this data set describe?

Title:
Projections of coastal flood velocities for Whatcom County, Northwest Washington State coast (2015-2100)
Abstract:
Projected flood velocities associated with compound coastal flood hazards for future sea-level rise (SLR) and storm scenarios are provided for Whatcom County, Washington, in a series of raster geotiff files. Projections were made using a system of numerical models with atmospheric forcing, tides, sea level position and stream discharge driven by output from Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 5 (CMIP5). The resulting computed coastal flood velocities along the Whatcom County coast due to predicted sea level rise and future storm conditions consider the changing climate. In addition to sea-level rise, flood simulations run by these numerical models included dynamic contributions from tide, storm surge, wind, waves, and seasonal sea-level fluctuations. In the absence of concordant downscaled GCM stream discharge, daily average stream discharge was fed to the model. Outputs include flood velocities from the combinations of SLR scenarios (0, 0.25, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 5.0 m) storm conditions including 1-year, 5-year, 10-year, 20-year, 50-year and 100-year return interval storms and a background condition (no storm - astronomic tide and average atmospheric conditions). Predicted flood velocities during the largest annual astronomic tides (King Tide) in combination with an average storm surge scenario are also provided.
Supplemental_Information:
This work is part of ongoing research and modeling efforts to evaluate hazards and inform planning for our Nation's coasts. For more information about the USGS Coastal Storm Modeling System (CoSMoS), see https://www.usgs.gov/centers/pcmsc/science/coastal-storm-modeling-system-cosmos. This work was funded by the City of Bellingham, Whatcom County, Port of Bellingham, City of Blaine, United States Environmental Protection Agency, and the United States Geological Survey. 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. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government. 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?
    Grossman, Eric E., vanArendonk, Nathan R., Crosby, Sean C., Tehranirad, Babak, Nederhoff, Kees, Parker, Kai A., Barnard, Patrick L., Erikson, Li, and Danielson, Jeffrey J., 20240213, Projections of coastal flood velocities for Whatcom County, Northwest Washington State coast (2015-2100): data release DOI:10.5066/P9I08NS5, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, California.

    Online Links:

    This is part of the following larger work.

    Grossman, Eric E., vanArendonk, Nathan R., Crosby, Sean C., Tehranirad, Babak, Nederhoff, Kees, Parker, Kai A., Barnard, Patrick L., Erikson, Li, and Danielson, Jeffrey J., 2024, Coastal hazards assessment associated with sea level rise and storms along the Whatcom County, Northwest Washington State coast: data release DOI:10.5066/P9I08NS5, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

    Online Links:

    Other_Citation_Details:
    Suggested Citation: Grossman, E.E., vanArendonk, N.R., Crosby, S.C., Tehranirad, B., Nederhoff, K., Barnard, P.L., Erikson, L., and Danielson, J.J., 2024, Coastal hazards assessment associated with sea level rise and storms along the Whatcom County, Northwest Washington State coast. U.S. Geological Survey data release, https://doi.org/10.5066/P9I08NS5.
  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -123.11107
    East_Bounding_Coordinate: -122.47924
    North_Bounding_Coordinate: 49.04608
    South_Bounding_Coordinate: 48.64034
  3. What does it look like?
    modeling_extent.png (PNG)
    Map showing the study area modeled.
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 2019
    Ending_Date: 2023
    Currentness_Reference:
    start of project work through publication year
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: geoTIFF
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      This is a Raster data set. It contains the following raster data types:
      • Dimensions 46237 x 45228, type Pixel
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      Universal_Transverse_Mercator:
      UTM_Zone_Number: 10
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 0.9996
      Longitude_of_Central_Meridian: -123.00000
      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 North American Datum of 1983.
      The ellipsoid used is GRS 1980.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257222101.
      Vertical_Coordinate_System_Definition:
      Altitude_System_Definition:
      Altitude_Datum_Name: North American Vertical Datum of 1988
      Altitude_Resolution: 0.01
      Altitude_Distance_Units: meters
      Altitude_Encoding_Method:
      Explicit elevation coordinate included with horizontal coordinates
  7. How does the data set describe geographic features?
    flood velocity projections
    geoTIFF files contain projections of flood-hazard velocities (Source: Producer defined)
    velocity
    flood velocity over land surface associated with corresponding flood associated with sea-level rise (SLR) and return period (RP) indicated (Source: model-derived)
    Range of values
    Minimum:0.00
    Maximum:2.50
    Units:meters per second
    Resolution:0.01
    Entity_and_Attribute_Overview:
    The data contain flood velocities (flow velocities of flood water over the land surface associated with coincident flood hazards). Return periods cover average conditions (RP0000), once-a-year storms (RP0001), every 5 (RP0005), every 10 (RP0010), every 20 (RP0020), every 50 (RP0050) and every 100 years (RP0100) storms, and a King tide scenario (RPKing). File names reflect the attribute (velocity), the area (county) of the projection (domain id, D1), 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 500 cm (SLR500). For example, velocity_D1_SLR200_RP0100 contains the flood velocities for Whatcom County (D1) sea level rise of 200 cm (2 m) during a hundred-year storm. For each scenario, a minimum and maximum potential flood velocities representing the uncertainty of each paired RP and SLR scenario are included. Data are shown landward of current shoreline locations (not in open ocean) and are spatially consistent for coastal flood hazards of the same scenario.
    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)
    • Eric E. Grossman
    • Nathan R. vanArendonk
    • Sean C. Crosby
    • Babak Tehranirad
    • Kees Nederhoff
    • Kai A. Parker
    • Patrick L. Barnard
    • Li Erikson
    • Jeffrey J. Danielson
  2. Who also contributed to the data set?
    This dataset was funded by the City of Bellingham, Whatcom County, Port of Bellingham, the City of Blaine, United States Environmental Protection Agency, and the United States Geological Survey.
  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 to support the information needs of policy makers, resource managers, science researchers, students, and the general public. These projections of flood velocities 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 exposure to flooding and 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?
  2. How were the data generated, processed, and modified?
    Date: 15-Jan-2020 (process 1 of 10)
    Generated XBeach cross-shore transects every 50 m alongshore and spanning elevations of -6 m to +10 m (NAVD88) with constant grid spacing of 0.5 m to accommodate the region's short period waves. Transects were attributed with elevations derived from a seamless topographic and bathymetric digital elevation model (Tyler and others 2020). Example XBeach model transects are available elsewhere in this data release.
    Date: 15-Feb-2020 (process 2 of 10)
    Generated rectangular SFINCS model domains across the study area each with a maximum size of 100,000 cells. A total of 29 individual domains were created reflecting the 10 m cell size, although a higher model resolution of 1 m was accommodated by use of a subgrid lookup tables (Nederhoff and others, 2024). The SFINCS models included spatially varying Manning's n roughness coefficients following Nederhoff and others (2021) based on the USGS National Land Cover Database (Dewitz, 2021). Example SFINCS model domains are available elsewhere in this data release.
    Date: 15-Apr-2020 (process 3 of 10)
    Prepared atmospheric forcing data for input into the model system for the validation analyses (December 2018) sourced from the Canadian High-Resolution Deterministic Product System (HRDPS) model, the 34-year (1981-2015) hindcast based on a Weather Research and Forecasting reanalysis carried out by the Pacific Northwest National Laboratory (Gao and others, 2017) and the 85-yr (2015-2100) dynamically downscaled Geophysical Fluid Dynamics Laboratory (GFDL) CM3 model forecast for CMIP5 (CMIP5-GFDL-CM3) derived by Mass and others (2022).
    Date: 01-Jul-2020 (process 4 of 10)
    Prepared time series of tide and surge water levels from a regional Delft3D Flexible Mesh (Delft3D FM) model (Grossman and others, 2023), and waves from a regional wave model alongshore between the -5 and -10 m depth contour (Crosby and others, 2023).
    Date: 01-Nov-2020 (process 5 of 10)
    Computed wave transformation and wave runup using a novel XBeach based lookup table approach. XBeach was run with a subset of water levels and wave parameters at each transect location to develop a lookup table of nearshore wave parameters and wave setup as a function of offshore waves and water levels. The lookup table was then used to generate a timeseries of wave driven water levels in the nearshore at all SFINCS ocean boundaries.
    Date: 01-Mar-2021 (process 6 of 10)
    Generated a synthetic continuous 300-year record of water levels, winds and waves from the 85-yr GCM forecast in order to determine a reliable 1 in 100-year event (1 percent chance event) using empirical extreme value analysis. The synthetic record was created by randomly selecting a yearly NTR signal from the 85-year record and applying a uniform distribution shift from -1 to +1 days to the time axis to increase variability. Tides were generated from astronomical components computed from the tide-only model results (Nederhoff and others, 2024) assuming independence between the tides and surge and assuming meteorological and wave conditions to be completely correlated with NTR.
    Date: 01-Jun-2021 (process 7 of 10)
    Computed total water level along each XBeach transect using the regional Delft 3D FM model and wave parameter timeseries. Approximately 300 maximum water level events were found for the 300-year synthetic time-series with independent storm peaks de-clustered using a 3-day window.
    Date: 01-Sep-2021 (process 8 of 10)
    Simulated the combined influence of tide and surge, incoming waves, stream discharge and wind speed on flood velocities using SFINCS for the more than 300 annual maximum water level events derived from XBeach along with the daily average and King Tide condition (Nederhoff and others, 2024). Simulated all storm and sea level rise combinations with 0.5 m lower and higher stillwater level inputs to SFINCS representing uncertainty to produce a minimum and maximum potential flood velocities for each primary RP and SLR scenario.
    Date: 01-Dec-2021 (process 9 of 10)
    Computed extreme recurrence values for flood velocities cell by cell for each SFINCS domain based on the more than 300 simulations based on an empirical frequency of exceedance without fitting an extreme value distribution.
    Date: 15-Jan-2022 (process 10 of 10)
    Merged flood velocity outputs for the multiple individual domains into single county-wide raster for all combinations of 9 SLR scenarios (0, 0.25, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 5.0 m), 8 storms (1-year, 5-year, 10-year, 20-year, 50-year and 100-year return period coastal events), the non-storm and King Tide condition (for a total of 72 scenarios). The overland flood velocity outputs including uncertainty layers were subsequently downscaled using subgrid tables (Leijnse and others, 2020) in combination with a box filter for smoothing. Overlapping domains were merged using an average and water depths less than 5 cm were designated as dry. Final geoTIFFs are organized by SLR amount (SLR).
  3. What similar or related data should the user be aware of?
    Mass, Clifford F., Salathe, Eric P., Steed, Richard, and Baars, Jeffrey, 2022, The Mesoscale Response to Global Warming over the Pacific Northwest Evaluated Using a Regional Climate Model Ensemble.

    Online Links:

    Other_Citation_Details:
    Mass, C.F., Salathe, E.P., Steed, R., and Baars, J., 2022, The mesoscale response to global warming over the Pacific Northwest evaluated using a regional climate model ensemble: Journal of Climate, v. 35, p. 2035-2053.
    Gao, Yang, Leung, Ruby L., Zhao, Chun, and Hagos, Samson, 2017, Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions.

    Online Links:

    Other_Citation_Details:
    Gao, Y., Leung, R.L., Zhao, C., and Hagos, S., 2017, Sensitivity of U.S. summer precipitation to model resolution and convective parameterizations across gray zone resolutions: Journal of Geophysical Research-Atmospheres, v. 122, p. 2714-2733.
    Dewitz, Jon, 2021, National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021).

    Online Links:

    Other_Citation_Details:
    Dewitz, J., and U.S. Geological Survey, 2021, National Land Cover Database (NLCD) 2019 Products (ver. 2.0, June 2021): U.S. Geological Survey data release, https://doi.org/10.5066/P9KZCM54
    Tyler, D.J., Danielson, J.J., Grossman, E.E., and Hockenberry, R.J., 2020, Topobathymetric Model of Puget Sound, Washington, 1887 to 2017.

    Online Links:

    Other_Citation_Details:
    Tyler, D.J., Danielson, J.J., Grossman, E.E., and Hockenberry, R.J., 2020, Topobathymetric model of Puget Sound, Washington, 1887 to 2017: U.S. Geological Survey data release, https://doi.org/10.5066/P95N6CIT.
    Grossman, Eric E, Tehranirad, Babak, Nederhoff, Kees, Crosby, Sean, Stevens, Andrew W, VanArendonk, Nathan R, Nowacki, Daniel, Erikson, Li, and Barnard, Patrick, 2023, Modeling extreme water levels in the Salish Sea: A new method for estimating sea level anomalies for application in hydrodynamic simulations.

    Online Links:

    Other_Citation_Details:
    Grossman, E.E., Tehranirad, B., Nederhoff, C.M., Crosby, S.C., Stevens, A.W., Van Arendonk, N.R., Nowacki, D.J., Erikson, L.H., Barnard, P.L. Modeling Extreme Water Levels in the Salish Sea: The Importance of Including Remote Sea Level Anomalies for Application in Hydrodynamic Simulations. Water 2023, 15, 4167. https://doi.org/10.3390/w15234167.
    Leijnse, Tim, Nederhoff, Kees, Dongeren, A. van, McCall, Robert, and Ormondt, Marten Van, 2020, Improving Computational Efficiency of Compound Flooding Simulations: the SFINCS Model with Subgrid Features, NH022-0006, 2020..

    Other_Citation_Details:
    Leijnse, T., Nederhoff, K., van Dongeren, A., McCall, R.T., Van Ormondt, M., 2020, Improving computational efficiency of compound flooding simulations: the SFINCS model with subgrid features: Abstract, AGU Fall Meeting, 2020, NH022-0006.
    Nederhoff, Kees, Saleh, Rohin, Tehranirad, Babak, Herdman, Liv, Erikson, Li, Barnard, Patrick L., and Mick van der Wegen, 2021, Drivers of extreme water levels in a large, urban, high-energy coastal estuary. A case study of the San Francisco Bay.

    Online Links:

    Other_Citation_Details:
    Nederhoff, K., Saleh, R., Tehranirad, B., Herdman, L., Erikson, L., Barnard, P.L., and van der Wegen, M., 2021, Drivers of extreme water levels in a large, urban, high-energy coastal estuary. A case study of the San Francisco Bay: Coastal Engineering, v. 170, 103984, https://doi.org/10.1016/j.coastaleng.2021.103984.
    Crosby, Sean C., Nederhoff, Kees, VanArendonk, Nathan R., and Grossman, Eric E., 2023, Efficient modeling of long-period wave propagation and short-period wind-wave generation: a comparison of several methods in a semi-enclosed estuary..

    Online Links:

    Other_Citation_Details:
    Crosby, S. C., Nederhoff, K, vanArendonk, N. R., Grossman, E. E. 2023. Efficient modeling of long-period wave propagation and short-period wind-wave generation: a comparison of several methods in a semi-enclosed estuary, Journal of Ocean Modelling, https://doi.org/10.1016/j.ocemod.2023.102231.
    Nederhoff, Kees, Crosby, Sean, Arendonk, Nathan van, Grossman, Eric, Tehranirad, Babak, Leijnse, Tim, Klessens, Wouter, and Barnard, Patrick, 2024, Dynamic Modeling of Coastal Compound Flooding Hazards Due to Tides, Extratropical Storms, Waves, and Sea-Level Rise: A Case Study in the Salish Sea, Washington (USA).

    Online Links:

    Other_Citation_Details:
    Nederhoff, K.; Crosby, S.C.; Van Arendonk, N.R.; Grossman, E.E.; Tehranirad, B.; Leijnse, T.; Klessens, W.; Barnard, P.L. Dynamic Modeling of Coastal Compound Flooding Hazards Due to Tides, Extratropical Storms, Waves, and Sea-Level Rise: A Case Study in the Salish Sea, Washington (USA). Water 2024, 16, 346. https://doi.org/10.3390/w16020346.

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 flood velocities due to plausible sea-level rise and future storm conditions, and therefore cannot be validated against observations.
  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 published uncertainty bounds that are provided with a minimum and maximum flood velocities layer surrounding each best estimate for combined sea level and storm scenario. Error accounts for total uncertainty from elevation and other contributing data sources, model processes, and vertical land motion. This value is spatially variable and dependent on scenario. See Process Steps and other layers 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) and will be updated as necessary as improvements are developed. Users are advised to read the metadata record and cited references carefully for additional details.
  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(s) of the dataset and in products derived from these data. This information is not intended for navigation purposes.
  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? This dataset consists of 18 zipped data files. There are two data files for each of 9 sea level rise scenarios; one containing the central estimate, the second containing the uncertainty layers. Each packaged output file contains all 8 storm recurrence events for the associated sea level rise scenario. The central estimate files are named velocity_<DomainID>_<SLRScenario>.zip and the uncertainty files are named velocity_<DomainID>_<SLRScenario>_uncertainty.zip where <DomainID> refers to the ID of each coastal county in Washington State following (D1=Whatcom, D2=Skagit, D3=Island, D4=Snohomish, D5=King; 6=Pierce, D7=Thurston, D8=Mason, D9=Kitsap, D10=Jefferson, D11=Clallum, D12=Grays Harbor, D13=Pacific, D14=Wahkiakum) and SLR scenario is defined as a number code in the form XXXX in centimeters (for example SLR0025 is SLR of 0.25m), and uncertainty serves the minimum and maximum estimates.
  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.
  4. How can I download or order the data?
  5. What hardware or software do I need in order to use the data set?
    These data can be viewed with GIS software.

Who wrote the metadata?

Dates:
Last modified: 13-Feb-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_P9I08NS5/velocity_D1_CoSMoS_WA_metadata.faq.html>
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