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Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins

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Abstract

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-Irrawaddy 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 further illustrates TL’s ability to capture spatial heterogeneity 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.

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Acknowledgments

We thank Peter Reichert for the support in the sensitivity analysis (Method used in 3.2.1) that helped improve the manuscript. The hydrologic deep learning code used in this work can be accessed at https://doi.org/10.5281/zenodo.3993880. Data for CAMELS can be downloaded at https://ral.ucar.edu/solutions/products/camels. Data for CAMELS-GB can be downloaded at https://doi.org/10.5285/8344e4f3-d2ea-44f5-8afa-86d2987543a9. Data for CAMELS-CL can be downloaded at http://www.cr2.cl/camels-cl/. Discharge data of DIRB can be downloaded at https://www.bafg.de/GRDC/EN/Home/homepage_node.html.

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Authors and Affiliations

  1. Institute of International Rivers and Eco-security, Yunnan University, Kunming, 650091, China

    Kai Ma, Ziyue Xu & Daming He

  2. Yunnan Key Laboratory of International Rivers and Transboundary Eco-security, Yunnan University, Kunming, 650091, China

    Kai Ma, Ziyue Xu & Daming He

  3. Civil and Environmental Engineering, Pennsylvania State University, University Park, PA, USA

    Chaopeng Shen

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Correspondence to Daming He.

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Foundation: National Key Research and Development Program of China, No.2022YFF1302405; National Natural Science Foundation of China, No.42201040; The National Key Research and Development Program of China, No.2016YFA0601601; The China Postdoctoral Science Foundation, No.2023M733006

Author: Ma Kai (1992–), PhD, specialized in transboundary hydrology and hydrologic modelling.

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Ma, K., Shen, C., Xu, Z. et al. Transfer learning framework for streamflow prediction in large-scale transboundary catchments: Sensitivity analysis and applicability in data-scarce basins. J. Geogr. Sci. 34, 963–984 (2024). https://doi.org/10.1007/s11442-024-2235-x

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