A cooperative search algorithm-based flood forecasting framework: application across diverse Chinese catchments - Jinhai Yang; Lei Wen; Junliang Guo; Yiru Chen; Yongchen Zhu; Yun Wang; Meihong Ma; Meihong Ma (Frontiers in Earth Science)
Flood forecasting is crucial for disaster mitigation, particularly in regions prone to flash floods. This study introduces a novel flood forecasting framework by coupling the Geomorphological Instantaneous Unit Hydrograph (GIUH) with the Xinanjiang model and optimizing parameters using the Cooperation Search Algorithm (CSA). Applied across six diverse Chinese catchments, the framework significantly improved computational efficiency and accuracy. Key findings demonstrate that: 1) CSA achieved high Nash-Sutcliffe Efficiency (NSE >0.9) with only 16 optimization trials on average, outperforming the SCE-UA algorithms; 2) The model performed exceptionally in data-sparse regions, achieving NSE values >0.9 even with minimal datasets; and 3) Enhanced runoff routing via GIUH enabled accurate simulation of extreme rainfall events. These results highlight the framework’s potential for operational flood forecasting and disaster management globally. Future research will expand validation datasets and explore applications across varied hydrological and climatic conditions.