Incorporating a backward-forward stochastic particle tracking model into a hydraulic modeling framework to identify probable sedimentation sources during typhoons

Abstract

Study region: Shih-men Reservoir, Taiwan Study focus: The primary purpose of this research is to integrate the novel backward-forward stochastic particle tracking model (BF-SPTM) into a three-dimensional sediment transport model for the Shih-men Reservoir. This integration aims to simulate the distribution of sediment during typhoon events and effcientlyidentify probable sedimentation sources . Additionally, a Genetic Algorithm (GA) is developed to optimize the critical parameters of the EFDC hydrodynamic module and sediment transport module. New hydrological insights: In the proposed BF-SPTM, the influence function is applied for the first time in a three-dimensional hydraulic model to integrate the backward and forward particle tracking processes, thereby providing more precise probable sources. In this study, a 'probable source' is considered a grid cell rather than a point, which substantially reduces the computational time in the BF-SPTM model. A case study using BF-SPTM during Typhoon MEGI demonstrates its capabilityto identify probable sources in natural water using the EFDC model. The output of the BF-SPTM can be effectively used to identify potential sources of sedimentation. Additionally, this study incorporated more realistic boundary and flow conditions from a natural river to enhance the practical application of BF-SPTM. These conditions included flow velocity, diffusivity, water surface, and bed topography.

Keywords

• Suspended sediment concentration
• Backward-forward Stochastic Particle Tracking Model (BF-SPTM)
• Probable sedimentation source
• Stochastic modeling