Multi‐Source Adaptive‐Fusion Transfer Learning for Streamflow Forecasting in Data‐Scarce Catchments

Streamflow gauging records remain sparse in many regions, limiting hydrological predictions. Transfer Learning (TL), which uses knowledge from data‐rich sources to improve predictions in data‐scarce targets, has emerged as a promising solution. Most TL applications in hydrology pre‐train deep learning (DL) models on large meteo‐hydrological data sets to enhance generalization across diverse catchments. However, this strategy faces two key limitations: (a) TL performance for a specific target may be hindered by irrelevant source inputs and suboptimal fusion of source information; and (b) TL data requirements are demanding. Here, we propose a Multi‐Source Adaptive‐Fusion Transfer Learning (MSAF‐TL) network to address these limitations. MSAF‐TL first extracts independent temporal features from each highly related source catchment and then weights them based on the similarity of their static attributes to those of the target. These weighted features are adaptively fused using a cross‐attention mechanism, strengthening the final target prediction. We test this framework using a large‐sample hydrological data set for Great Britain (CAMELS‐GB). Results show that small sets of highly similar source catchments can match the transferability of large data sets. For a target catchment with only 1 year of training data, MSAF‐TL achieves an NSE of 0.85 using only the top two similar sources, compared with 0.78 when using all 668 CAMELS‐GB catchments. With limited sources, MSAF‐TL consistently outperforms conventional multi‐source TL (MSTL) across varying source quantities and target conditions. This performance gap widens under greater data scarcity, with average NSE improvements over MSTL reaching 22% when only 6 months of target training data are available. Overall, this study demonstrates that MSAF‐TL can achieve more accurate and stable TL performance in data‐scarce regions, and that data requirements can be reduced without compromising performance through source pre‐selection.