Investigating the potential of time-varying free parameters in equilibrium shoreline change models through data assimilation

Equilibrium shoreline change models with calibrated, time-invariant free parameters have demonstrated good skill in hindcasting shoreline evolution at sites dominated by cross-shore sediment transport. However, their performance can be biased by the specific conditions present during the calibration period. In this study, a dual parameter-state ensemble Kalman filter (EnKF) was applied to track non-stationarity in model free parameters at three sites along the west coast of Europe. Introducing time-varying parameters did not substantially improve performance relative to an already well-calibrated stationary model. Model skill improvement occurred mainly during the EnKF correction step, highlighting the potential of real-time data assimilation for maintaining model stability. Although variations in model parameters may compensate for unresolved processes and should be interpreted cautiously, incorporating climate-driven, time-varying parameters could improve extreme-event predictions at seasonally dominated sites and enhance overall model performance in regions influenced by complex, multimodal wave climates.