Decomposition–Migration Cooperative Modeling Approach for Forecasting Runoff in Data-Scarce Watershed Areas

To address runoff forecasting inaccuracies caused by data gaps in reservoir operations, this paper proposes a collaborative modeling framework integrating deep learning, signal decomposition, uncertainty quantification, and transfer learning. Validated on the Wei River (source basin) and Yongding River (target basin) with similar hydrological characteristics, the framework first constructs a Pyraformer-BiLSTM-LSS point forecasting model to enhance characterization of non-stationary runoff sequences. Then, the BLSO-VMD optimization decomposition technique filters and reconstructs forecasting noise, improving model robustness. Subsequently, a probabilistic interval forecasting model is developed via multi-task learning to reliably quantify uncertainty. To tackle data scarcity in the target domain, a “decomposition–reconstruction–transfer” learning mechanism transfers model knowledge from the source domain to the target domain. Results show that the framework achieves excellent performance in the source domain and successfully transfers to the data-scarce target domain, significantly enhancing the accuracy and stability of both point and interval forecasts. By establishing a collaborative modeling framework combining transfer learning and multi-task learning, along with an adaptive signal decomposition method based on BLSO and a multi-scale deep learning model, this study effectively addresses the challenges of accuracy and reliability in runoff forecasting for data-scarce basins. It provides a transferable and scalable technical pathway for runoff simulation and reservoir operation in hydrologically underserved regions, supporting sustainable water resource management and ecological protection.