Adaptive Estimation of Time -Varying Longshore Sediment Transport Coefficients Using Extended Kalman Data Assimilation

Long shore sediment transport (LST) is a fundamental process driving shoreline change, beach nourishment stability, and near shore morphodynamics. Classical formulations, such as the CERC equation, Bailard energetics model, and Souls by–van Rijn predictor, rely on a proportionality coefficient (K) that is commonly assumed constant across space and time. Mounting evidence suggests K is dynamic, varying under storm clusters, seasonal morphodynamic transitions, and anthropogenic interventions. This paper proposes a framework for adaptive estimation of the LST coefficient using an Extended Kalman Filter EKF) embedded in the one-line shoreline model. Shoreline observations from satellite imagery (Coast Sat), video monitoring (ARGUS, Coast Snap), and UAV surveys update both shoreline positions and K(s,t) in real time. The framework yields posterior distributions of K that reflect uncertainty and spatio-temporal variability and improves hind cast accuracy over static-coefficient models. Implications include enhanced nourishment design, inlet management, and climate adaptation