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A model integrating satellite-derived shoreline observations for predicting fine-scale shoreline response to waves and sea-level rise across large coastal regions
  • +2
  • Sean Vitousek,
  • Kilian Vos,
  • Kristen D Splinter,
  • Li Erikson,
  • Patrick L Barnard
Sean Vitousek
University of Illinois at Chicago, USGS Pacific Coastal & Marine Science Center

Corresponding Author:svitousek@gmail.com

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Kilian Vos
University of New South Wales - Sydney
Kristen D Splinter
University of New South Wales - Sydney
Li Erikson
USGS Pacific Coastal & Marine Science Center
Patrick L Barnard
USGS Pacific Coastal & Marine Science Center


Satellite-derived shoreline observations combined with dynamic shoreline models enable fine-scale predictions of coastal change across large spatiotemporal scales. Here, we present a satellite-data-assimilated, "littoral-cell"-based, ensemble Kalman-filter shoreline model to predict coastal change and uncertainty due to waves, sea-level rise, and other natural and anthropogenic processes. We apply the developed ensemble model to the entire California coastline (approximately 1,350 km), much of which is sparsely monitored with traditional survey methods (e.g., Lidar/GPS). Water-level-corrected, satellite-derived shoreline observations (obtained from the CoastSat toolbox) offer a nearly unbiased representation of in-situ surveyed shorelines (e.g., Mean Sea Level elevation contours) at Ocean Beach, San Francisco. We demonstrate that model calibration with satellite observations during a 20-year hindcast period (1995 to 2015) provides a nearly equivalent model forecast accuracy during a validation period (2015 to 2020) compared to model calibration with monthly in-situ observations at Ocean Beach. When comparing model-predicted shoreline positions to satellite-derived observations, the model achieves an accuracy of <10 m RMSE for nearly half of the entire California coastline for the validation period. The calibrated/validated model is then applied for multi-decadal simulations of shoreline change due projected wave and sea-level conditions while holding the model parameters fixed. By 2100, the model estimates that 25 to 70% of California's beaches may become completely eroded due to sea-level rise scenarios of 0.5 to 3.0 m, respectively. The satellite-data-assimilated modeling system presented here is generally applicable to a variety of coastal settings around the world owing to the global coverage of satellite imagery.  
07 Mar 2023Submitted to ESS Open Archive
09 Mar 2023Published in ESS Open Archive