Andrea C. O'Neill
Li H. Erikson
Patrick L. Barnard
20221012
Nearshore total water level (TWL) proxies (2018-2100) for Northern California
Nearshore proxies for total water level (TWL) data in NetCDF format
data release
DOI:10.5066/P9048D1S
Pacific Coastal and Marine Science Center Santa Cruz, California
U.S. Geological Survey
https://doi.org/10.5066/P9048D1S
https://www.sciencebase.gov/catalog/item/62ec48e0d34eacf539724fd0
Patrick L. Barnard
Li H. Erikson
Amy C. Foxgrover
Patrick W. Limber
Andrea C. O'Neill
Jenny Thomas
Sean Vitousek
2022
Coastal Storm Modeling System (CoSMoS) for northern California 3.2
modeling results presented in various formats
data release
DOI:10.5066/P9048D1S
Pacific Coastal and Marine Science Center, Santa Cruz, California
U.S. Geological Survey
https://doi.org/10.5066/P9048D1S
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.
These data are intended for science researchers and technical users.
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
202210
year and month of publication
None planned
-124.4448
-122.4774
41.9985
37.8921
Global Change Master Directory (GCMD)
Hazards Planning
Ocean Waves
Ocean Winds
Beaches
Erosion
Storm Surge
Extreme Weather
Floods
Water Depth
USGS Thesaurus
Climate Change
Storms
Wind
Floods
mathematical modeling
effects of climate change
earth sciences
ISO 19115 Topic Category
Oceans
ClimatologyMeteorologyAtmosphere
Marine Realms Information Bank (MRIB) Keywords
waves
None
U.S. Geological Survey
USGS
Coastal and Marine Hazards and Resources Program
CMHRP
Pacific Coastal and Marine Science Center
PCMSC
USGS Metadata Identifier
USGS:62ec48e0d34eacf539724fd0
Geographic Names Information System
State of California
None
Northern California
Northern California Coast
none
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.
U.S. Geological Survey, Pacific Coastal and Marine Science Center
PCMSC Science Data Coordinator
mailing and physical
2885 Mission Street
Santa Cruz
CA
95060-5792
US
831-460-4747
831-427-4748
pcmsc_data@usgs.gov
This project was funded by U.S. Geological Survey.
Erikson, L.H.
Espejo, A.
Barnard, P.L.
Serafin, K.A.
Hegermiller, C.A.
O'Neill, A.C.
Ruggiero, P.
Limber, P.W.
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
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.
Wang, W.
2002
An improved in situ and satellite SST analysis for climate
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.
Zadeh, N.
2012
GFDL’s ESM2 Global Coupled Climate-Carbon Earth System Models. Part I: Physical Formulation and Baseline Simulation Characteristics
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.
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.
Data have undergone QA/QC and fall within expected/reasonable ranges.
Dataset is considered complete for the information presented. Users are advised to read the metadata record and cited references carefully for additional details.
Data are concurrent with referenced cross-shore transect locations.
Model-derived data are accurate within the limitations outlined in Erikson and others (2018).
Pierce, D.W.
Cayan, D.R.
Thrasher, B.L.
2015
LOCA Statistical Downscaling (Localized Constructed Analogs)
online
Scripps Institute of Oceanography, University of California, San Diego, California
http://loca.ucsd.edu/
online database
2015
publication date
GCM data
statistically downscaled Global Climate Model (GCM) data for California
Erikson, L.H.
Storlazzi, C.D.
Barnard, P.L.
Hegermiller, C.E.
Shope, J.B.
2016
Wave and wind projections for United States Coasts; Mainland, Pacific Islands, and United States-Affiliated Pacific Islands
online
U.S. Geological Survey
https://doi.org/10.5066/F72B8W3T
http://cmgwindwave.usgsportals.net/
online dataset
2016
publication date
wave model data
historical and future wave conditions generated from WaveWatch III simulations of Global Climate Models
National Data Buoy Center (NDBC)
2019
Historical data for various buoy stations
online
National Oceanic and Atmospheric Administration (NOAA)
https://www.ndbc.noaa.gov/
online database
2019
date of access
buoy observations
ocean wave and meteorological observation records at buoy stations
National Oceanic and Atmospheric Administration (NOAA)
2019
NOAA Water Level Information for Tide Stations
online
National Oceanic and Atmospheric Administration (NOAA)
https://tidesandcurrents.noaa.gov/stations.html?type=Water+Levels
online database
2019
date of access
water level measurements
water level measurements at various tide stations for determination of storm surge estimation parameters
National Oceanic and Atmospheric Administration (NOAA) Physical Sciences Laboratory
2019
NOAA High-resolution Blended Analysis of Daily SST
online
National Oceanic and Atmospheric Administration (NOAA)
https://psl.noaa.gov/data/gridded/data.noaa.oisst.v2.highres.html
online dataset
2019
date of access
satellite-derived SST
sea surface temperature data used for determination of sea level anomoly parameters
Coastal Data Information Program (CDIP))
2019
MOP v1.1 model output
online
Scripps Institute of Oceanography, University of California, San Diego, California
http://cdip.ucsd.edu/MOP_v1.1/
online database
2019
date of access
CDIP
nearshore wave information used for determination of estimated runup parameters
Foxgrover, A.C.
Erikson, L.H.
O'Neill, A.C.
2022
Northern California cross-shore transects used in CoSMoS 3.2
online
U.S. Geological Survey
https://doi.org/10.5066/P9048D1S
online dataset
2022
publication date
CSTs
cross-short transects (CSTs) used throughout study region for model setup
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.
CST
CDIP
wave model data
20190819
LUT relating nearshore conditions to deep-water wave conditions
Bulk wave statistic time-series
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).
Bulk wave statistic time-series
GCM data
water level measurements
CST
20210220
SS estimation time-series for each CST
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.
satellite-derived SST
GCM data
water level measurements
CST
20210301
SLA estimation time-series for each CST
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.
Bulk wave statistic time-series
SS estimation time-series for each CST
SLA estimation time-series for each CST
CST
20210323
data represented at user-defined cross-shore transects throughout study region
Universal Transverse Mercator
10
0.999600
-100.000000
0.000000
500000.000000
0.000000
row and column
2.000000
2.000000
meters
North American Datum 1983 (NSRS2007)
Geodetic Reference System 80
6378137.000000
298.257222
Mean sea level
.01
meters
Attribute values
NetCDF files are self-contained and attribute information may be found in the header of the file itself and with each variable.
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.
U.S. Geological Survey - ScienceBase
Mailing and Physical Address
Denver Federal Center, Building 810, Mail Stop 302
Denver
CO
80225
USA
1-888-275-8747
sciencebase@usgs.gov
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.
NetCDF
MATLAB R2016b
File contains TWL proxy time-series data in NetCDF format for the period of 2018-2100
No compression applied
3300
https://www.sciencebase.gov/catalog/file/get/62ec48e0d34eacf539724fd0?name=NorthernCalifornia_TWL_proxies_2018-2100.nc
https://www.sciencebase.gov/catalog/file/item/62ec48e0d34eacf539724fd0
https://doi.org/10.5066/P9048D1S
https://www.sciencebase.gov/catalog/item/5633fea2e4b048076347f1cf
Data can be downloaded using the Network_Resource_Name links. The first link is a direct link to download the data file. The second link points to the page with the data and metadata. The third link points to the landing page for all of the CoSMoS Northern California 3.2 data. The third link points to the community landing page for the entire CoSMoS project.
None
20221012
U.S. Geological Survey, Pacific Coastal and Marine Science Center
PCMSC Science Data Coordinator
mailing and physical
2885 Mission Street
Santa Cruz
CA
95060-5792
US
831-460-4747
831-427-4748
pcmsc_data@usgs.gov
Content Standard for Digital Geospatial Metadata
FGDC-STD-001-1998