A Model Integrating Satellite‐Derived Shoreline Observations for Predicting Fine‐Scale Shoreline Response to Waves and Sea‐Level Rise Across Large Coastal Regions.

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 (SLR), and other natural and anthropogenic processes. We apply the developed ensemble model to the entire California coastline (approximately 1,760 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 (1995–2015) provides nearly equivalent model forecast accuracy during a validation period (2015–2020) 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 to projected wave and sea‐level conditions, while holding the model parameters fixed. By 2100, the model estimates that 24%–75% of California's beaches may become completely eroded due to SLR scenarios of 1.0–3.0 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. Plain Language Summary: We present a computer model to predict shoreline change due to waves, sea‐level rise (SLR), and other local processes. We apply the model to the entire California coastline (approximately 1,760 km), much of which is not well monitored using traditional survey methods. Observations of historical shoreline position obtained from satellite images can be used in lieu of traditional shoreline survey data to estimate erosion/accretion trends as well as to calibrate and validate models. By 2100, the model estimates that 24%–75% of California's beaches may become completely eroded due to SLR scenarios of 1.0–3.0 m, respectively. Key Points: We assimilate a large‐scale, ensemble shoreline‐change model with observations of shoreline position derived from Landsat satellitesSatellite‐derived shoreline observations perform similarly to in situ observations for calibrating and validating shoreline modelsThe model estimates that, without interventions, 24%–75% of California's beaches may be completely eroded by 2100 due to future sea‐level rise [ABSTRACT FROM AUTHOR]