Projections of shoreline change of current and future (2005-2100) sea-level rise scenarios for the U.S. Atlantic Coast

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What does this data set describe?

1. How might this data set be cited?
   Vitousek, Sean, Vos, Kilian, Barnard, Patrick L., O’Neill, Andrea C., Foxgrover, Amy C., and Hayden, Maya K., 20230315, Projections of shoreline change of current and future (2005-2100) sea-level rise scenarios for the U.S. Atlantic Coast: data release DOI:10.5066/10.5066/P9BQQTCI, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

   Online Links:

   • https://doi.org/10.5066/P9BQQTCI

   This is part of the following larger work.
   Barnard, Patrick L., Befus, Kevin M., Danielson, Jeffrey J., Engelstad, Anita C., Erikson, Li H., Foxgrover, Amy C., Hayden, Maya K., Hoover, Daniel J., Leijnse, Tim, Massey, Chris, McCall, Robert, Nadal-Caraballo, Norberto C., Nederhoff, Kees M., Ohenhen, Leonard, O’Neill, Andrea C., Parker, Kai A., Shirzaei, Manoocher, Su, Xin, Thomas, Jennifer A., Ormondt, Maarten van, Vitousek, Sean F., Vos, Kilian, and Yawn, Madison C., 2023, Future coastal hazards along the U.S. Atlantic coast: data release DOI:10.5066/P9BQQTCI, U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

   Online Links:

   • https://doi.org/10.5066/P9BQQTCI

2. What geographic area does the data set cover?

   West_Bounding_Coordinate: -82.11192
   East_Bounding_Coordinate: -75.32334
   North_Bounding_Coordinate: 38.81563
   South_Bounding_Coordinate: 26.06347
   

3. What does it look like?

   Shorelineprojections_US_Atlantic.png (PNG)

   Image map showing study area for shoreline change projections.

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 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 (14726880)
   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 0.000001. Longitudes are given to the nearest 0.000001. Latitude and longitude values are specified in Decimal Degrees. The horizontal datum used is North American Datum 1983.
      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: NAVD88
      Depth_Resolution: 2.0
      Depth_Distance_Units: meters
      Depth_Encoding_Method: Implicit coordinate
      

7. How does the data set describe geographic features?

   Projections of shoreline change position for hindcast period (1990-2021) and for future 21st Century (2021-2100) due to sea-level rise

   KMZs include projected shoreline position changes caused by wave impacts in different named sea-level rise scenarios and different model-case scenarios. 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. (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

   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:	34067
   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), or “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) (Source: Producer Defined)

   Formal codeset
   Codeset Name:	full model, cross-shore only, or rate 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:	-29.82
   Maximum:	18.42
   Units:	meters per year
   Resolution:	0.01

   v_lt_assim

   Long-term shoreline change rate, which is assimilated during the model hindcast period (1990-2015) (Source: Producer Defined)

   Range of values
   Minimum:	-3.97
   Maximum:	3.77
   Units:	meters per year
   Resolution:	0.01

   v_lt_proj

   Long-term shoreline change rate for projection period (2020-2100). (Source: Producer Defined)

   Range of values
   Minimum:	-5.0
   Maximum:	5.0
   Units:	meters per year
   Resolution:	0.01

   DT

   The equilibrium time scale parameter, as defined in Vitousek and others (2021; 2023), assimilated during the model hindcast period (1990-2015) (Source: Producer Defined)

   Range of values
   Minimum:	10.0
   Maximum:	120.0
   Units:	days
   Resolution:	0.01

   DY

   The equilibrium shoreline excursion scale parameter, as defined in Vitousek and others (2021; 2023), which is assimilated during the model hindcast period (1990-2015) (Source: Producer Defined)

   Range of values
   Minimum:	2.01
   Maximum:	30.0
   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:	0.01
   Maximum:	4.0
   Units:	m
   Resolution:	0.01

   c_BrunnCo

   Bruun coefficient, assimilated during model hindcast period (1990-2015) (Source: Producer Defined)

   Range of values
   Minimum:	0.01
   Maximum:	2.0
   Units:	N/A
   Resolution:	0.01

   K_LongShrT

   longshore transport coefficient determined from model hindcast period (1990-2015) (Source: Producer Defined)

   Range of values
   Minimum:	0.01
   Maximum:	700.0
   Units:	N/A
   Resolution:	0.01

   sigma

   Gaussian noise parameter (see Vitousek and others, 2017; 2021) determined from model hindcast period (1990-2015) (Source: Producer Defined)

   Range of values
   Minimum:	0.0001
   Maximum:	2.0
   Units:	m
   Resolution:	0.0001

   SLRcm

   SLR in centimeters referenced for the projection (Source: Producer Defined)

   Range of values
   Minimum:	0
   Maximum:	300
   Units:	cm
   Resolution:	25

   TrgSlope

   transgression slope used at location (Source: Producer Defined)

   Range of values
   Minimum:	0.0024
   Maximum:	0.1060
   Units:	N/A
   Resolution:	0.0001

   Entity_and_Attribute_Overview:

   KMZ files include projections of shoreline position changes caused by wave impacts in different named sea-level rise scenarios and different model-case scenarios. For each SLR scenario, shoreline projections illustrate shoreline position changes, model uncertainty bands, modeled uncertainty with potential storm erosion, and unresolved process uncertainty. Transect/model locations and model parameters at each location are also displayed. Initial shorelines (from automatically detected shorelines at the start of the model run) are included to show trends in shoreline change.

   Entity_and_Attribute_Detail_Citation:

   Shoreline projections and model uncertainty are determined at transects for U.S. Atlantic Coast; transects are spaced approximately 50 m apart alongshore and identified in whole sequential numbers, increasing to the north. Free parameters and coefficients for numerical models are tuned at each transect location to fit historical data; see Vitousek and others (2021; 2023) for details and mathematical definitions. Coefficients and data used to tune individual transect models are listed at each transect location.

Who produced the data set?

1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
   • Sean Vitousek
   • Kilian Vos
   • Patrick L. Barnard
   • Andrea C. O’Neill
   • Amy C. Foxgrover
   • Maya K. Hayden
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?

How was the data set created?

1. From what previous works were the data drawn?

   Aerial imagery (source 1 of 6)

   Earth, Google, 2020, Aerial imagery for U.S. Atlantic Coast: Google Earth, online.

   Online Links:

   • https://earth.google.com/

   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. This was the most recent cloud-free imagery at the time of access (between December 2016 and March 2020 across the region).

   SDS (source 2 of 6)

   Vitousek, S., Vos, K., and Barnard, P.L., 2023, Satellite-derived shorelines for the U.S. Atlantic Coast (1984-2021): U.S. Geological Survey, online.

   Online Links:

   • https://doi.org/10.5066/P9BQQTCI

   Type_of_Source_Media: online database
   Source_Contribution:

   Satellite-derived shorelines were used to calibrate CoSMoS-COAST model parameters at all transects available.

   WW3 (source 3 of 6)

   Erikson, Li, Herdman, Liv, Flanary, Chris, Engelstad, Anita, Pusuluri, Prasad, Barnard, Patrick, Storlazzi, Curt, Beck, Mike, Reguero, Borja, and Parker, Kai, 2022, Ocean wave time-series simulated with a global-scale numerical wave model under the influence of projected CMIP6 wind and sea ice fields: U.S. Geological Survey, online.

   Online Links:

   • https://doi.org/10.5066/P9KR0RFM

   Type_of_Source_Media: online database
   Source_Contribution:

   High-resolution nearshore wave data derived from several global climate models were used to calibrate CoSMoS-COAST models during hindcast period and used in future projections.

   NOAA SLR projections (source 4 of 6)

   Sweet, W.V., Hamlington, B.D., Kopp, R.E., Weaver, C.P., Barnard, P.L., Bekaert, D., Brooks, W., Craghan, M., Dusek, G., Frederikse, T., Garner, G., Genz, A. S., Krasting, J.P., Larour, E., Marcy, D., Marra, J.J., Obeysekera, J., Osler, M., Pendleton, M., Roman, D., Schmied, L., Veatch, W., White, K.D., and Zuzak, C., 20220215, Global and regional sea level rise scenarios for the United States: updated mean projections and extreme water level probabilities along U.S. coastlines. NOAA Technical Report, SLR and Coastal Flood Hazard Scenarios and Tools: Interagency Task Force: National Oceanic and Atmospheric Administration (NOAA) and National Aeronautics and Space Administration (NASA), online.

   Online Links:

   • https://sealevel.nasa.gov/task-force-scenario-tool
   • https://oceanservice.noaa.gov/hazards/sealevelrise/sealevelrise-tech-report.html

   Type_of_Source_Media: online database
   Source_Contribution:

   SLR rates were used to create SLR curves for future shoreline change model scenarios.

   NOAA tide stations (source 5 of 6)

   National Oceanic and Atmospheric Administration (NOAA), 2022, NOAA water level stations: NOAA, online.

   Online Links:

   • https://tidesandcurrents.noaa.gov/stations.html?type=Water+Levels

   Type_of_Source_Media: online database
   Source_Contribution: adjusting SLR projections for models
   

   topobathy slopes (source 6 of 6)

   Mickey, R.C., and Passeri, D.L., 20220706, Atlantic and Gulf coast sandy coastline topo-bathy profile and characteristic database: U.S. Geological Survey data release: U.S. Geological Survey, online.

   Online Links:

   • https://doi.org/10.5066/P9838KPW

   Type_of_Source_Media: online database
   Source_Contribution:

   beach profile information used to determine transgression slopes

2. How were the data generated, processed, and modified?

   Date: 09-Feb-2021 (process 1 of 8)

   Set up model structure as discussed in Vitousek and others (2017, 2021, and 2023) for the U.S. Atlantic Coast. The structure included defining cross-shore transect locations modified from DSAS (Himmelstoss and others, 2021) as locations for the one-dimensional numerical models simulating the shoreline position given changing sea level and wave forcing. Modifications mainly consisted of reduction of excess length to focus on regions of sandy beach. 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). Members were differentiated by wave forcing (multiple realizations of future wave conditions), which in this study area was derived from seven WW3 wave model simulations of Global Climate Models (GCMs). The model was set up to assimilate SDS observations and calibrate itself for a majority portion of the historical data period (1990-2015), 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 December 2016 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, SDS, WW3

   Date: 20-Jul-2021 (process 2 of 8)

   Obtained SDS observations from all available Landsat satellite imagery in the study area using the CoastSat toolbox (Vos and others,2019a; 2019b), at model transect locations. Transects were included along sections of beach adjacent to river mouths and inlets, and these locations should be understood to be highly dynamic and include more uncertainty. SDS observations used in model assimilation and validation are derived from low-cloud-cover (less than 50 percent cloud cover) Landsat imagery from 1990 to 2020, where images with large georectification errors were removed. Imagery had horizontal resolution of 30 m, which was pan-sharpened to 15 m. See the SDS dataset for more details. See Vitousek and others (2021; 2023) for more information. Data sources used in this process:

   • SDS

   Date: 30-Nov-2022 (process 3 of 8)

   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 (1990-2015), 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 1990, 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). 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 wave conditions from seven different GCMs. 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 and transgression slopes, representing end-members of model behavior bracketing a spectrum of possible solutions. The first key model parameter affecting shoreline change is a slope factor. The effective slope of the beach (called the equilibrium beach profile slope) is connected to the transgression slope (defined as the relationship between future sea-level rise and beach recession; Wolinsky and Murray, 2009). The equilibrium beach slope is typically derived geometrically from the beach shape, whereas the transgression slope is also affected by physical/hydrodynamic/geologic processes that can vary across landscapes. As these can be defined quite differently for varying needs and landscapes, three different slope scenarios that span a potential range of shoreline changes associated with different equilibrium beach profile slopes and transgression slopes are presented: A) steep, B) intermediate, and C) gentle. For slope A (steep), the transgression slope is defined by a steep equilibrium beach profile slope, derived geometrically from alongshore-smoothed topobathy slopes (between MSL and dune toe) and is more reflective of passive flooding of the beach and in locations with limited space to accommodate active beach changes (such as urban beaches); it is also applicable at shorter time-scales. Slope B (intermediate) has the transgression slope set to be an average of the steeper beach-face slope (between MSL and the dune toe) and the gently sloping offshore beach slope (between the depth of closure and the back of the dune or beach). This case is typical driven by a combination of active beach change and passive flooding of the beach, applicable at intermediate time-scales or at partially developed/partially natural beaches with moderate space to accommodate beach retreat. For slope C (gentle), the transgression slope is set as the offshore beach slope (derived from alongshore-smoothed slope between depth of closure at approximately 10 m water depth to the back of the dune or beach), and typically yields the largest changes in shoreline position. It is applicable for longer time-scales (or larger SLR scenarios) and for low-lying, natural beach and barrier systems with ample space to accommodate active beach changes. Additional 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 2017 or later); 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 from NOAA SLR projections through 2100 (relative to 2005; Vitousek and others, 2023) for locations on the U.S. East Coast are used in model scenarios for this study’s SLR scenarios. The SLR projections provide unique conditions for each tide station, and here we apply spatially variable sea-level conditions based on a nearest neighbor approach. For SLR scenarios of 100 cm and less, the NOAA SLR curve showing 114 cm at 2100 is used. For SLRs 150 cm and 200 cm, NOAA’s projection of 150 cm and 209 cm are used, respectively, representing those values of SLR by 2100. A 300 cm sea-level scenario is not reported in the NOAA projection dataset; however, this sea-level scenario is constructed by extrapolating the difference between the 200 cm and 150 cm curve to a 300 cm curve. Final shoreline positions are taken at the corresponding dates along the NOAA SLR projection curve corresponding to the target SLR scenario. For the hindcast period (1990-2015), SLR is extrapolated linearly backwards from 2005 to the beginning of the model (January 1990) based on a historical rate. Data sources used in this process:

   • NOAA SLR projections, NOAA tide stations, WW3, SDS, topobathy slopes

   Date: 07-Dec-2022 (process 4 of 8)

   Checked all output to ensure quality results.

   Date: 30-Dec-2022 (process 5 of 8)

   Organized model projections into KMZ files grouped by state. Output is further grouped by model case, numbered thusly: In model case 1 shorelines are not allowed to evolve and erode past current boundaries and recent historical accretion rates are set to 0; case 2 shorelines are not allowed to evolve and erode past current boundaries and recent historical accretion rates are preserved; case 3 shorelines are allowed to erode without limitation and recent historical accretion rates are set to 0; and case 4 shorelines are allowed to erode without limitation and recent historical accretion rates are preserved. KMZs include the initial shoreline, final shoreline projections for SLR scenarios, model uncertainty (representing 95 percent of the ensemble model spread and robust model uncertainty) and transect information including all model parameters calibrated at each respective site. 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. Null projection and model parameter values are listed as NaN. Data are only shown for transect locations where models were run. File names indicate state and model parameters; for example, ShorelineChange_projctn_NC_Case1_TrgSlopeA.kmz contains shoreline projections in North Carolina for case 1 (landward boundary limitation is imposed to shoreline evolution, and recent historical accretion rates do not continue) and transgression slope A (using a steep equilibrium beach profile slope as the transgression slope). For best display of results, it is recommended to turn off any 3D viewing.

   Date: 16-May-2023 (process 6 of 8)

   Edits were made to correct spelling in authors names. No data were changed. The metadata available from a harvester may supersede metadata bundled within a download file. Users are advised to compare the metadata date of this file to any similar file to ensure they are using the most recent version. (scochran@usgs.gov)

   Date: 08-Aug-2023 (process 7 of 8)

   Edits were made to to include final citation information for a Cross_Reference. No data were changed. The metadata available from a harvester may supersede metadata bundled within a download file. Users are advised to compare the metadata date of this file to any similar file to ensure they are using the most recent version. (aoneill@usgs.gov)

   Date: 09-May-2024 (process 8 of 8)

   Edits to metadata were made to reflect corrected model case number identifiers in the process step explaining organization of the files. No data were changed. The metadata available from a harvester may supersede metadata bundled within a download file. Users are advised to compare the metadata date of this file to any similar file to ensure they are using the most recent version. (aoneill@usgs.gov)

3. What similar or related data should the user be aware of?
   Vitousek, S., Barnard, P.L., Limber, P. W., 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:

   • https://doi.org/10.1002/2016JF004065

   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., 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.

   Online Links:

   • https://doi.org/10.1029/2019JF005506

   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:

   • https://doi.org/10.1029/2022JF006936

   Other_Citation_Details:

   Vitousek, S., Vos, K., Splinter, K.D., Erikson, L. and Barnard, P.L., 2023, A model integrating satellite-derived shoreline observations for predicting fine-resolution shoreline response to waves and sea-level rise applied across large coastal regions: Journal of Geophysical Research: Earth Surface, v. 128(7), e2022JF006936.

   Vos, K., 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..

   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., 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..

   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.

   Vos, K., Harley, M.D., Splinter, K.D., Walker, A., and Turner, I.L., 2020, Beach slopes from satellite‐derived shorelines..

   Online Links:

   • https://doi.org/10.1029/2020GL088365

   Other_Citation_Details:

   Vos, K., Harley, M. D., Splinter, K. D., Walker, A., and Turner, I. L, 2020, Beach slopes from satellite‐derived shorelines: Geophysical Research Letters, v. 47(14), e2020GL088365.

   Himmelstoss, E.A., Farris, A.S., Henderson, R.E., Kratzmann, M.G., Ergul, A., Zhang, O., Zichichi, J.L., and Thieler, E.R., 2021, Digital Shoreline Analysis System (version 5.1).

   Online Links:

   • https://code.usgs.gov/cch/dsas

   Other_Citation_Details:

   Himmelstoss, E.A., Farris, A.S., Henderson, R.E., Kratzmann, M.G., Ergul, A., Zhang, O., Zichichi, J.L., and Thieler, E.R., 2021, Digital Shoreline Analysis System (version 5.1): U.S. Geological Survey software release, https://code.usgs.gov/cch/dsas.

   Stockdon, H.F., Holman, R.A., Howd, P.A., and Sallenger, A.H.J., 2006, Empirical parameterization of setup, swash, and runup.

   Online Links:

   • https://doi.org/10.1016/j.coastaleng.2005.12.005

   Other_Citation_Details:

   Stockdon, H.F., Holman, R.A., Howd, P.A., and Sallenger, A.H.J., 2006, Empirical parameterization of setup, swash, and runup: Coastal Engineering, v. 53, p. 573-588.

   Wolinsky, M.A., and Murray, A.B., 2009, A unifying framework for shoreline migration: 2. Application to wave-dominated coasts.

   Online Links:

   • https://doi.org/10.1029/2007JF000856

   Other_Citation_Details:

   Wolinsky, M.A, and Murray, A.B., 2009, A unifying framework for shoreline migration: 2. Application to wave-dominated coasts: Journal of Geophysical Research, v. 114, F01009.

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?
   N/A
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?

1. Who distributes the data set? (Distributor 1 of 1)
   
   U.S. Geological Survey - CMGDS

   2885 Mission Street

   Santa Cruz, CA
   

   831-427-4747 (voice)

   pcmsc_data@usgs.gov
2. What's the catalog number I need to order this data set? These data are available in KMZ format grouped by state and model case (for example, ShorelineChange_projctn_FL.zip contains all model case KMZs for Florida).
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?
   • Availability in digital form:

     Data format:	The .zip file contains KMZ files for Florida in format KMZ (version Google Earth Pro (version 7.3, Google, 2017)) Features are in KMZ format and are projected in UTM Zone 17 and 18 coordinates, with horizontal datum NAD83 and vertical datum NAVD88. Size: 258.5
     Network links:	https://doi.org/10.5066/10.5066/P9BQQTCI

     Data format:	The .zip file contains KMZ files for Georgia in format KMZ (version Google Earth Pro (version 7.3, Google, 2017)) Features are in KMZ format and are projected in UTM Zone 17 and 18 coordinates, with horizontal datum NAD83 and vertical datum NAVD88. Size: 58.4
     Network links:	https://doi.org/10.5066/10.5066/P9BQQTCI

     Data format:	The .zip file contains KMZ files for Virginia in format KMZ (version Google Earth Pro (version 7.3, Google, 2017)) Features are in KMZ format and are projected in UTM Zone 17 and 18 coordinates, with horizontal datum NAD83 and vertical datum NAVD88. Size: 68.1
     Network links:	https://doi.org/10.5066/10.5066/P9BQQTCI

     Data format:	The .zip file contains KMZ files for Maryland in format KMZ (version Google Earth Pro (version 7.3, Google, 2017)) Features are in KMZ format and are projected in UTM Zone 17 and 18 coordinates, with horizontal datum NAD83 and vertical datum NAVD88. Size: 22.8
     Network links:	https://doi.org/10.5066/10.5066/P9BQQTCI

     Data format:	The .zip file contains KMZ files for Delaware in format KMZ (version Google Earth Pro (version 7.3, Google, 2017)) Features are in KMZ format and are projected in UTM Zone 17 and 18 coordinates, with horizontal datum NAD83 and vertical datum NAVD88. Size: 17.9
     Network links:	https://doi.org/10.5066/10.5066/P9BQQTCI

   • Cost to order the data: None.

5. What hardware or software do I need in order to use the data set?
   These data can be viewed with Google Earth software and other compatible GIS software, such as Global Mapper and QGIS, and can be translated to ESRI formats.

Who wrote the metadata?

Dates:

Last modified: 10-May-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)