Reconstructing Long-Term Daily Streamflow Data at the Discontinuous Monitoring Station in the Ungauged Transboundary Basin Using Machine Learning

Vinh Ngoc Tran, Hanh Duc Nguyen, Hai Khuong, Huy Ba Dao, Quan Huu Minh Le, Chi Que Nguyen and Giang Tien Nguyen ()
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Vinh Ngoc Tran: University of Michigan
Hanh Duc Nguyen: VNU University of Science
Hai Khuong: VNU University of Science
Huy Ba Dao: VNU University of Science
Quan Huu Minh Le: VNU University of Science
Chi Que Nguyen: VNU University of Science
Giang Tien Nguyen: VNU University of Science

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2025, vol. 39, issue 7, No 18, 3327-3348

Abstract: Abstract Streamflow data is essential for water resource management, especially in transboundary river basins where data sharing between countries is often limited. Simulating and forecasting streamflow in such basins, particularly those with large upstream reservoir systems, presents significant challenges. This study introduces a novel machine learning (ML) approach to reconstruct streamflow data at intermittent gauging stations in transboundary rivers, using streamflow and water level data from neighboring stations to enhance model performance. This approach contrasts with traditional methods that mainly rely on forcing data. We applied six ML models to the Da River basin in Northern Vietnam, where all models achieved high accuracy, with Nash-Sutcliffe Efficiency and Kling-Gupta Efficiency exceeding 0.9. The LGBM (light gradient boosting machine regressor) performed best overall. We found that combining multiple ML models improved simulation accuracy, and some models performed reliably without precipitation data, highlighting the importance of nearby stream gauge data. Furthermore, the ML models outperformed a process-based distributed model (Variable Infiltration Capacity) in general metrics and hydrological signature evaluations, especially in simulating baseflow, low flow, and high flow conditions. ML also demonstrated faster computational efficiency and required less data for configuration. This research emphasizes the need for tailored approaches and data selection in complex transboundary river systems, offering a promising solution for effective water resource management in regions with limited cross-border data sharing and contributing to more accurate, adaptable hydrological forecasting.

Keywords: Streamflow reconstruction; Machine learning; Transboundary river; Process-based model; Hydrological signature; Explainable artificial intelligence (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11269-025-04109-6

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