Dual-Source Adaptive-Fusion Transfer Learning for Hydrological Forecasting in Data-scarce Catchments

Historical records observed at hydrological stations are scarce in many regions, bringing significant challenges to the hydrological predictions for these regions. Transfer learning (TL), increasingly applied in hydrology, leverages knowledge from data-rich catchments (sources) to enhance predictions in data-scarce catchments (targets), providing new insight into data-scarce region predictions. Most existing TL approaches pre-train models using large meteoro-hydrological datasets to improve overall generalizability to target catchments. However, the predictive performance for specific catchments would be constrained due to irrelevant source data inputs and the lack of effective source fusion strategies. To address these challenges, this study proposes the Dual-Source Adaptive Fusion TL Network (DSAF-Net), which utilizes a pre-trained dual-branch feature extraction module (DBFE) to extract knowledge from two carefully selected source catchments, minimizing noise and redundancy associated with larger datasets. A cross-attention fusion module is then incorporated to dynamically identify key knowledge of the target catchment and adaptively fuse complementary information. This fusion module is embedded after each layer in the DBFE to enhance multi-level feature integration. Results demonstrate that DSAF-Net achieves superior prediction accuracy to single-source TL and large dataset TL strategies. These findings highlight the potential of DSAF-Net to advance hydrological forecasting and support water resource management in data-scarce regions.