Nearshore total water level (TWL) proxies (2018-2100) for Northern California

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Frequently anticipated questions:

What does this data set describe?

Title:
Nearshore total water level (TWL) proxies (2018-2100) for Northern California
Abstract:
Nearshore proxies for total water level (TWL) developed for Coastal Storm Model (CoSMoS) work in Northern California 3.2 are presented. Deterministic dynamical modeling of future climate conditions and associated hazards, such as flooding, can be computationally-expensive if century-long time-series of waves, sea level variations, and overland flow patterns are simulated. To focus such modeling on storm events of interest, local impacts over long time periods and large geographical areas are estimated. Nearshore proxies for total water level (TWL) are generated via a computationally simple approach, assuming a linear superposition of the important processes contributing to overall total water level. A time series of TWL proxies is used as the basis for 1) identifying coastal segments that respond similarly to region-wide coastal storms, 2) selecting storm events for detailed hydrodynamic modeling within CoSMoS, and 3) to drive long-term shoreline change and bluff retreat models.
Supplemental_Information:
This work is one supporting part of ongoing modeling efforts for California and the western United States. For information on data sources and details on methodology of this dataset, see source information below. For more information on CoSMoS implementation, see https://www.usgs.gov/centers/pcmsc/science/coastal-storm-modeling-system-cosmos?qt-science_center_objects=0#qt-science_center_objects
  1. How might this data set be cited?
    O'Neill, Andrea C., Erikson, Li H., and Barnard, Patrick L., 20221012, Nearshore total water level (TWL) proxies (2018-2100) for Northern California: data release DOI:10.5066/P9048D1S, U.S. Geological Survey, Pacific Coastal and Marine Science Center Santa Cruz, California.

    Online Links:

    This is part of the following larger work.

    Barnard, Patrick L., Erikson, Li H., Foxgrover, Amy C., Limber, Patrick W., O'Neill, Andrea C., Thomas, Jenny, and Vitousek, Sean, 2022, Coastal Storm Modeling System (CoSMoS) for northern California 3.2: data release DOI:10.5066/P9048D1S, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, California.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -124.4448
    East_Bounding_Coordinate: -122.4774
    North_Bounding_Coordinate: 41.9985
    South_Bounding_Coordinate: 37.8921
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: Oct-2022
    Currentness_Reference:
    year and month of publication
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form:
    Nearshore proxies for total water level (TWL) data in NetCDF format
  6. How does the data set represent geographic features?
    1. How are geographic features stored in the data set?
      Indirect_Spatial_Reference:
      data represented at user-defined cross-shore transects throughout study region
    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: 0.999600
      Longitude_of_Central_Meridian: -100.000000
      Latitude_of_Projection_Origin: 0.000000
      False_Easting: 500000.000000
      False_Northing: 0.000000
      Planar coordinates are encoded using row and column
      Abscissae (x-coordinates) are specified to the nearest 2.000000
      Ordinates (y-coordinates) are specified to the nearest 2.000000
      Planar coordinates are specified in meters
      The horizontal datum used is North American Datum 1983 (NSRS2007).
      The ellipsoid used is Geodetic Reference System 80.
      The semi-major axis of the ellipsoid used is 6378137.000000.
      The flattening of the ellipsoid used is 1/298.257222.
      Vertical_Coordinate_System_Definition:
      Depth_System_Definition:
      Depth_Datum_Name: Mean sea level
      Depth_Resolution: .01
      Depth_Distance_Units: meters
      Depth_Encoding_Method: Attribute values
  7. How does the data set describe geographic features?
    Entity_and_Attribute_Overview:
    NetCDF files are self-contained and attribute information may be found in the header of the file itself and with each variable.
    Entity_and_Attribute_Detail_Citation:
    The entity and attribute information was generated by the individual and/or agency identified as the originator of the data set. Please review the rest of the metadata record for additional details and information.

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Andrea C. O'Neill
    • Li H. Erikson
    • Patrick L. Barnard
  2. Who also contributed to the data set?
    This project was funded by U.S. 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
    US

    831-460-4747 (voice)
    831-427-4748 (FAX)
    pcmsc_data@usgs.gov

Why was the data set created?

These data are intended for science researchers and technical users.

How was the data set created?

  1. From what previous works were the data drawn?
    GCM data (source 1 of 7)
    Pierce, D.W., Cayan, D.R., and Thrasher, B.L., 2015, LOCA Statistical Downscaling (Localized Constructed Analogs): Scripps Institute of Oceanography, University of California, San Diego, California, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    statistically downscaled Global Climate Model (GCM) data for California
    wave model data (source 2 of 7)
    Erikson, L.H., Storlazzi, C.D., Barnard, P.L., Hegermiller, C.E., and Shope, J.B., 2016, Wave and wind projections for United States Coasts; Mainland, Pacific Islands, and United States-Affiliated Pacific Islands: U.S. Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online dataset
    Source_Contribution:
    historical and future wave conditions generated from WaveWatch III simulations of Global Climate Models
    buoy observations (source 3 of 7)
    National Data Buoy Center (NDBC), 2019, Historical data for various buoy stations: National Oceanic and Atmospheric Administration (NOAA), online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    ocean wave and meteorological observation records at buoy stations
    water level measurements (source 4 of 7)
    National Oceanic and Atmospheric Administration (NOAA), 2019, NOAA Water Level Information for Tide Stations: National Oceanic and Atmospheric Administration (NOAA), online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    water level measurements at various tide stations for determination of storm surge estimation parameters
    satellite-derived SST (source 5 of 7)
    National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory, 2019, NOAA High-resolution Blended Analysis of Daily SST: National Oceanic and Atmospheric Administration (NOAA), online.

    Online Links:

    Type_of_Source_Media: online dataset
    Source_Contribution:
    sea surface temperature data used for determination of sea level anomoly parameters
    CDIP (source 6 of 7)
    Coastal Data Information Program (CDIP)), 2019, MOP v1.1 model output: Scripps Institute of Oceanography, University of California, San Diego, California, online.

    Online Links:

    Type_of_Source_Media: online database
    Source_Contribution:
    nearshore wave information used for determination of estimated runup parameters
    CSTs (source 7 of 7)
    Foxgrover, A.C., Erikson, L.H., and O'Neill, A.C., 2022, Northern California cross-shore transects used in CoSMoS 3.2: U.S. Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online dataset
    Source_Contribution:
    cross-short transects (CSTs) used throughout study region for model setup
  2. How were the data generated, processed, and modified?
    Date: 19-Aug-2019 (process 1 of 4)
    Same methods as laid out in Erikson and others (2018) with changes for geography of this study region as noted in following steps. Deep-water waves are translated to estimates of nearshore values at designated CSTs using the look-up table (LUT) approach laid out in Erikson and others (2018). Rather than using a spectral wave approach, the LUT was populated by relating nearshore conditions from CDIP with deep-water conditions from wave model data and wave observations at buoys in the region over the years 2000-2017. In the manner described in Erikson and others (2018) observations from four buoys were employed: observations at buoy NDBC 46214 were used to build LUTs for CSTs #8067-9172 (within Marin and Sonoma counties), NDBC 46014 for CSTs #9173-10225 (Mendocino County), NDBC 46213 for CSTs #10226-1121 (Humboldt County), and NDBC 46027 for CSTs #11222-11594 (Del Norte County). Significant wave heights were binned from 0.5-10.25 m at 0.25 m intervals; peak wave periods from 3-24 s at 3 s intervals; peak wave directions from 5-360 degrees at 5 degree intervals; and wind speeds from 0-24 m/s at 6 m/s intervals. Interval sizes for H_s and T_p were based on the average RMSE for each variable. For each combination of deep-water H_s, T_p, D_p, and U, time indices falling into each bin were identified. For each nearshore location, median H_s, T_p, T_m, D_p, and D_m corresponding to all time indices of a given set of deep-water binned conditions were computed to complete the lookup table. Because swell travel time from offshore to nearshore is on the order of 1.5 hours (assuming an average depth of 100 m and T_p = 15 s over a distance of approximately 120 km) and the model outputs are at three-hourly intervals, no time lag is assumed between deep-water and nearshore conditions. The LUTs, developed from the Jan 2000 through 2017 wave observation and CDIP data, were then used to generate bulk wave statistic time-series at each of the CSTs for the 2018-2100 time period, and an additional hindcast period (1979-1999) for a longer historical period. Wave model data for projected waves and winds from GCM data, representing the GFDL-ESM2M RCP4.5 climate scenario (Dunne and others, 2012), were used in conjunction with the LUT as described by Erikson and others (2018). Wave statistic time series for the historical period were also generated using information from historical periods of GCM data. Data sources used in this process:
    • CST
    • CDIP
    • wave model data
    Data sources produced in this process:
    • LUT relating nearshore conditions to deep-water wave conditions
    • Bulk wave statistic time-series
    Date: 20-Feb-2021 (process 2 of 4)
    An estimation of the storm surge (SS) term for TWL (equation 3 in Erikson and others, 2018) was generated using wind and pressure data from GCM data and previously generated wave statistic time series. Coefficients were found by obtaining least-square residual best fits for data spanning 1979 through Dec 2017 of overlapping nearshore hindcast data and tide gauge water level measurements at regional tide stations (San Francisco, Point Reyes, Arena Cove, North Spit, and Crescent City). The coefficients for each tide station ranged as follows: -0.0139 - 0.1321 for C0, 0.1019 - 0.1865 for C1, and 1.3 - 1.9 for C2. An SS time series was generated for each tide station, and nearshore SS at each CST was interpolated between the stations (at each time step), owing to the variation in shelf width and coastal exposure at the tide stations compared to study region in Erikson and others (2018). Data sources used in this process:
    • Bulk wave statistic time-series
    • GCM data
    • water level measurements
    • CST
    Data sources produced in this process:
    • SS estimation time-series for each CST
    Date: 01-Mar-2021 (process 3 of 4)
    Sea level anomaly (SLA) time-series were derived from observations for the hindcast period and from a simple linear model that used sea-surface temperature anomalies (SSTA) as the independent variable for the future period (2017-2100). For the hindcast time-period, monthly mean sea levels from NOAA were prepared with the average seasonal cycle and linear trends removed, for all tide stations listed in the previous step. The coefficients (equation 4, in Erikson and others, 2018) were developed from mean SSTA observations at the San Francisco buoy (NDBC 46237); SSTAs were computed by subtracting out the seasonal signal and long-term means (1971-2000; Reynolds and others, 2002) from the satellite-derived SST time-series for 1981-2014. Coefficients C0 and C1 were found to equal 0.033758 and 0.095694, respectively, by a least squares linear fit through the upper envelope of the mean monthly SSTA and SLA. The upper envelope SLA was defined by the maximum SLA within 0.25-degree SSTA bins from -3.0 deg C to +3.0 deg C. A fit through the upper envelope, rather than all the data, errs conservatively high by assuring a positive SLA for higher SSTAs. Due to scatter in the data and relatively small SLAs, a fit through all the data would yield only a slight positive SLA for the maximum observed SSTA, which is well below observed extremes. As in the previous step, SLA time series were generated at each tide station using the determined coefficients and GCM data, and values were interpolated alongshore at CSTs at each time step. Data sources used in this process:
    • satellite-derived SST
    • GCM data
    • water level measurements
    • CST
    Data sources produced in this process:
    • SLA estimation time-series for each CST
    Date: 23-Mar-2021 (process 4 of 4)
    An estimation of runup (R2%; equation 2 in Erikson and others, 2018) was made for the study region using the nearshore wave statistics at each CST. A representative slope of 0.066 was used for Northern California. TWL proxies were generated for each CST by summing all SS, SLA and R2% contributions, per equation 1 in Erikson and others (2018). These proxy values can functionally be referenced as elevations above Mean Sea Level (MSL). Data were prepared for packaging into NetCDF format. Proxy values are identified and organized by CST location. Time units are in decimal days since January 01, 1900. See NetCDF attributes for variables for more information. Data sources used in this process:
    • Bulk wave statistic time-series
    • SS estimation time-series for each CST
    • SLA estimation time-series for each CST
    • CST
  3. What similar or related data should the user be aware of?
    Erikson, L.H., Espejo, A., Barnard, P.L., Serafin, K.A., Hegermiller, C.A., O'Neill, A.C., Ruggiero, P., Limber, P.W., and Mendez, F.J., 2018, Identification of storm events and contiguous coastal sections for deterministic modeling of extreme coastal flood events in response to climate change.

    Other_Citation_Details:
    Erikson, L.H., Espejo, A., Barnard, P.L., Serafin, K.A., Hegermiller, C.A., O'Neill, A.C., Ruggiero, P., Limber, P.W., and Mendez, F.J., 2018, Identification of storm events and contiguous coastal sections for deterministic modeling of extreme coastal flood events in response to climate change: Coastal Engineering, v. 140, p. 316-330, https://doi.org/10.1016/j.coastaleng.2018.08.003.
    Reynolds, R.W., Rayner, N.A., Smith, T.M., Stokes, D.C., and Wang, W., 2002, An improved in situ and satellite SST analysis for climate.

    Other_Citation_Details:
    Reynolds, R.W., Rayner, N.A., Smith, T.A., Stokes, D.C. and Wang, W., 2002, An improved in situ and satellite SST analysis for climate: Journal of Climate, v. 15, p. 1609-1625.
    Dunne, J.P., John, J.G., Adcroft, A.J., Griffies, S.M., Hallberg, R.W., Shevliakova, E., Stouffer, R.J., Cooke, W., Dunne, K.A., Harriso, M.J., Krasting, J.P., Malyshev, S.L., Milly, P.C.D., Phillips, P.J., Sentman, L.T., Samuels, B.L., Spelman, M.J., Winton, M., Wittenberg, A.T., and Zadeh, N., 2012, GFDL’s ESM2 Global Coupled Climate-Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics.

    Other_Citation_Details:
    Dunne, J.P., John, J.G., Adcroft, A.J., Griffies, S.M., Hallberg, R.W., Shevliakova, E., Stouffer, R.J., Cooke, W., Dunne, K.A., Harrison, M.J., Krasting, J.P., Malyshev, S.L., Milly, P.C.D., Phillipps, P.J., Sentman, L.T., Samuels, B. L., Spelman, M. J., Winton, M., Wittenberg, A. T., and Zadeh, N., 2012, GFDL’s ESM2 Global Coupled Climate-Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics: Journal of Climate, v. 25, p. 6646-6665, https://doi.org/10.1175/JCLI-D-11-00560.1.

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 proxies of nearshore total water level due to plausible future storm conditions and therefore cannot be validated against observations. The projections were generated using climate projections for California. See Erikson and others (2018) for more information.
  2. How accurate are the geographic locations?
    Data are concurrent with referenced cross-shore transect locations.
  3. How accurate are the heights or depths?
    Model-derived data are accurate within the limitations outlined in Erikson and others (2018).
  4. Where are the gaps in the data? What is missing?
    Dataset is considered complete for the information presented. 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 QA/QC and fall within expected/reasonable ranges.

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.
  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
    USA

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set?
  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?

Who wrote the metadata?

Dates:
Last modified: 12-Oct-2022
Metadata author:
U.S. Geological Survey, Pacific Coastal and Marine Science Center
Attn: PCMSC Science Data Coordinator
2885 Mission Street
Santa Cruz, CA
US

831-460-4747 (voice)
831-427-4748 (FAX)
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_P9048D1S/NorthernCalifornia_TWLproxies_2018-2100_metadata.faq.html>
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