CoSMoS (Coastal Storm Modeling System) Southern California v3.0 projections of shoreline change due to 21st century sea level rise

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What does this data set describe?

Title:
CoSMoS (Coastal Storm Modeling System) Southern California v3.0 projections of shoreline change due to 21st century sea level rise
Abstract:
Summary: This dataset contains projections of shoreline positions and uncertainty bands for future scenarios of sea-level rise. Projections were made using CoSMoS-COAST, a numerical model forced with global-to-local nested wave models and assimilated with LIDARlidar-derived shoreline vectors. Details: Projections of shoreline position in Southern California are made for scenarios of 0.0, 0.5, 1.0, 1.5, 2.0 and 5.0 meters of sea-level rise by the year 2100. Projections are made at CoSMoS Monitoring and Observation Points, which represent shore-normal transects spaced 100 m alongshore. The newly developed CoSMoS-COAST model solves a coupled set of partial differential equations that resembles conservation of sediment for the series of transects. The model is synthesized from several shoreline models in the scientific literature: One-line model formulations (Pelnard-Considere, 1956; Larson and others, 1997; Vitousek and Barnard, 2015) account for longshore transport, equilibrium shoreline-model formulations (Yates and others, 2009) account for wave-driven cross-shore transport, and equilibrium beach-profile formulations (Bruun, 1954; Davidson-Arnot, 2005; Anderson and others, 2015) account for long-term beach-profile adjustments due to sea-level rise. The model uses an extended Kalman filter data-assimilation method to improve the fit of the model to lidar-derived observed shoreline positions. As with previous studies (for example, USGS National Assessment of Shoreline Change), the available shoreline data are spatially and temporally sparse. The data-assimilation method automatically adjusts model parameters and estimates the effects of unresolved processes such as natural and anthropogenic sediment supply. The data-assimilation method used in CoSMoS-COAST has been improved over the original method of Long and Plant (2012). The new method ensures that the coefficients of the equilibrium shoreline-change model retain their preferred sign. Without this improvement, the data-assimilation method was subject to instability. Data assimilation is performed only on days of the simulations where shoreline data are observed. For the shoreline projection period (2015–2100), no such data are available and thus no data-assimilation can be performed. Some of the model components are ignored for certain transects and geographic locations. For example, on small pocket beaches longshore transport is assumed negligible and, therefore, is not computed via the model. Generally, projections were not made at transects where the shoreline is armored and sandy beaches are not present. The formulations that comprise the shoreline model are only valid for sandy beaches. Furthermore, they become invalid as the beach becomes fully eroded and possibly undermines coastal infrastructure. Hence, we have specified a maximally eroded shoreline state that represents the interface of sandy beaches and coastal infrastructure (for example, roads, homes, buildings, sea-walls). If the beach erodes to this line, then it is not permitted to erode further. However, we note that the model can be run without specifying this unerodable line. The shoreline model uses a series of global-to-local nested wave models (such as WaveWatch III and SWAN) forced with Global Climate Model (GCM)-derived wind fields. Historical and projected time series of daily maximum wave height and corresponding wave period and direction from 1990 to 2100 force the shoreline model. The modeled wave predictions are a key input to the CoSMoS-COAST shoreline model because the calculation of both the longshore sediment-transport rate (obtained via the “CERC” equation developed by the Army Corp of Engineers; Shore Protection Manual, 1984) and equilibrium shoreline change (Yates and others, 2009) critically depends on the wave conditions. Notably, variations in nearshore wave angle can significantly affect the calculation of longshore transport. Thus, high-resolution modeling efforts to predict nearshore wave conditions are integral components of the shoreline modeling. Sea level vs. time curves are modeled as a quadratic function. Coefficients of the quadratic curves are obtained via three equations: (1) present sea level is assumed to be at zero elevation, (2) the present rate of sea-level rise is assumed to be 2 mm/yr, which is consistent with values observed at local tide gages, (3) future sea-level elevation at 2100 is either 0.5, 1.0, 1.5, 2.0 or 5.0 m based on the scenarios considered. We note that sea level only affects the equilibrium-profile changes derived via the Anderson and others (2015) model. The model uses a forward Euler time-stepping method with a daily time step. The longshore sediment-transport term has the option of using a second-order, implicit time-stepping method (Vitousek and Barnard 2015). However, for these modeling efforts, the forward Euler time-stepping method is sufficient and does not violate numerical stability determined by the Courant–Friedrichs–Lewy CFL condition when using a daily time step on 100 m-spaced transects. The model is composed of numerous scripts and functions implemented in Matlab. The main modeling routines have approximately 1,000-plus lines of code. However, many other functions exist that are necessary to initialize and operate the model. Overall the entire shoreline-modeling system is estimated to have approximately 10,000 lines of code. The modeling system is computationally efficient in comparison to traditional coupled hydrodynamic-wave-morphology models like Delft3D. Century-scale simulations for the entire 400 km coast of Southern California take approximately 20–30 minutes of wall-clock time. This limited computational cost allows the possibility of applying ensemble prediction. Significant uncertainty is associated with the process noise of the model and unresolved coastal processes. This makes estimation of uncertainty difficult. The uncertainty bands predicted here represent 95 percent confidence bands associated with the modeled shoreline fluctuations. Unresolved processes are not accounted for in the uncertainty bands and could lead to significantly more uncertainty than reported in these predictions. These results should be considered preliminary. Although some QA/QC has been completed, the results will improve through time as 1) more shoreline data become available to the data-assimilation method, 2) the models are improved, and 3) ensemble wave-forcing is applied to the model. Refereces Cited: Anderson, T. R., Fletcher, C. H., Barbee, M. M., Frazer, L. N., and Romine, B. M., 2015. Doubling of coastal erosion under rising sea level by mid-century in Hawaii. Natural Hazards, 1-29. Bruun, P., 1954. Coast erosion and the development of beach profiles. Technical Memorandum, vol. 44. 82 pp. Beach Erosion Board, Corps of Engineers. Davidson-Arnott, R. G., 2005. Conceptual model of the effects of sea level rise on sandy coasts. Journal of Coastal Research, 1166-1172. Hapke, C. J., Reid, D., Richmond, B. M., Ruggiero, P., and List, J., 2006. National assessment of shoreline change Part 3: Historical shoreline change and associated coastal land loss along sandy shorelines of the California Coast. US Geological Survey Open File Report, 1219, 27. Larson, M., Hanson, H., and Kraus, N. C., 1997. Analytical solutions of one-line model for shoreline change near coastal structures. Journal of Waterway, Port, Coastal, and Ocean Engineering, 123(4), 180-191. Long, J. W., and Plant, N. G., 2012. Extended Kalman Filter framework for forecasting shoreline evolution. Geophysical Research Letters, 39(13). Pelnard-Considere, R., 1956. Essai de theorie de l'evolution des formes de rivage en plages de sable et de galets. Société hydrotechnique de France. Shore Protection Manual, 1984. U.S. Army Corps of Engineers, Coastal Engineering Research Center, U.S. Government Printing Office, Washington, D.C. Vitousek, S., and Barnard, P.L., 2015. A nonlinear, implicit one-line model to predict long-term shoreline change. Proceedings of the Coastal Sediments Conference 2015. Yates, M. L., Guza, R. T., and O'Reilly, W. C., 2009. Equilibrium shoreline response: Observations and modeling. Journal of Geophysical Research: Oceans (1978–2012), 114(C9).
Supplemental_Information:
This work is one portion of on-going modeling efforts for California and the western United States. For more information on CoSMoS implementation, see https://walrus.wr.usgs.gov/coastal_processes/cosmos/ For best display, turn off any 3-D visualization on the Geographic Information System (GIS) platform used to show the data. For example, if viewing this data in Google Earth, turn off the "3D Buildings" layer, or it will interfere with display of the inundation surfaces.
  1. How might this data set be cited?
    Vitousek, Sean, 20151116, CoSMoS (Coastal Storm Modeling System) Southern California v3.0 projections of shoreline change due to 21st century sea level rise: U.S. Geological Survey, Pacific Coastal and Marine Science Center, Santa Cruz, CA.

    Online Links:

  2. What geographic area does the data set cover?
    West_Bounding_Coordinate: -120.48706054569
    East_Bounding_Coordinate: -117.01538085833
    North_Bounding_Coordinate: 34.51524902452
    South_Bounding_Coordinate: 32.472325899539
  3. What does it look like?
  4. Does the data set describe conditions during a particular time period?
    Beginning_Date: 01-Jul-2014
    Ending_Date: 16-Nov-2015
    Currentness_Reference:
    publication date
  5. What is the general form of this data set?
    Geospatial_Data_Presentation_Form: Google Earth KML/KMZ
  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.
    2. What coordinate system is used to represent geographic features?
      Grid_Coordinate_System_Name: Universal Transverse Mercator
      Universal_Transverse_Mercator:
      UTM_Zone_Number: 11
      Transverse_Mercator:
      Scale_Factor_at_Central_Meridian: 0.999600
      Longitude_of_Central_Meridian: -100.000000
      Latitude_of_Projection_Origin: 0.000000
      False_Easting: 500000.000000
      False_Northing: 0.000000
      Planar coordinates are encoded using row and column
      Abscissae (x-coordinates) are specified to the nearest 2.000000
      Ordinates (y-coordinates) are specified to the nearest 2.000000
      Planar coordinates are specified in meters
      The horizontal datum used is North American Datum 1983.
      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?
    Coastal erosion
    Coastal erosion caused by waves and sea-level rise (Source: originators at United States Geolgical Survey, Pacific Coastal and Marine Science Center)
    Shoreline position
    Long-term (decadal) shoreline position (Source: producer defined)
    ValueDefinition
    for example "0.2"Long-term shoreline position in 2100 due to waves and sea-level rise

Who produced the data set?

  1. Who are the originators of the data set? (may include formal authors, digital compilers, and editors)
    • Sean Vitousek
  2. Who also contributed to the data set?
  3. To whom should users address questions about the data?
    Sean Vitousek
    U.S. Geological Survey, Pacific Coastal and Marine Science Center
    400 Natural Bridges Drive
    Santa Cruz, CA
    USA

    831-460-7549 (voice)
    svitousek@usgs.gov

Why was the data set created?

The projections were made to support the USGS's Coastal Storm Modeling System (CoSMoS), which provides coastal managers and policymakers with potential long-term coastal erosion and flooding scenarios.

How was the data set created?

  1. From what previous works were the data drawn?
  2. How were the data generated, processed, and modified?
    Date: 15-Feb-2015 (process 1 of 5)
    Projections of shoreline position in Southern California are made for scenarios of 0.0, 0.5, 1.0, 1.5, 2.0 and 5.0 meters of sea-level rise by the year 2100. Projections are made at CoSMoS Monitoring and Observation Points (MOP) which represent shore-normal transects spaced 100 m alongshore. The newly-developed CoSMoS-COAST model solves a coupled set of partial differential equations that resembles conservation of sediment for the series of transects. The model is synthesized from several shoreline models in the scientific literature: One-line model formulations (Pelnard-Considere 1956, Larson et al. 1997, Vitousek and Barnard 2015) account for longshore transport, equilibrium shoreline model formulations (Yates et al. 2009) account for wave-driven cross-shore transport, and equilibrium beach profile formulations (Bruun 1954, Davidson-Arnot 2005, Anderson et al. 2015) account for long-term beach profile adjustments due to sea-level rise. The model uses an extended Kalman filter data assimilation method to improve the fit of the model to LIDAR-derived observed shoreline positions. As with previous studies (e.g. USGS National Assessment of Shoreline Change), the available shoreline data is spatially and temporally sparse. The data assimilation method automatically adjusts model parameters and estimates the effects of unresolved processes such as natural and anthropogenic sediment supply. The data assimilation method used in CoSMoS-COAST has been improved over the original method of Long and Plant 2012: The new method ensures that the coefficients of the equilibrium shoreline change model retain their preferred sign. Without this improvement, the data assimilation method was subject to instability. Data assimilation is performed only on days of the simulations where shoreline data is observed. For the shoreline projection period (2015-2100), no such data is available and thus no data assimilation can be performed. Some of the model components are ignored for certain transects and geographic locations; for example, on small pocket beaches longshore transport is assumed negligible and, therefore, is not computed via the model. Generally, projections were not made at transects where the shoreline is armored and sandy beaches are not present. The formulations that comprise the shoreline model are only valid for sandy beaches. Furthermore, they become invalid as the beach becomes fully eroded and possibly undermines coastal infrastructure. Hence, we have specified a maximally eroded shoreline state that represents the interface of sandy beaches and coastal infrastructure (i.e. roads, homes, buildings, sea-walls, etc.). If the beach erodes to this line, then it is not permitted to erode further. However, we note that the model can be run without specifying this unerodable line. The shoreline model uses a series of global-to-local nested wave models (i.e. WaveWatch III and SWAN) forced with GCM-derived wind fields. Historical and projected time series of daily maximum wave height and corresponding wave period and direction from 1990 to 2100 force the shoreline model. The modeled wave predictions are a key input to the CoSMoS-COAST shoreline model because the calculation of both the longshore sediment transport rate (obtained via the CERC equation developed by the Army Corp of Engineers) and equilibrium shoreline change (Yates et al. 2009) critically depends on the wave conditions. Notably, variations in nearshore wave angle can significantly affect the calculation of longshore transport. Thus, high-resolution modeling efforts to predict nearshore wave conditions are integral components of the shoreline modeling. Sea-level vs. time curves are modeled as a quadratic function. Coefficients of the quadratic curve are obtained via three equations: (1) present sea-level is assumed to be at zero elevation, (2) the present rate of sea-level rise is assumed to be (2 mm/yr), which is consistent with values observed at local tide gages, (3) future sea-level elevation at 2100 is either 0.5, 1.0, 1.5, 2.0 and 5.0 m based on the scenarios considered. We note that sea-level only affects the equilibrium profile changes derived via the Anderson et al. (2015) model. The model uses a forward Euler time-stepping method with a daily time step. The longshore sediment transport term has the option of using a second-order, implicit time-stepping method (Vitousek and Barnard 2015). However, for these modeling efforts, the forward Euler time-stepping method is sufficient and does not lead violate numerical stability determined by the CFL condition when using a daily time step on 100 m-spaced transects. The model is composed of numerous scripts and functions implemented in Matlab. The main modeling routines have approximately 1000+ lines of code. However, many other functions exist that are necessary to initialize and operate the model. Overall the entire shoreline modeling system is estimated to have approximately 10,000 lines of code. The modeling system is computationally efficient in comparison to traditional coupled hydrodynamic-wave-morphology models like Delft3D. Century-scale simulations for the entire 400 km coast of Southern California take approximately 20-30 minutes of wall clock time. This limited computational costs allows the possibility of applying ensemble prediction. Significant uncertainty is associated with the process noise of the model and unresolved coastal processes. This makes estimation of uncertainty difficult. The uncertainty bands predicted here represent 95% confidence bands associated with the modeled shoreline fluctuations. Unresolved processes are not accounted for in the uncertainty bands and could lead to significantly more uncertainty than reported in these predictions. Built numerical models (run within Matlab) of shoreline change due to wave impacts and sea-level rise
    Date: 01-Jun-2015 (process 2 of 5)
    Obtained shoreline vectors from LIDAR surveys
    Date: 01-Jul-2015 (process 3 of 5)
    Improved data assimilation techniques
    Date: 01-Aug-2015 (process 4 of 5)
    Applied models to Southern California
    Date: 02-May-2018 (process 5 of 5)
    Added keywords from Coastal and Marine Ecological Classification Standard (CMECS) to metadata. Person who carried out this activity:
    U.S. Geological Survey
    Attn: Alan O. Allwardt
    Contractor -- Information Specialist
    2885 Mission Street
    Santa Cruz, CA

    831-460-7551 (voice)
    831-427-4748 (FAX)
    aallwardt@usgs.gov
  3. What similar or related data should the user be aware of?

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

  1. How well have the observations been checked?
    Attribute values are estimates of shoreline position due to plausible future sea-level rise scenarios and therefore cannot be cross-checked with observations, because observations do not exist. The projections were generated using up-to-date numerical methods and are in line with projections made by previous researchers. Uncertainty bands are included that take seasonal variations in the shoreline as derived using the numerical model into account.
  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?
    Data set is considered complete, but preliminary, for the information presented (as described in the abstract) and will be updated as needed through July 2016. The dataset will improve through time as 1) more shoreline data becomes available, 2) the models are improved, and 3) an ensemble wave forcing is prescribed. Data includes only areas where shoreline armoring (for example, seawalls, revetments) is not present, though some exceptions may apply. 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.

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. Share data products developed using these data with the U.S. Geological Survey. This information is not intended for navigational purposes. Read and fully comprehend the metadata prior to data use. Uses of these data should not violate the spatial resolution of the data. Where these data are used in combination with other data of different resolution, the resolution of the combined output will be limited by the lowest resolution of all the data. This database has been approved for release and publication by the Director of the USGS. Although this database has been subjected to rigorous review and is substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. Furthermore, it is released on condition that neither the USGS nor the United States Government may be held liable for any damages resulting from its authorized or unauthorized use. Although this Federal Geographic Data Committee-compliant metadata file is intended to document these data in nonproprietary form, as well as in ArcInfo format, this metadata file may include some ArcInfo-specific terminology.
  1. Who distributes the data set? (Distributor 1 of 1)
    U.S. Geological Survey - ScienceBase
    Denver Federal Center, Building 810, Mail Stop 302
    Denver, CO
    USA

    1-888-275-8747 (voice)
    sciencebase@usgs.gov
  2. What's the catalog number I need to order this data set?
  3. What legal disclaimers am I supposed to read?
    These data, identified as shoreline projections for the Southern California coast, have been approved for release and publication by the U.S. Geological Survey (USGS). Although these data have been subjected to rigorous review and are substantially complete, the USGS reserves the right to revise the data pursuant to further analysis and review. Furthermore, it is released on condition that neither the USGS nor the United States Government may be held liable for any damages resulting from its authorized or unauthorized use. Although these data have been processed successfully on a computer system at 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. The USGS or the U.S. Government shall not be held liable for improper or incorrect use of the data described and/or contained herein. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. These data are not intended for navigational use.
  4. How can I download or order the data?
    • Availability in digital form:
      Data format: The .zip file includes KMZ files, as well as the XML(.xml) version of the metadata and a windows media file fly-through of the KMZ. in format ZIP (version Google Earth Pro (version 7.0, Google, 2015)) Features are in KMZ format and are projected in UTM Zone 11 coordinates, with horizontal datum NAD83 and vertical datum NAVD88. Size: 172
      Network links: http://dx.doi.org/10.5066/F7T151Q4
    • Cost to order the data: none


Who wrote the metadata?

Dates:
Last modified: 02-May-2018
Metadata author:
Sean Vitousek
U.S. Geological Survey, Pacific Coastal and Marine Science Center
400 Natural Bridges Drive
Santa Cruz, CA
USA

831-460-7549 (voice)
svitousek@usgs.gov
Metadata standard:
FGDC Content Standard for Digital Geospatial Metadata (FGDC-STD-001-1998)

This page is <https://cmgds.marine.usgs.gov/catalog/pcmsc/cosmos/CoSMoS_Coastal_Storm_Modeling_System_Southern_California_v3_0_projections_of_shoreline_change_due_to_21st_century_sea_level_rise.faq.html>
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