A hybrid deep learning rainfall-runoff forecasting model Incorporating spatiotemporal information from multi-source data

Deep learning has been widely applied in runoff forecasting, focusing primarily on temporal features but neglecting the influence of spatial heterogeneity. Capturing complex spatiotemporal atmosphere–land–hydrology interactions by deep learning remains challenging in rainfall–runoff forecasting. This study proposes a hybrid deep learning framework, Runoff Forecasting Model Integrating Spatiotemporal Features (RFMISF), which leverages the complementary strengths of multiple deep learning architectures to construct five modules, thereby fusing multi-source data. Specifically, the framework integrates the Convolutional Neural Networks for extracting spatial features of underlying surface, the LSTM for capturing temporal dependencies in rainfall and runoff, and the Convolutional LSTM (ConvLSTM) for learning spatiotemporal features of meteorological inputs. Two case studies of daily runoff forecasting have been deviced for the BHT and SBY hydrological stations with distinct hydrological regimes. At the BHT, the RFMISF reduced RMSE by 31.53% and MAE by 33.39% compared to the Xinanjiang baseline; at the SBY, the RFMISF improved NSE by 13.6% and decreased MAE by 27.39%. Ablation experiments of excluding station rainfall, underlying surface, and meteorological data are further conducted to underline the importance of multi-source data. At the BHT, the experiments led to RMSE increases of 9.29%, 4.69%, and 5.59% during flood season, respectively. At the SBY, the experiments resulted in reductions of NSE by 15.08%, 4.46%, and 12.94%. Additionally, model performance varies with rainfall intensity, indicating the differentiated contributions of multi-source data in complex runoff responses. Although reanalysis data enhance spatial representativeness, their systematic errors require careful treatment. Overall, this study introduces a novel, robust framework for enhancing runoff prediction and improving water resource management in hydrologically complex environments.