Accelerating large-scale hydrological modeling with stepwise spatial-temporal multimember parallelization

Advancements in distributed hydrological modeling require higher temporal and spatial resolutions, increasing the demand for high-performance computing. Runoff-routing models face inefficiencies due to upstream–downstream dependencies. Increasing threads reduce computing time but lower efficiency due to task imbalances. We propose a stepwise spatial–temporal–multimember domain decomposition method with OpenMP. Applied to the Pearl River Basin at 90-m resolution, the method was tested at three stations: ZhaiGao (110,808 grids), ShiJiao (4.94 million grids), and Outlet0 (48.58 million grids). Results showed traditional serial computing took 172.18, 7726.94, and 79,470.21 seconds, respectively, for 10-year daily simulations (totaling 3653 time steps). With 13 threads, spatial layering parallelization reduced times to 21.86, 757.93, and 7262.06 seconds, achieving efficiencies of 0.61, 0.78, and 0.84. At ZhaiGao, 52 threads yielded efficiency of 0.06 with only spatial layering but increased to 0.55 and 0.80 upon adding temporal indexing and multimember parallelization. Overall, our approach significantly accelerates large-scale hydrodynamic flood modeling.