A GPU-accelerated local time step-based shallow water model for integrated sea–land flood inundation in densely built urban areas during storm surges

Coastal flood models for storm surge often face considerable computational challenges due to the large number of computational cells in integrated sea–land scenarios and the need for extremely small time steps in certain regions, which are typically constrained by the globally established minimum time step. Aiming to address these limitations, this study presents an efficient integrated sea–land flood model for storm surge disaster prediction in coastal urban areas with dense buildings. The model uses a graphics processing unit (GPU)-accelerated framework to handle large computational grids and incorporates a local time step (LTS) approach to mitigate restrictions from locally refined grids, extremely small time steps, and flow condition disparities between sea and land. A GPU-optimized parallel algorithm enhances computational performance by refining the numerical framework, optimizing kernel functions, and improving memory utilization, demonstrating a seamless integration with the LTS approach. The efficiency of the model is demonstrated through storm surge and flood simulations in Macau, China, covering the sea, straits, and densely built coastal land areas. Results show that LTS scheme reduces computation time by approximately 40 times, markedly improving computational efficiency across different mess configurations. Owing to GPU and LTS acceleration, the model provides a powerful tool for real-time coastal flood forecasting.