Advancements in enhancing flood evolution and urban inundation predictions: A study of local time stepping algorithm and GPU-accelerated hydrodynamic model

Abstract

In recent years, floods occur frequently affected by extreme rainstorms. Timely flood prediction and rapid simulation are crucial in mitigating their impact. However, the current models often utilize the global time step method for updates and solving, resulting in high computational costs. To further improve the efficiency while ensuring calculational accuracy, this study proposes the local time stepping and GPU-accelerated hydrodynamic model based on non-uniform grid. The proposed model utilizes the HLLC approximate Riemannian solution to estimate interface flux and employs the local time step algorithm, enabling each unit to update variables using the locally permitted maximum time step. It relies on non-uniform grid technology, the local time step algorithm, and GPU acceleration to optimize computational efficiency, facilitating swift simulation of flood evolution processes across large-scale terrains. The results show that the hydrodynamic model has good stability. The results show that the model has good accuracy and stability. It can accurately predict the flood evolution process, flood arrival time, flood inundation range, and urban inundation process in complex terrains. Researchers can identify effective measures for reducing flood risks and improving the area’s resilience against flooding events. Due to its significantly improved efficiency in flood prediction, it is more suitable for actual large-scale flood processes.