Climate change and human activities are intensifying the hydrologic cycle and increasing extreme events, challenging accurate prediction. This study builds on the Transformer architecture by introducing a sliding time window and runoff classification mechanism, enabling high-precision long-term runoff forecasting and significantly improving the simulation of extreme floods. However, the generalization ability of data-driven models remains limited in non-stationary environments. To address this issue, we further propose a hybrid framework that couples the process-based GBHM with the enhanced Transformer via bias correction. This fusion leverages the strengths of both models: the process-based model explicitly captures topographic heterogeneity, the spatial distribution of meteorological forcings, and their temporal variability, while the data-driven model excels at uncovering latent relationships among hydrological variables. The results demonstrate that the coupled model significantly outperforms traditional approaches in peak-flow prediction and exhibits superior robustness and generalizability under changing environmental conditions.
Gu et al. (Thu,) studied this question.