A Unified, Data‐Informed Model for Shoreline Evolution Due To Longshore and Cross‐Shore Processes

Abstract Due partially to a changing global climate, there is an increasing need to develop a modeling approach capable of providing warnings of coastal evolution, including erosion at storm, interannual, and decadal timescales and in complex coastal settings. This research presents a reduced‐complexity model with a common approach to predicting shoreline change due to cross‐shore and longshore processes. The model architecture is designed to be flexible and data‐informed, incorporating an extended Kalman filter (eKF) for the assimilation of field measurements of shoreline change. Depending on the values of the optimized model‐free parameters resulting from the eKF assimilation, the longshore and cross‐shore shoreline model components can resemble either new, established, or hybrid versions of previously reported models. Thus, the model also provides new insight into the dynamics of shoreline change. The model is tested and demonstrated via a series of numerical tests and applied to field data collected at a complex coastal setting, showing good performance with R.M.S. shoreline prediction errors of <6 m. The eKF data assimilation suggests that a hybrid cross‐shore transport model, combining the characteristics of two established semiempirical models and a new disequilibrium longshore transport model, effectively describes the shoreline evolution at the swash‐aligned field site. The computational efficiency, stability, and versatility of the ForCE‐LX model provides a promising tool for the prediction of shoreline evolution on time‐scales of days to decades, in widespread coastal settings.