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 constr...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 importance.This study employs a transfer learning(TL)framework to simulate daily streamflow in the Dulong-lrrawaddy River Basin(DIRB),a less-studied transboundary basin shared by Myanmar,China,and India.Our results show that TL significantly improves streamflow predictions: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.Additionally,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 accurately capturing non-linear relationships among multiple variables than the process-based model.Integrated gradients(IG)analysis furtherillustrates TL's ability to capture spatial het-erogeneity in upstream and downstream sub-basins and its adeptness in characterizing different flow regimes.This study underscores the potential of TL in enhancing the understanding of hydrological processes in large-scale catchments and highlights its value for water resource management in transboundary basins under data scarcity.展开更多
基金National Key Research and Development Program of China,No.2022YFF1302405National Natural Science Foundation of China,No.42201040+1 种基金The National Key Research and Development Program of China,No.2016YFA0601601The China Postdoctoral Science Foundation,No.2023M733006。
文摘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 importance.This study employs a transfer learning(TL)framework to simulate daily streamflow in the Dulong-lrrawaddy River Basin(DIRB),a less-studied transboundary basin shared by Myanmar,China,and India.Our results show that TL significantly improves streamflow predictions: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.Additionally,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 accurately capturing non-linear relationships among multiple variables than the process-based model.Integrated gradients(IG)analysis furtherillustrates TL's ability to capture spatial het-erogeneity in upstream and downstream sub-basins and its adeptness in characterizing different flow regimes.This study underscores the potential of TL in enhancing the understanding of hydrological processes in large-scale catchments and highlights its value for water resource management in transboundary basins under data scarcity.
