Climate Warming-Induced Hydrological Regime Shifts in Cold Northeast Asia: Insights from the Heilongjiang-Amur River Basin

Jiaoyang Li, Ruixin Wang, Qiwei Huang, Jun Xia (), Ping Wang, Yuanhao Fang, Vladimir V. Shamov, Natalia L. Frolova and Dunxian She
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Jiaoyang Li: State Key Laboratory of Water Resources Engineering & Management, Wuhan University, Wuhan 430072, China
Ruixin Wang: Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China
Qiwei Huang: Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China
Jun Xia: State Key Laboratory of Water Resources Engineering & Management, Wuhan University, Wuhan 430072, China
Ping Wang: Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A, Datun Road, Chaoyang District, Beijing 100101, China
Yuanhao Fang: College of Hydrology and Water Resources, Hohai University, Nanjing 210024, China
Vladimir V. Shamov: Pacific Geographical Institute, Far-Eastern Branch, Russian Academy of Sciences, Vladivostok 690041, Russia
Natalia L. Frolova: Department of Land Hydrology, Lomonosov Moscow State University, GSP-1, Leninskie Gory, Moscow 119991, Russia
Dunxian She: State Key Laboratory of Water Resources Engineering & Management, Wuhan University, Wuhan 430072, China

Land, 2025, vol. 14, issue 5, 1-22

Abstract: Rapid climate warming and intensified human activities are causing profound alterations in terrestrial hydrological systems. Understanding shifts in hydrological regimes and the underlying mechanisms driving these changes is crucial for effective water resource management, watershed planning, and flood disaster mitigation. This study examines the hydrological regimes of the Heilongjiang-Amur River Basin, a transboundary river basin characterized by extensive permafrost distribution in northeastern Asia, by analyzing long-term daily meteorological (temperature, precipitation, evaporation) and hydrological data from the Komsomolsk, Khabarovsk, and Bogorodskoye stations. Missing daily runoff data were reconstructed using three machine learning methods: Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), and Convolutional Long Short-Term Memory Networks (CNN-LSTM). Trend analysis, abrupt change detection, and regression techniques revealed significant warming and increased actual evapotranspiration in the basin from 1950 to 2022, whereas precipitation and snow water equivalent showed no significant trends. Climate warming is significantly altering hydrological regimes by changing precipitation patterns and accelerating permafrost thaw. At the Komsomolsk station, an increase of 1 mm in annual precipitation resulted in a 0.48 mm rise in annual runoff depth, while a 1 °C rise in temperature led to an increase of 1.65 mm in annual runoff depth. Although annual runoff exhibited no significant long-term trend, low-flow runoff demonstrated substantial increases, primarily driven by temperature and precipitation. These findings provide critical insights into the hydrological responses of permafrost-dominated river basins to climate change, offering a scientific basis for sustainable water resource management and strategies to mitigate climate-induced hydrological risks.

Keywords: Heilongjiang-Amur river basin; machine learning; runoff variation; multiple regression; climate change (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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