Enhanced Coastal Shoreline Modeling Using an Ensemble Kalman Filter to Include Nonstationarity in Future Wave Climates

Abstract

AbstractA novel approach to improve seasonal to interannual sandy shoreline predictions is presented, whereby model‐free parameters can vary in time, adjusting to potential nonstationarity in the underlying model forcing. This is achieved by adopting a suitable data assimilation technique (dual state‐parameter ensemble Kalman filter) within the established shoreline evolution model ShoreFor. The method is first tested and evaluated using synthetic scenarios, specifically designed to emulate a broad range of natural sandy shoreline behavior. This approach is then applied to a real‐world shoreline data set, revealing that time‐varying model‐free parameters are linked through physical processes to changing characteristics of the wave forcing. Greater accuracy of shoreline predictions is achieved, compared to existing stationary modeling approaches. It is anticipated that the wider application of this method can improve our understanding and prediction of future beach erosion patterns and trends in a changing wave climate.