Projected coastal flooding extents for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in American Samoa

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Frequently anticipated questions:

What does this data set describe?

Title:
Projected coastal flooding extents for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in American Samoa
Abstract:
This data release provides flooding extent polygons based on sea-level rise and wave-driven total water levels for the coast of American Samoa's most populated islands of Tutuila, Ofu-Olosega, and Tau. Oceanographic, coastal engineering, ecologic, and geospatial data and tools were combined to evaluate the increased risks of storm-induced coastal flooding due to climate change and sea-level rise. We followed risk-based valuation approaches to map flooding due to waves and storm surge at 10-m2 resolution along these islands' coastlines for annual (1-year), 20-year, and 100-year return-interval storm events and +0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m sea-level rise scenarios.
Supplemental_Information:
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 dataset 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?
    Alkins, Kristen C., Gaido, Camila L., Reguero, Borja G., and Storlazzi, Curt D., 20240130, Projected coastal flooding extents for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in American Samoa: data release DOI:10.5066/P9RIQ7S7, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

    Online Links:

    This is part of the following larger work.

    Alkins, Kristen C., Gaido, Camila L., Reguero, Borja G., Storlazzi, Curt D., and Fregoso, Theresa A., 2024, Projected coastal flooding extents and depths for 1-, 20-, and 100-year return interval storms and 0.00, +0.25, +0.50, +1.00, +1.50, +2.00, and +3.00 meter sea-level rise scenarios in the Hawaiian, Mariana, and American Samoan Islands: data release DOI:10.5066/P9RIQ7S7, 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: -170.846546803
    East_Bounding_Coordinate: -169.416950927
    North_Bounding_Coordinate: -14.153287314
    South_Bounding_Coordinate: -14.37292361
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 03-Jan-2023
    Ending_Date: 01-Apr-2023
    Currentness_Reference:
    publication date
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: shapefile
  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. It contains the following vector data types (SDTS terminology):
      • GT-polygon composed of chains
    2. What coordinate system is used to represent geographic features?
      Horizontal positions are specified in geographic coordinates, that is, latitude and longitude. Latitudes are given to the nearest 0.000000001. Longitudes are given to the nearest 0.000000001. Latitude and longitude values are specified in Decimal Degrees. The horizontal datum used is D_WGS_1984.
      The ellipsoid used is WGS_1984.
      The semi-major axis of the ellipsoid used is 6378137.0.
      The flattening of the ellipsoid used is 1/298.257223563.
  7. How does the data set describe geographic features?
    [Island]_wd_[sea-level rise scenario]_[RP]_E.shp
    shapefiles consisting of the SLR and storm return period scenario for each island of American Samoa. (Source: Producer defined)
    FID
    Object ID (Source: Esri) automatically generated unique identifiers
    gridcode
    grid ID (Source: producer defined) automatically generated unique identifiers
    Shape
    feature type (Source: Esri) automatically generated unique identifiers
    Entity_and_Attribute_Overview:
    shapefiles contained in this part of the data release include projected flood extents in the following format [Island]_wd_[SLR scenario]_[RP]_E.shp, where [sea-level rise scenario] = one of the 7 sea-level rise scenarios modeled (000cm, 025cm, 050cm, 100cm, 150cm, 200cm, 300cm), and [RP] = one of the following storm return periods (001, 020, 100) Storm scenario return periods cover once-a-year on average storms (001), every 20 years on average (020) and every 100 years on average (100) storms. Files are grouped by island, containing all sea-level rise scenarios, RP and output files. For example, Projections_Floodextents_Tutuila.zip, contains all outputs for Tutuila, within which Tutuila_wd_SLR300_100_E.shp illustrates the flood extents for a sea-level rise of 300 cm (3 m) during a 100-year storm.
    Entity_and_Attribute_Detail_Citation: U.S. Geological Survey

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Kristen C. Alkins
    • Camila L. Gaido
    • Borja G. Reguero
    • Curt D. Storlazzi
  2. Who also contributed to the data set?
  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 flood extent files were created to evaluate the increased risks of storm-induced coastal flooding in the most populated American Samoan Islands of Tutuila, Ofu-Olosega, and Tau due to climate change and sea-level rise. The data are intended to provide a spatially explicit, rigorous valuation of how, where, and when climate change and sea-level rise increase coastal storm-induced flooding to help identify areas where management and/or restoration could potentially help reduce the risk to, and increase the resiliency of, the coastal communities in American Samoa. The data can be used with geographic information systems (GIS) software for research purposes. The methods follow a sequence of steps derived from Storlazzi and others (2019, 2021) and Reguero and others (2021) that integrate physics-based oceanographic and coastal engineering modeling, along with ecologic and geospatial data and tools, to quantify the role of climate change and sea-level rise in increasing coastal flooding hazards.

How was the data set created?

  1. From what previous works were the data drawn?
    WaveWatchIII (source 1 of 6)
    Tolman, H. L., 1997, User manual and system documentation of WAVEWATCH-III version 1.15: NOAA / NWS / NCEP / OMB, online.

    Online Links:

    Type_of_Source_Media: Digital
    Source_Contribution:
    WaveWatchIII wave climate parameters were used as input on the hybrid downscaling approach.
    WaveWatchIII (source 2 of 6)
    Tolman, H. L.,, 1999, User manual and system documentation of WAVEWATCH-III version 1.18.: NOAA / NWS / NCEP / OMB, online.

    Online Links:

    Type_of_Source_Media: Digital
    Source_Contribution:
    WaveWatchIII wave climate parameters were used as input on the hybrid downscaling approach.
    WaveWatchIII (source 3 of 6)
    Tolman, H. L.,, 2009, User manual and system documentation of WAVEWATCH III version 3.14: NOAA / NWS / NCEP / OMB, online.

    Online Links:

    Type_of_Source_Media: Digital
    Source_Contribution:
    WaveWatchIII wave climate parameters were used as input on the hybrid downscaling approach.
    GTSM (source 4 of 6)
    Muis, S., Verlaan, M., Winsemius, H.C., Aerts, J., and Ward, P.J., 2016, A global reanalysis of storm surge and extreme sea levels.: Nature Communications, online.

    Online Links:

    Type_of_Source_Media: Digital
    Source_Contribution:
    The nearest GTSM output point was fit to a General Pareto Distribution (GPD), selecting the maxima using the peak exceedances over a threshold method. The latter was done to calculate the annual (1-year), 20-year, and 100-year storm return period extreme water levels for each location.
    GTSM (source 5 of 6)
    Muis, S., Apecechea, M.I., Dullaart, J., de Lima Rego, J., Madsen, K.S., Su, J., Yan, K., and Verlaan, M., 2020, A high-resolution global dataset of extreme sea levels, tides, and storm surges, including future projections.: Frontiers in Marine Science, online.

    Online Links:

    Type_of_Source_Media: Digital
    Source_Contribution:
    The nearest GTSM output point was fit to a General Pareto Distribution (GPD), selecting the maxima using the peak exceedances over a threshold method. The latter was done to calculate the annual (1-year), 20-year, and 100-year storm return period extreme water levels for each location.
    GTSM (source 6 of 6)
    Muis, S., Apecechea, M., Álvarez, J., Verlaan, M., Yan, K., Dullaart, J., Aerts, J., Duong, T., Ranasinghe, R., Erikson, L., O'Neill, A., Duong, T., le Bars, D., Haarsma, R., and Roberts, M., 2022, Global water level change indicators from 1950 to 2050 derived from HighRes CMIP6 climate projections: Copernicus Climate Change Service Climate Data Store, online.

    Online Links:

    Type_of_Source_Media: digital
    Source_Contribution:
    Source_Contribution: The nearest GTSM output point was fit to a General Pareto Distribution (GPD), selecting the maxima using the peak exceedances over a threshold method. The latter was done to calculate the annual (1-year), 20-year, and 100-year storm return period extreme water levels for each location.
  2. How were the data generated, processed, and modified?
    Date: 2023 (process 1 of 12)
    All processes and methods are outlined in Storlazzi and others (2023); please refer to that for more information beyond the summary in this document Data sources used in this process:
    • Storlazzi and others 2023
    Date: 01-Oct-2021 (process 2 of 12)
    Hindcasted and forecasted deep-water wave data from WaveWatchIII (Tolman 1997, 1999, 2009) simulations forced from four Intergovernmental Panel on Climate Change (IPCC; https://www.ipcc.ch/) Couple Model Intercomparison Project, Phase 6 (CMIP6; https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6) global climate models (GCMs) were produced for 31 years (2020-2050) by Erikson and others (2022) for the Hawaiian, Mariana, and American Samoan Islands. Person who carried out this activity:
    Borja Reguero
    UCSC
    Associate Research Professor
    115 McAllister Way
    Santa Cruz, CA

    831-459-1459 (voice)
    Breguero@ucsc.edu
    Data sources used in this process:
    • WaveWatchIII
    Date: 01-Apr-2021 (process 3 of 12)
    Hindcasted and forecasted tide and storm surge data from GTSM (Verlaan and others, 2015; Muis and others, 2016, 2020) simulations were forced using the same four GCMs for the same 31 years (2020-2050) by Muis and others (2022) for the Hawaiian, Mariana, and American Samoan Islands. Person who carried out this activity:
    Borja Reguero
    UCSC
    Associate Research Professor
    115 McAllister Way
    Santa Cruz, CA

    831-459-1459 (voice)
    Breguero@ucsc.edu
    Data sources used in this process:
    • GTSM
    Date: 01-May-2021 (process 4 of 12)
    Following the methodology of Camus and others (2011) the offshore wave climate data were synthesized into 999 combinations of sea states (wave height, wave periods, and wave directions) that best represented the range of conditions from the Erikson and others (2022) database. Person who carried out this activity:
    Borja Reguero
    UCSC
    Associate Research Professor
    115 McAllister Way
    Santa Cruz, CA

    831-459-1459 (voice)
    Breguero@ucsc.edu
    Data sources used in this process:
    • GTSM
    Date: 15-May-2021 (process 5 of 12)
    The 999 selected sea states were propagated to the coast using the physics-based Simulating Waves Nearshore (SWAN) spectral wave model (Booij and others, 1999; Ris and others, 1999; SWAN, 2016), which simulates wave transformations nearshore by solving the spectral action balance equation. Person who carried out this activity:
    Borja Reguero
    UCSC
    Associate Research Professor
    115 McAllister Way
    Santa Cruz, CA

    831-459-1459 (voice)
    Breguero@ucsc.edu
    Data sources used in this process:
    • SWAN
    Date: 15-Jul-2021 (process 6 of 12)
    The propagated 999 shallow-water wave conditions were extracted at 100-m intervals along the coastline, at a water depth of 30 m, and then reconstructed into hourly time series using multidimensional interpolation techniques (Camus and others, 2011). Person who carried out this activity:
    Borja Reguero
    UCSC
    Associate Research Professor
    115 McAllister Way
    Santa Cruz, CA

    831-459-1459 (voice)
    Breguero@ucsc.edu
    Date: 01-Mar-2021 (process 7 of 12)
    Benthic habitat maps defining coral reef spatial extent and percent coral cover were used to delineate the location of nearshore coral reefs and their relative coral abundance along the reef-lined shorelines. Person who carried out this activity:
    Borja Reguero
    UCSC
    Associate Research Professor
    115 McAllister Way
    Santa Cruz, CA

    831-459-1459 (voice)
    Breguero@ucsc.edu
    Data sources used in this process:
    • USGS
    Date: 01-Oct-2021 (process 8 of 12)
    The nearshore wave time series (hourly data from 2020 to 2050) were fit to a General Pareto Distribution (GPD) selecting the maxima using the peak exceedances over a threshold method, to obtain the significant wave heights associated with the annual (1-year), 20-year, and 100-year storm return periods. Given the differences between GCMs, the GPDs for each GCM were adjusted using a common, unique threshold, which was obtained as the minimum threshold obtained for each extreme value model estimated for each GCM individually. The results correspond to the ensemble, as the mean value for each return period. The corresponding annual (1-year), 20-year, and 100-year storm return period extreme water levels for the location were calculated similarly, from the nearest GTSM output point. Person who carried out this activity:
    Borja Reguero
    UCSC
    Associate Research Professor
    115 McAllister Way
    Santa Cruz, CA

    831-459-1459 (voice)
    Breguero@ucsc.edu
    Data sources used in this process:
    • GCM, GTSM
    Date: 01-Apr-2022 (process 9 of 12)
    The return value significant wave heights and associated peak periods, estimated through a linear regression on the wave time series from SWAN, were then propagated over the coral reefs with corresponding return value sea levels from GTSM along the 100-m spaced shore-normal transects using the numerical model XBeach (Roelvink and others, 2009; XBeach, 2016). XBeach generated forcing wave time series for each modeled storm return period, which were reused as inputs for modeling six sea-level rise scenarios (+0.25 m, +0.50 m, +1.00 m, +1.50 m, +2.00 m, and +3.00 m sea-level rise) under the same return period. Person who carried out this activity:
    Camila Gaido
    UCSC
    Coastal Modeler
    115 McAllister Way
    Santa Cruz, CA

    831-459-1459 (voice)
    cgaido@ucsc.edu
    Data sources used in this process:
    • SWAN, XBeach
    Date: 01-Aug-2022 (process 10 of 12)
    XBeach outputs, extracted at the 0.5 m depth, were used to calculate water level and infragravity waves time series used to force SFINCS. SFINCS is a super-fast flooding model that dynamically calculates two-dimensional compound flooding maps in coastal areas (Leijnse and others, 2021). In this project, SFINCS was coupled with XBeach to compute two-dimensional flood maps for all the islands, storm return periods, and sea-level rise scenarios. Person who carried out this activity:
    Camila Gaido
    UCSC
    Coastal Modeler
    115 McAllister Way
    Santa Cruz, CA

    831-459-1459 (voice)
    cgaido@ucsc.edu
    Data sources used in this process:
    • SFINCS
    Date: 03-Jan-2023 (process 11 of 12)
    All originating rasters for each data layer were imported into ArcMap and converted from netCDF (.nc) to raster (.tif) files. These outputs were clipped to fit the land area of each island. The geoTIFFs were exported as shapefiles from ArcMap for all combinations of seven sea-level rise scenarios (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) for a total of 21 scenarios. Final shapefiles were separated by island (Projections_FloodExtents_*Island*.zip) for file-size considerations. Shapefiles are further organized by storm scenario return period ('RP').
    Date: 20-Aug-2024 (process 12 of 12)
    Minor edits were made to metadata to correct spelling and add clarification. No data were changed. (mau@usgs.gov)
  3. What similar or related data should the user be aware of?
    Storlazzi, Curt D., Reguero, Borja G., Gaido, Camila L., Alkins, Kristen C., Lowrie, Chris, Nederhoff, Kees M., Erikson, Li H., O'Neill, Andrea C., and Beck, Mike W., 2024, Forecasting Storm-Induced Coastal Flooding for 21st Century Sea-Level Rise Scenarios in the Hawaiian, Mariana, and American Samoan Islands.

    Online Links:

    Other_Citation_Details:
    Storlazzi, C.D., Reguero, B.G., Gaido L., C., Alkins, K.C., Lowrie, C., Nederhoff, K.M., Erikson, L.H., O'Neill, A.C., and Beck, M.W., 2024, Forecasting storm-induced coastal flooding for 21st century sea-level rise scenarios in the Hawaiian, Mariana, and American Samoan Islands: U.S. Geological Survey Data Report 1184, 21 p., https://doi.org/10.3133/dr1184.
    Storlazzi, Curt D., Reguero, Borja G., Cole, Aaron D., Lowe, Eric, Shope, James B., Gibbs, Ann E., Nickel, Barry A., McCall, Robert T., Ap R. van Dongeren, and Beck, Mike W., 2019, Rigorously Valuing the Role of U.S. Coral Reefs in Coastal Hazard Risk Reduction.

    Online Links:

    Other_Citation_Details:
    Storlazzi, C.D., Reguero, B.G., Cole, A.D., Lowe, E., Shope, J.B., Gibbs, A.E., Nickel, B.A., McCall, R.T., van Dongeren, A.R., and Beck, M.W., 2019, Rigorously valuing the role of U.S. coral reefs in coastal hazard risk reduction: U.S. Geological Survey Open-File Report 2019-1027, 42 p.
    Storlazzi, Curt D., Reguero, Borja G., Viehman, T. Shay, Cumming, Kristen A., Cole, Aaron D., Shope, James B., Groves, Sarah H., L., Camila Gaido, Nickel, Barry A., and Beck, Mike W., 2021, Rigorously Valuing the Impact of Hurricanes Irma and Maria on Coastal Hazard Risk in Florida and Puerto Rico.

    Online Links:

    Other_Citation_Details:
    Storlazzi, C.D., Reguero, B.G., Viehman, T.S., Cumming, K.A., Cole, A.D., Shope, J.B., Groves, S.H., Gaido L., C., Nickel, B.A., and Beck, M.W., 2021, Rigorously valuing the impact of Hurricanes Irma and Maria on coastal hazard risks in Florida and Puerto Rico: U.S. Geological Survey Open-File Report 2021-1056, 29 p.
    Reguero, Borja G., Storlazzi, Curt D., Gibbs, Ann E., Shope, James B., Cole, Aaron D., Cumming, Kristen A., and Beck, Mike W., 2021, The value of US coral reefs for flood risk reduction.

    Online Links:

    Other_Citation_Details:
    Reguero, B.G., Storlazzi, C.D., Gibbs, A.E., Shope J.B., Cole, A.D., Cumming, K.A., and Beck, M.W., 2021 Author Correction: The value of US coral reefs for flood risk reduction. Nat Sustain 4, 457.
    Booij, N, Ris, R.C., and Holthuijsen, L.H., 1999, A third-generation wave model for coastal regions - 1. Model description and validation.

    Other_Citation_Details:
    Booij, N., Ris, R.C. and Holthuijsen, L.H., 1999, A third-generation wave model for coastal regions - 1. Model description and validation, Journal of Geophysical Research-Oceans, 104(C4): 7649-7666.
    Camus, P., Mendez, F.J., Medina, R., and Cofino, A.S.,, 2011, Analysis of clustering and selection algorithms for the study of multivariate wave climate.

    Other_Citation_Details:
    Camus, P., Mendez, F.J., Medina, R., and Cofino, A.S., 2011. Analysis of clustering and selection algorithms for the study of multivariate wave climate, Coastal Engineering, 58: 453-462.
    Erikson, L.H., Herdman, L., Flahnerty, C., Engelstad, A., Pusuluri, P, Barnard, P.L, Storlazzi, C.D., Beck, M., Reguero, B., and Parker, K., 2022, Ocean wave time-series data simulated with a global-scale numerical wave model under the influence of projected CMIP6 wind and sea ice fields.

    Online Links:

    Other_Citation_Details:
    Erikson, L.H., Herdman, L., Flahnerty, C., Engelstad, A., Pusuluri, P., Barnard, P.L., Storlazzi, C.D., Beck, M., Reguero, B., Parker, K., 2022, Ocean wave time-series data simulated with a global-scale numerical wave model under the influence of projected CMIP6 wind and sea ice fields: U.S. Geological Survey data release
    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.

    Other_Citation_Details:
    Leijnse, T., van Ormondt, M., Nederhoff, K., & 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, 163, 103796.
    Muis, S., Verlaan, M., Winsemius, H.C., Aerts, J., and Ward, P.J., 2016, A global reanalysis of storm surge and extreme sea levels..

    Other_Citation_Details:
    Muis, S., Verlaan, M., Winsemius, H.C., Aerts, J.C.J.H., Ward, P.J., 2016. A global reanalysis of storm surge and extreme sea levels. Nature Communications, 7(7:11969), 1-11.
    Verlaan, M., De Kleermaeker, S., and Buckman, L, 2015, GLOSSIS: Global storm surge forecasting and information system..

    Other_Citation_Details:
    Verlaan, M., De Kleermaeker, S., Buckman, L., 2015, GLOSSIS: Global storm surge forecasting and information system. In: Australasian Coasts & Ports Conference, p. 22.
    XBeach, 2016, XBeach Open Source Community website..

    Online Links:

    Other_Citation_Details:
    XBeach, 2016. XBeach Open-Source Community website. http://oss.deltares.nl/web/xbeach/ [access date: Dec 19, 2016]

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 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 digital elevation model (DEM) locations.
  3. How accurate are the heights or depths?
    Model-derived data are accurate within the flood potential layers, 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.
  4. Where are the gaps in the data? What is missing?
    Dataset is considered complete for the information presented as described in the abstract. Users are advised to read the metadata record 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 No access constraints
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 and University of California, Santa Cruz 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 - ScienceBase
    Denver Federal Center, Building 810, Mail Stop 302
    Denver, CO

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set? These data are available as zip files by island for which [Projections_FloodExtent_*ISLAND*.zip] is the filename, where *ISLAND* could be Tutuila, Ofu-Olosega, or Tau.
  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 ArcGIS or other spatial analysis software.

Who wrote the metadata?

Dates:
Last modified: 10-Sep-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/ScienceBase/DR_P9RIQ7S7/Projections_FloodExtents_AmericanSamoa_metadata.faq.html>
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