An Integrated Hydrological–Hydrodynamic Model Based on GPU Acceleration for Catchment-Scale Rainfall Flood Simulation

Extreme rainstorms are difficult to predict and often result in catchment-scale rainfall flooding, leading to substantial economic losses globally. Enhancing the numerical computational efficiency of flood models is essential for improving flood forecasting capabilities. This study presents an integrated hydrological–hydrodynamic model accelerated using GPU (Graphics Processing Unit) technology to perform high-efficiency and high-precision rainfall flood simulations at the catchment scale. The model couples hydrological and hydrodynamic processes by solving the fully two-dimensional shallow water equations (2D SWEs), incorporating GPU-accelerated parallel computing. The model achieves accelerated rainstorm flooding simulations through its implementation on GPUs with parallel computing technology, significantly enhancing its computational efficiency and maintaining its numerical stability. Validations are conducted using an idealized V-shaped catchment and an experimental benchmark, followed by application to a small catchment on the Chinese Loess Plateau. The computational experiments reveal a strong positive correlation between grid cell numbers and GPU acceleration efficiency. The results also demonstrate that the proposed model offers better computational accuracy and acceleration performance than the single-GPU model. This GPU-accelerated hydrological–hydrodynamic modeling framework enables rapid, high-fidelity rainfall flood simulations and provides critical support for timely and effective flood emergency decision making.