首页|Transfer learning framework for streamflow prediction in large-scale transboundary catchments:Sensitivity analysis and applicability in data-scarce basins
Transfer learning framework for streamflow prediction in large-scale transboundary catchments:Sensitivity analysis and applicability in data-scarce basins
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The imbalance in global streamflow gauge distribution and regional data scarcity,especially in large transboundary basins,challenge regional water resource management.Effectively utilizing these limited data to construct reliable models is of crucial practical im-portance.This study employs a transfer learning(TL)framework to simulate daily streamflow in the Dulong-Irrawaddy River Basin(DIRB),a less-studied transboundary basin shared by Myanmar,China,and India.Our results show that TL significantly improves streamflow pre-dictions:the optimal TL model achieves an average Nash-Sutcliffe efficiency of 0.872,showing a marked improvement in the Hkamti sub-basin.Despite data scarcity,TL achieves a mean NSE of 0.817,surpassing the 0.655 of the process-based model MIKE SHE.Addition-ally,our study reveals the importance of source model selection in TL,as different parts of the flow are affected by the diversity and similarity of data in the source model.Deep learning models,particularly TL,exhibit complex sensitivities to meteorological inputs,more accu-rately capturing non-linear relationships among multiple variables than the process-based model.Integrated gradients(IG)analysis further illustrates TL's ability to capture spatial het-erogeneity in upstream and downstream sub-basins and its adeptness in characterizing dif-ferent flow regimes.This study underscores the potential of TL in enhancing the under-standing of hydrological processes in large-scale catchments and highlights its value for wa-ter resource management in transboundary basins under data scarcity.
transfer learningstreamflow predictiondeep learningmodel sensitivitydata scarcityinternational river
MA Kai、SHEN Chaopeng、XU Ziyue、HE Daming
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Institute of International Rivers and Eco-security,Yunnan University,Kunming 650091,China
Yunnan Key Laboratory of International Rivers and Transboundary Eco-security,Yunnan University,Kunming 650091,China
Civil and Environmental Engineering,Pennsylvania State University,University Park,PA,United States
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