Application of stochastic models for long-range sediment transport during extreme typhoon events

The research focuses on the Shihmen Reservoir in northern Taiwan, a multifunctional rolled rockfill reservoir experiencing reduced capacity due to sediment accumulation, which is further exacerbated by significant flood events during typhoons. Our research employs a novel fractional stochastic diffusion particle tracking model (FSD-PTM), incorporating fractional Brownian motion to account for the long-range dependencies observed in suspended sediment transport during extreme typhoon events. By capturing these dependencies, the model offers improved predictions of sediment dynamics, essential for understanding and managing sediment-related challenges in hydrological reservoir systems under severe weather conditions, thereby supporting operational safety and environmental compliance. The application of the FSD-PTM has provided new insights into sediment dynamics during typhoon events in the Shihmen Reservoir. Traditional models based on memoryless diffusion often fail to represent the persistence observed in suspended sediment transport under sustained turbulent flows. By incorporating long-range dependence into the particle motion framework, this study highlights the importance of memory effects in simulating sediment spread. These findings support more accurate predictions of sediment deposition patterns, providing a scientific basis for adaptive reservoir operation strategies in response to intensifying climatic extremes. • Captures memory-driven sediment dynamics using a fractional stochastic model. • Demonstrates non-Fickian transport during typhoons via Hurst-based simulation. • Aims to improve sediment modeling under time-persistent turbulent conditions.