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In the Pacific Basin, the El Niño/Southern Oscillation (ENSO) is the dominant mode of interannual climate variability, driving substantial changes in oceanographic forcing and impacting Pacific coastlines. Yet, how sandy coasts respond to these basin-scale changes has to date been limited to a few long-term beach monitoring sites, predominantly on developed coasts. Here we use 38 years of Landsat imagery to map shoreline variability around the Pacific Rim and identify coherent patterns of beach erosion and accretion controlled by ENSO. On the basis of more than 83,000 beach transects covering 8,300 km of sandy coastline, we find that approximately one-third of all transects experience significant erosion during El Niño phases. The Eastern Pacific is particularly vulnerable to widespread erosion, most notably during the large 1997/1998 El Niño event. By contrast, La Niña events coincide with significant accretion for approximately one-quarter of all transects, although substantial erosion is observed in southeast Australia and other localized regions. The observed regional variability in the coastal response to ENSO has important implications for coastal planning and adaptation measures across the Pacific, particularly in light of projected future changes in ENSO amplitude and flavour.

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. 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. 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 We assimilate a large‐scale, ensemble shoreline‐change model with observations of shoreline position derived from Landsat satellites Satellite‐derived shoreline observations perform similarly to in situ observations for calibrating and validating shoreline models The model estimates that, without interventions, 24%–75% of California's beaches may be completely eroded by 2100 due to future sea‐level rise

The steepness of the beach face is a fundamental parameter for coastal morphodynamic research. Despite its importance, it remains extremely difficult to obtain reliable estimates of the beach‐face slope over large spatial scales (thousands of km of coastline). In this letter, a novel approach to estimate this slope from time series of satellite‐derived shoreline positions is presented. This new technique uses a frequency domain analysis to find the optimum slope that minimizes high‐frequency tidal fluctuations relative to lower‐frequency erosion/accretion signals. A detailed assessment of this new approach at eight locations spanning a range of tidal regimes, wave climates, and sediment grain sizes shows strong agreement (R2= 0.93) with field measurements. The automated technique is then applied across thousands of beaches in eastern Australia and California, USA, revealing similar regional‐scale distributions along these two contrasting coastlines and highlights the potential for new global‐scale insight to beach‐face slope spatial distribution, variability, and trends. How steep a beach is can dictate the way the beach interacts with the incoming ocean waves and therefore is of paramount importance for coastal scientists and engineers, coastal flood modelers, and swim safety officers. However, despite its importance, it is impractical to obtain reliable estimates of the “typical” beach‐face slope along large lengths of sandy coastlines (hundreds to thousands of km) because of the logistics that would be necessary to visit many sites repeatedly to obtain these measurements. This letter describes a new technique to estimate the beach‐face slope in the absence of field observations, relying instead on long‐term publicly available satellite observations and a global tide model. This technique is then applied to thousands of beaches along the coastlines of eastern Australia and California in the United States. A novel remote sensing technique to estimate beach‐face slopes from satellite imagery and modeled tides is presentedTime series of shoreline change are transformed into frequency domain to find the slope that minimizes high‐frequency tidal fluctuationsValidation against in situ data shows high accuracy across sites ranging in grain size, tidal range, and wave climate