[A-OS18] Physical Oceanography (General)

[AOS18-P08] Integrating Stochastic Diffusion and One-Line Models for Enhanced Coastline Evolution Prediction

*Yu-Ju Hung¹, Gene Jiing-Yun You¹ (1.National Taiwan University)

Keywords:Coastline Evolution, Stochastic Diffusion Particle Tracking Model, One-Line Model, Sediment Transport

Coastline evolution is significantly shaped by the interplay of fluvial sediment input and complex coastal hydrodynamics. Traditional one-line models provide a deterministic framework to capture shoreline changes driven by sediment supply, yet they often overlook key processes—such as alongshore currents, tidal flows, and wave-driven transport—that are essential for accurate coastal predictions. This research addresses these limitations by coupling the one-line model with a stochastic diffusion particle tracking model (SD-PTM). As a Langevin-based method, SD-PTM captures sediment dispersion under variable flow conditions, serving as a non-constant term that enhances the predictive capacity of the one-line model. This integrated approach more effectively represents the combined marine and fluvial influences on coastline evolution in dynamic estuarine environments. By refining how sediment transport pathways and shoreline responses are modeled, the proposed hybrid methodology offers a promising tool for coastal management and adaptation planning. It underscores the need for models that capture the nuanced interactions of hydrodynamic drivers and sediment supply, ultimately improving our ability to understand and predict coastline change.