Projections of shoreline change for California due to 21st century sea-level rise

Metadata also available as - [Outline] - [Parseable text] - [XML]

Frequently anticipated questions:


What does this data set describe?

Title:
Projections of shoreline change for California due to 21st century sea-level rise
Abstract:
This dataset contains projections of shoreline change and uncertainty bands across California for future scenarios of sea-level rise (SLR). Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations across the state. Scenarios include 25, 50, 75, 100, 125, 150, 175, 200, 250, 300 and 500 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2000.
This model shows change in shoreline positions along pre-determined cross-shore transects, considering sea level, wave conditions, along-shore/cross-shore sediment transport, long-term trends due to sediment supply, and estimated variability due to unresolved processes (as described in Vitousek and others, 2021). Variability associated with complex coastal processes (for example, beach cusps/undulations and shore-attached sandbars) are included via a noise parameter in a model, which is tuned using observations of shoreline change at each transect and run in an ensemble of 200 simulations; this approach allows for a representation of statistical variability in a model that is assimilated with sequences of noisy observations. The model synthesizes and improves upon numerous, well-established shoreline models in the scientific literature; processes and methods are described in this metadata (see lineage and process steps), but also described in more detail in Vitousek and others 2017, 2021, and 2023. Output includes different cases covering important model behaviors (cases are described in process steps of this metadata). KMZ data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D features or terrain. For technical users and researchers, shapefile and KMZ data can be ingested into geographic information system (GIS) software such as Global Mapper or QGIS.
Supplemental_Information:
This work is one portion of ongoing modeling efforts for California and the western United States. This data release was funded by the Coastal and Marine Hazards and Resources Program. For more information on CoSMoS implementation, see https://walrus.wr.usgs.gov/coastal_processes/cosmos/. Any use of trade, product, or firm names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
  1. How might this data set be cited?
    Vitousek, Sean F., Vos, Kilian, Splinter, Kristen D., O'Neill, Andrea C., Foxgrover, Amy C., Hayden, Maya K., Barnard, Patrick L., and Erikson, Li H., 20230601, Projections of shoreline change for California due to 21st century sea-level rise: data release DOI:10.5066/P9CJMB2H, 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: -117.0655
    North_Bounding_Coordinate: 41.9985
    South_Bounding_Coordinate: 32.5343
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Calendar_Date: 2023
    Currentness_Reference:
    year of publication
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form:
    Shoreline change projections in Google Earth KMZ and shapefile formats
  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 (4600600)
    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 0.001. Longitudes are given to the nearest 0.001. Latitude and longitude values are specified in Decimal degrees. The horizontal datum used is World Geodetic System 1984 (WGS84).
      The ellipsoid used is WGS84.
      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?
    Projections of shoreline change position, uncertainty, and model parameters for hindcast period (1995-2021) and for future 21st Century (2021-2100) due to sea-level rise
    KMZ files consist of shoreline position changes caused by wave impacts in different named sea-level rise scenarios and different model case options. Initial shorelines (from automatically detected shorelines at the start of the model run) are included to show trends in shoreline change. Model parameters for each transect model are listed with the respective transect. Shoreline model uncertainty (determined from the rigorous 200-member ensemble) and unresolved process uncertainty are also included. All data presented in KMZ files are also presented in shapefile format, as described below. (Source: producer defined)
    initial shoreline
    Part of model initial conditions. Position of initial shoreline used at start of simulation. Determined from source satellite-derived shoreline data on 02 January 1995. The position was automatically detected from imagery and should not be used in place of definitive historical shoreline records but is meant to provide comparisons for projected trends in shoreline change. (Source: producer defined) geographic position of the initial shoreline condition used in the models, selected from a single shoreline observation circa the model start time (Jan 1st, 1995).
    modeled shoreline
    Position of shoreline, defined as the land-water boundary at Mean Sea Level (MSL) for the indicated SLR scenario. This represents the median trajectory of a 200-member ensemble using multiple realizations of future wave conditions. As the models use the change in sea level over time during computation, final shoreline positions for SLR scenarios are identified for the date indicated. (Source: producer defined) modeled shoreline position for the SLR indicated
    modeled shoreline uncertainty
    Modeled uncertainty for projected shoreline position for the given SLR scenario, defined as the upper and lower bounds of a 95 percent confidence interval for the projected 200-member ensemble trajectories. The uncertainty is representative of long-term, seasonal, and daily shoreline change. In model cases where a landward model boundary was imposed (cases 1 and 2), uncertainty does not extend past the model boundary. (Source: producer defined) modeled uncertainty of shoreline change determined at 95 percent confidence interval for SLR indicated
    potential storm erosion uncertainty
    Illustrates the potential additional impact of extreme storms on episodic shoreline change for full model and cross-shore transects. It is defined from an extreme value analysis of modeled shoreline positions from the 200-member ensemble based on the return-period/intensity indicated. Thus, it captures extreme potential erosion events from the model ensemble. For other model configurations (for example, “rate only”), see process steps for more info. (Source: producer defined) uncertainty in modeled shoreline position considering potential storm erosion for SLR and storm intensity for given return period indicated
    shoreline change hazard zone
    Zone impacted by projected shoreline change for given SLR. This zone includes area between initial shoreline, projected shoreline and associated model uncertainty. (Source: producer defined) shoreline change hazard zone for SLR indicated
    extreme storm hazard zone
    Zone impacted by projected shoreline change and extreme storms for given SLR. This zone includes area between initial shoreline, projected shoreline and potential storm erosion uncertainty. (Source: producer defined) shoreline change hazard zone including extreme storm impact for SLR indicated
    unresolved process uncertainty
    Uncertainty considering unresolved processes and other sources of error not explicitly included in the model, which captures the larger uncertainty in model projections for dynamic and complex areas such as river mouths, capes, inlets, and the ends of spits. Estimates of the so-called unresolved process uncertainty are derived from comparing model output with Satellite-Derived Shoreline (SDS) observations during the validation period (2015-2020). Unresolved process uncertainty is shown for SLR scenarios 25 cm and greater and where validation data were present. For model configurations other than “full” (for example, “rate only”), see process steps and citations for more information. (Source: producer defined) uncertainty of shoreline change projection considering unresolved processes and other sources of error
    Trans_ID
    Identification of individual transects for each model location, unique across study area (Source: producer defined)
    Range of values
    Minimum:1
    Maximum:11594
    Units:NA
    Resolution:1
    ShrType
    Shoreline Type used in the transect model, defined as “full model”, “cross-shore only” (longshore transport is excluded in locations where applicable, such as when the beach was very short or enclosed as a “pocket beach”, or there was curvature in the shoreline), “rate only” (only historical rates of shoreline change are used because the model’s governing equations would not be appropriately descriptive for response of the beach’s sediment type, for example with cobbled beaches), or "cliff only" (no shoreline model or projection at this location) (Source: producer defined)
    Formal codeset
    Codeset Name:full model, cross-shore only, rate only, or cliff only
    Codeset Source:producer defined
    ChgRate
    Shoreline change rate determined from a linear regression fit to all available shoreline data (Source: producer defined)
    Range of values
    Minimum:-10.4
    Maximum:4.706
    Units:meters per year
    Resolution:0.001
    v_lt_assim
    Long-term shoreline change rate, which is assimilated during the model hindcast period (1995-2015) (Source: producer defined)
    Range of values
    Minimum:-3.637
    Maximum:1.486
    Units:meters per year
    Resolution:0.001
    v_lt_proj
    Long-term shoreline change rate for projection period (2020-2100) (Source: producer defined)
    Range of values
    Minimum:-3.637
    Maximum:1.486
    Units:meters per year
    Resolution:0.001
    DT_days
    The equilibrium time scale parameter, as defined in Vitousek and others (2021; 2023), assimilated during the model hindcast period (1995-2015) (Source: producer defined)
    Range of values
    Minimum:14.42
    Maximum:110.9
    Units:days
    Resolution:0.01
    DY_m
    The equilibrium shoreline excursion scale parameter, as defined in Vitousek and others (2021; 2023), which is assimilated during the model hindcast period (1995-2015) (Source: producer defined)
    Range of values
    Minimum:4.55
    Maximum:26.42
    Units:meters
    Resolution:0.01
    Hsb
    The background wave height parameter, as defined in Vitousek and others (2021; 2023) (Source: producer defined)
    Range of values
    Minimum:0.1024
    Maximum:3.089
    Units:meters
    Resolution:0.0001
    c_BrunnCo
    Bruun coefficient, assimilated during model hindcast period (1995-2015) (Source: producer defined)
    Range of values
    Minimum:0.7336
    Maximum:1.443
    Units:NA
    Resolution:0.0001
    K_LongShrT
    longshore transport coefficient determined from model hindcast period (1995-2015) (Source: producer defined)
    Range of values
    Minimum:0.0154
    Maximum:699.9
    Units:NA
    Resolution:0.0001
    sigma
    Gaussian noise parameter (see Vitousek and others, 2017; 2021) determined from model hindcast period (1995-2015) (Source: producer defined)
    Range of values
    Minimum:0.2254
    Maximum:0.2739
    Units:m
    Resolution:0.0001
    SLRcm
    SLR in centimeters referenced for the projection (Source: producer defined)
    Range of values
    Minimum:0
    Maximum:500
    Units:cm
    Resolution:25
    Initial shoreline position used for models
    Shapefile consist of the initial shoreline (CA_initial_shoreline) used for modeling (Source: producer defined)
    Projections of final shoreline change position for future 21st Century (2021-2100) due to sea-level rise.
    Shapefiles consist of final shoreline change projection lines (CA_[case number]_final_shorelines) determined at model transects caused by wave impacts in different named sea-level rise (SLR) scenarios and different model cases. Model case options illustrate impacts of the extent or boundary of the beach (that is, where parameters derived from observed shoreline movement may or may not remain valid over long periods of time), and parameters accounting for shoreline accretion. (Source: producer defined)
    SLR_cm
    amount of sea-level rise (SLR) for projected shoreline change (25 cm or greater) (Source: producer defined)
    Range of values
    Minimum:0
    Maximum:500
    Units:cm
    Resolution:25
    Shoreline change projection uncertainty.
    Shapefiles consist of model uncertainty polygons (CA_[case number]_model_uncertainty, CA_[case number]_unresolved_process_uncertainty, and CA_[case number]_potential_storm_erosion_uncertainty) and hazard zones (CA_[case number]_extreme_storm_hazard_zone and CA_[case number]_unresolved_process_uncertainty) in different named sea-level rise (SLR) scenarios and different model cases. (Source: producer defined)
    SLR_cm
    amount of sea-level rise (SLR) for projected shoreline change (25 cm or greater) (Source: producer defined)
    Range of values
    Minimum:0
    Maximum:500
    Units:cm
    Resolution:25
    Landward model boundary positions
    Shapefile consists of points showing placement of landward extent of the beach used in the model simulations along this transect (CA_landward_model_boundary) (Source: producer defined)
    Trans_ID
    Identification of individual transects for each model location, unique across study area (Source: producer defined)
    Range of values
    Minimum:1
    Maximum:11594
    Units:NA
    Resolution:1
    Entity_and_Attribute_Overview:
    CoSMoS-COAST projections of shoreline position for 21st century sea-level rise. The KMZ and shapefile files consist of shoreline positions changes (displayed as lines, with vertices at defined transects) caused by wave impacts in different named sea-level rise scenarios and model case options. Landward model boundary locations for each transect are included, as well as uncertainty polygons illustrating uncertainty from the model, unresolved processes, and wintertime erosion around each projection line.
    Entity_and_Attribute_Detail_Citation:
    Shoreline change projections, uncertainty, and model parameters are generated and shown at transects (spaced approximately 100m-300m apart alongshore, identified in whole numbers) for California.

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Sean F. Vitousek
    • Kilian Vos
    • Kristen D. Splinter
    • Andrea C. O'Neill
    • Amy C. Foxgrover
    • Maya K. Hayden
    • Patrick L. Barnard
    • Li H. Erikson
  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, California
    USA

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

Why was the data set created?

These data provide an estimate of potential shoreline change in response to SLR during the 21st century, to help identify and assess possible areas of vulnerability. Uncertainty should be included in any assessment or analysis. Data are intended for policy makers, resource managers, science researchers, and students. These data can be used with geographic information systems or other software to help identify and assess possible areas of 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?
    Aerial imagery (source 1 of 7)
    Earth, Google, 2020, Aerial imagery for California: Google Earth, online.

    Online Links:

    Type_of_Source_Media: online viewer
    Source_Contribution:
    Recent aerial imagery accessed through Google Earth was used to delineate sandy beach areas of focus for the study.
    SDS (source 2 of 7)
    Vos, K., 2020, Time-series of shoreline change along the Pacific Rim: University of New South Wales, Sydney, Australia, online.

    Online Links:

    Type_of_Source_Media: online data viewer
    Source_Contribution:
    Satellite-derived shoreline time series used to calibrate CoSMoS-COAST model parameters at all transects available
    Future wave data (source 3 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
    Source_Contribution:
    Wave data derived from global climate models used for CoSMoS-COAST model future projections.
    NOAA tide stations (source 4 of 7)
    National Oceanic and Atmospheric Administration (NOAA), 2021, NOAA water level stations: NOAA, online.

    Online Links:

    Type_of_Source_Media: online
    Source_Contribution: adjusting SLR projections for models
    CSTs (source 5 of 7)
    Foxgrover, A.C., Erikson, L.H., and O'Neill, A.C., 2022, Northern California cross-shore transects for 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 locations
    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:
    defined alongshore model-output site locations and coincident nearshore wave data for hindcast validation
    nearshore wave forcing (source 7 of 7)
    O'Neill, A.C., Erikson, L.H., and Barnard, P.L., 2022, Nearshore total water level (TWL) proxies (2018-2100) for Northern California: U.S. Geological Survey, online.

    Online Links:

    Type_of_Source_Media: online dataset
    Source_Contribution: time series of nearshore wave impacts for future projections
  2. How were the data generated, processed, and modified?
    Date: 29-Oct-2021 (process 1 of 5)
    Set up model structure as discussed in Vitousek and others (2017, 2021, and 2023) for the California coast. The structure included defining cross-shore transect locations, from CSTs and CDIP, as locations for the one-dimensional numerical models simulating the shoreline position given changing sea level and wave forcing. The assimilation scheme outlined in Vitousek and others (2023; 2021, section 2.3), was set up to use an ensemble of 200 members (Nens = 200). The model was set up to assimilate SDS observations and calibrate itself for a majority portion of the historical data period (1995-2015), reserving 5 years of SDS observations (2015-2020) for validation of the model. Although the uncertainty in the SDS positions (with RMS error of 10 m) is generally larger than traditional surveys (RMS error of centimeters to meters), the larger observational uncertainty is handled within the context of the Kalman filter.
    A representation of the landward boundary of the beach was hand-digitized from aerial imagery in Google Earth. This boundary was used in models at individual transects to classify the landward end of the sandy beach. This sandy beach limit separates the beach from other landscapes, such as vegetated or urban landscapes, in model simulations (representing a coarse categorization of sandy versus not-sandy beach). This limit was digitized from the most recent, cloud-free imagery available at the time of digitization (between October 2014 and March 2020 across the region). Imagery was viewed in Google Earth at a minimum 1:300 scale, and digitized at an average horizontal vertex spacing of 10-20 m. The landward edge or boundary of the sandy beach was visually identified in the imagery using several criteria, dependent on the landscape, by the presence of infrastructure or buildings; changes in vegetation; or established dune systems. If multiple criteria were present, the feature encountered first (as the landward boundary of the beach) was generally used. Data sources used in this process:
    • Aerial imagery
    • CSTs
    • CDIP
    Data sources produced in this process:
    • model transects and landward model boundaries
    Date: 20-Jul-2021 (process 2 of 5)
    Obtained SDS observations from all available satellite imagery in the study area (Vos and others, 2019a; 2019b, 2020), at model transect locations. Data for transects along sections of beach adjacent to river mouths and inlets should be understood to be highly dynamic and include more uncertainty. Data sources used in this process:
    • SDS
    • model transects and landward model boundaries
    Data sources produced in this process:
    • historical SDS observations at model transects
    Date: 30-Nov-2022 (process 3 of 5)
    Shoreline change models were run within Matlab to correct SDS observations for synoptic wave setup as predicted with empirical runup equations as described in Vitousek and others (2021; 2023). The model was run sequentially for three periods: a hindcast/calibration period (1995-2015), a validation period (2015-2020), and projection period (2020-2100). The hindcast period serves as the calibration period, assimilating available data including SDS observations, to automatically-tune and optimize parameters at every transect. The model was started on 01 Jan 1995, using a SDS for the initial condition. This shoreline can appear ‘spikey’, as it is only derived and shown at discrete transect locations. In rare circumstances, the initial shoreline comes from observations at two different instances in time on neighboring transects, which can also lead to spikiness. Any uncertainty in both modeled and observed shoreline position is accounted for and adjusted in the Kalman filter for subsequent time steps, while refining model parameters (see Vitousek and others, 2021; 2023).
    Depending on location and availability of SDS data, transects were run in 3 configurations: “full model” configuration included all model parameters; “cross-shore only” configuration excluded longshore transport in locations where this was applicable (for example, when the beach was short or enclosed, or if there was too much curvature on the shoreline for the long-shore transport term to be resolved); and a “rate only” configuration shows where only historical rates of shoreline change are used (usually due to limited SDS data). Note that the model was not run in areas without beach and these are labeled as "cliff only" transects.
    As the models are run in an ensemble, uncertainty was defined as 95 percent confidence intervals determined by the band that enclosed the middle 95 percent of model trajectories in the ensemble illustrating impacts from variable projected wave conditions (see Vitousek and others, 2023). This uncertainty encapsulates long-term changes as well as episodic changes and reflects decades of data (Vitousek and others, 2023). However, shoreline changes and erosion from extreme storms can lie outside this band of trajectories, and so to illustrate the potential impact of extreme storms, the maximum landward ensemble trajectory for wave heights of certain intensities (return periods of 1-year, 20-year and 100-years, representative of extreme coastal storm impacts) are also provided. For locations where “rate only” model configurations were used, episodic changes are not projected, and potential storm erosion uncertainty is not available.
    The impact of large historical events may affect the model output in certain locations; in highly dynamic regions that have experienced large episodic shoreline change (such as near headlands or river mouths), SDS may have higher uncertainty as well as model projections.
    Data were assimilated during the validation period (2015-2020). While several processes are implicitly included with each location, the model does not explicitly account for all coastal processes. In dynamic areas including around river mouths, capes, inlets, and at the end of spits, uncertainty is greater. An estimate of this potential uncertainty due to unresolved processes was derived from comparing shoreline predictions for this period to observations. This comparison showed an RMS error of less than 15 m most of the study area (on the order of SDS positional error), with higher values in the dynamic areas mentioned above (Vitousek and others, 2023). The confidence bands of the unresolved process uncertainty are based on 2x the root-mean-square error of the un-assimilated model versus observations during this validation period. This unresolved process uncertainty is separate from and not mathematically additive to model uncertainty. Unresolved process uncertainty is not available at locations that do not have enough data for validation (for example, “rate only” and some “cross-shore only” transects).
    To run any shoreline model, as a simplified representation of shoreline evolution, certain assumptions about the behavior of the model need to be made, since the effect of these assumptions over long projection periods can lead to different outcomes (Vitousek and others, 2017; 2021; 2023). To explore the importance and impact of certain key model assumptions, the model was run for different cases representing end-members of model behavior bracketing a spectrum of possible solutions. Key aspects of model behavioral assumptions were investigated in combination: the extent or boundary of the beach (that is, where parameters derived from observed shoreline movement may or may not remain valid over long periods of time), and parameters accounting for shoreline accretion. For the first aspect, the shoreline model does not differentiate different landscapes in terms of shoreline evolution and erodibility. In natural settings, derived parameters from assimilated historical records may arguably hold (or be modified) for areas landward of the beach to include dunes and vegetated areas. But it is similarly arguable that parameters would not hold when encountering hardened infrastructure. Therefore, the model was run for two different cases to show solutions bracketing this behavioral assumption: 1) allowing the shoreline to evolve/erode without impediment/constraint as determined by its historical behavior or 2) limiting the shoreline erosion to the landward end of the modern-day beach. Similarly, modern-day, long-term, cross-shore shoreline change rates (particularly for developed, accreting beaches) may be reflective of human intervention/nourishments, and it is exceedingly difficult to project how interventions/nourishments may progress and/or persist in the future. Therefore, the model was run for two different cases to show end-member solutions of cross-shore accretion (and possibly reflective of generalized coastal management options) in the future: 1) retaining the model-derived residual long-term shoreline change rate (Vitousek and others, 2017; 2021; 2023) for future projections and 2) suppressing the residual shoreline trend by setting this parameter to 0 when it is estimated to be positive (accretionary). The latter case only suppresses the residual trend of the process; it does not affect accretion or erosion due to longshore transport: accretion (and erosion) due to alongshore sediment transport are still reflected in the modeling results. These different end-member solutions are combined for four different model cases. In model cases where a landward model boundary is imposed, model shoreline uncertainty is not shown landward of the boundary; however, potential storm erosion uncertainty is still projected landward. Also note that in these cases when a model boundary is imposed, there are rare locations where the initial shoreline was located landward of the model boundary (occurred in dynamic areas, as landward boundary was digitized from imagery dated after initialization periods); in these locations, when the historical or modeled shoreline is landward of boundary, beach width was then 0 m, long-short transport was neglected, and the resultant projected shoreline was held at imposed model boundary. When historical or modeled shoreline was oceanward of model boundary, all model parameters (as defined per transect) were used and resultant projections are displayed normally.
    It is important to note that historical impacts of nourishment are captured in the SDS observations, and so impacts are implicitly included in the calibration and shoreline projections. However, as mentioned above, we provided no assumptions about the persistence or policy of this practice. Model parameters derived during the calibration period and projection periods are preserved and used without adjustment in those model cases.
    Projected SLR curves through 2100 (relative to 2000; Vitousek and others, 2023) are used in model scenarios for this study’s SLR scenarios of 1.0 m SLR and higher; final shoreline projections for these scenarios are taken at 2100. For SLR scenarios of 0.25 m – 0.75 m, final shoreline positions are taken at the corresponding dates for the target SLR scenario along a SLR projection of curve of 1.0 m by 2100. See Vitousek and others (2023) for details. For the hindcast period (1995-2015), SLR is extrapolated linearly backwards from 2000 to the beginning of the model (January 1995) based on a historical rate. Data sources used in this process:
    • model transects and landward model boundaries
    • historical SDS observations at model transects
    • NOAA tide stations
    • CDIP
    • Future wave data
    • nearshore wave forcing
    Date: 07-Dec-2022 (process 4 of 5)
    Checked all output to ensure quality results.
    Date: 30-Jan-2023 (process 5 of 5)
    Organized model projections into groups by model case, numbered thusly: In model case 1, shorelines are allowed to evolve and erode without limitation/impediment and long-term shoreline change rate parameters derived by the model are preserved with no adjustments; in model case 2, shorelines are not allowed to evolve and erode past current boundaries and change rate parameters are preserved; in model case 3, shorelines are allowed to erode without limitation while cross-shore residual long-term shoreline change rates are set to 0; and in model case 4, shorelines are not allowed to erode past current boundaries and cross-shore residual accretionary change rates are set to 0. Projections are presented in both KMZ and shapefile formats. KMZs and shapefiles include the initial shoreline, landward model boundary locations (applied in model cases 1 and 2), final shoreline projections for SLR scenarios, model uncertainty (representing 95 percent of the ensemble model spread and robust model uncertainty). Files also include unresolved process uncertainty, as an estimate of uncertainty for unresolved processes and other sources of error not explicitly included in the model, as well as uncertainty with potential storm erosion, shoreline change hazard zones, and extreme storm hazard zones. KMZs also include transect information including all model parameters calibrated at each respective site. Null projection and model parameter values are listed as NaN.
    File names indicate state and model parameters; for example, ShorelineChange_projctn_CA_Case1.kmz contains shoreline projections in California for case 1 (no landward limitation to shoreline evolution, and no adjustments to model accretion parameters).
    For best display of results, it is recommended to turn off any 3D viewing.
  3. What similar or related data should the user be aware of?
    Vitousek, S., Barnard, P.L., Limber, P., Erikson, L.H., and Cole, B., 2017, A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change..

    Online Links:

    Other_Citation_Details:
    Vitousek, S., Barnard, P.L., Limber, P., Erikson, L.H., and Cole, B., 2017, A model integrating longshore and cross-shore processes for predicting long-term shoreline response to climate change: Journal of Geophysical Research--Earth Surface, v. 122, p. 782-806.
    Vitousek, S., Cagigal, L., Montano, J., Rueda, A., Mendez, F., Coco, G., and Barnard, P.L., 2021, The application of ensemble wave forcing to quantify uncertainty of shoreline change predictions.

    Online Links:

    Other_Citation_Details:
    Vitousek, S., Cagigal, L., Montaño, J., Rueda, A., Mendez, F., Coco, G., and Barnard, P.L., 2021, The application of ensemble wave forcing to quantify uncertainty of shoreline change predictions: Journal of Geophysical Research: Earth Surface, v. 126(7), e2019JF005506.
    Vitousek, S., Vos, K.D., Splinter, K.D., Erikson, L.H., and Barnard, P.L., 2023, A model integrating satellite-derived shoreline observations for predicting fine-scale shoreline response to waves and sea-level rise across large coastal regions.

    Online Links:

    Other_Citation_Details:
    Vitousek, S., Vos, K.D., Splinter, K.D., Erikson, L.H., and Barnard, P.L., 2023, A model integrating satellite-derived shoreline observations for predicting fine-scale shoreline response to waves and sea-level rise across large coastal regions: Journal of Geophysical Research-Earth Surface, v. 128, https://doi.org/10.1029/2022JF006936.
    Vos, K.D., Harley, M.D., Splinter, K.D., Simmons, J.A., and Turner, I.L., 2019, Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery.

    Online Links:

    Other_Citation_Details:
    Vos, K., Harley, M.D., Splinter, K.D., Simmons, J.A., and Turner, I.L., 2019a, Sub-annual to multi-decadal shoreline variability from publicly available satellite imagery; Coastal Engineering, v. 150, p. 160-174.
    Vos, K.D., Splinter, K.D., Harley, M.D., Simmons, J.A., and Turner, I.L., 2019, CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery.

    Online Links:

    Other_Citation_Details:
    Vos, K., Splinter, K.D., Harley, M.D., Simmons, J.A., and Turner, I.L., 2019b, CoastSat: A Google Earth Engine-enabled Python toolkit to extract shorelines from publicly available satellite imagery: Environmental Modelling and Software, v. 122, 104528.

How reliable are the data; what problems remain in the data set?

  1. How well have the observations been checked?
    Attribute values are projections of shoreline position at discrete transect locations due to plausible future sea-level rise scenarios in the future and therefore cannot be cross-checked with observations, because observations do not exist. A formal model accuracy assessment of the projections was conducted for each model output location, and model uncertainty is determined at the 95 percent confidence level.
    Model positional performance is validated for the period 2015-2020. Unresolved process uncertainty bands based on calculated uncertainty during this model evaluation period are included that also take seasonal variations in the shoreline as derived using the numerical model into account.
    While several complex coastal processes are explicitly and implicitly accounted for, the model’s estimate of uncertainty does not account for all coastal processes. In dynamic areas including around river mouths, capes, and end of spits, the model’s performance is often poorer and, hence, the uncertainty is often greater. An estimate of unresolved process uncertainty is included to account for the model accuracy (compared with shoreline observations) during a validation period (2015-2020), when such an assessment of accuracy is possible. The unresolved process uncertainty is comparable to the model’s reported uncertainty in most locations. However, in complex locations such as spits, capes, and river inlets, the unresolved process uncertainty is often much larger that the reported model uncertainty.
  2. How accurate are the geographic locations?
    Data are concurrent with specified transect locations.
  3. How accurate are the heights or depths?
    Not applicable because this dataset doesn’t serve any bathymetric or elevation data
  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 rest of the metadata record 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 (Vitousek and others, 2021; 2023; Vos and others, 2019a).

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? These data are available in shapefile and in KMZ formats. KMZ files are grouped by state and case (for example, ShorelineChange_projctn_CA_case1). Shapefiles are similarly grouped.
  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: 01-Jun-2023
Metadata author:
U.S. Geological Survey, Pacific Coastal and Marine Science Center
Attn: PCMSC Science Data Coordinator
2885 Mission Street
Santa Cruz, California
USA

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_P9CJMB2H/Shoreline_change_projections_withSLR_for_California_metadata.faq.html>
Generated by mp version 2.9.51 on Fri Jun 2 14:26:50 2023