Transboundary water security challenges in the Upper Heilong-Amur River Basin under climate stress: Insights from a hydrological perspective

Kaiwen Zhang , Kai Ma , Changlei Dai , Miao Yu , Jiwei Leng , Ziyue Xu , Daming He

Geography and Sustainability ›› 2026, Vol. 7 ›› Issue (3) : 100450

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Geography and Sustainability ›› 2026, Vol. 7 ›› Issue (3) :100450 DOI: 10.1016/j.geosus.2026.100450
Research Article
research-article
Transboundary water security challenges in the Upper Heilong-Amur River Basin under climate stress: Insights from a hydrological perspective
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Abstract

The Heilong-Amur River Basin (HARB), the largest transboundary basin in northeastern Asia, is increasingly vulnerable to water-security risks arising from climate change and human activity, yet robust quantitative spatiotemporal attribution remains challenging. In this study, we developed a Grid-based Hydrological Response Attribution (GHRA) framework that integrates the Variable Infiltration Capacity (VIC) model with a cumulative slope change approach. By coupling a distributed hydrological model with attribution analysis and incorporating a physically based correction coefficient, the framework enables spatially explicit and physically consistent streamflow attribution. The GHRA was applied to the upper HARB for historical (1992–2017) and future periods (2025–2099). Results show that climate change and human activity contributed 49.57 % and 50.43 % to historical streamflow changes, respectively. Climate change is projected to dominate during the future period, contributing 71.11 % under SSP2–4.5 and 76.91 % under SSP5–8.5. Compared with uncorrected results, the GHRA reduced the standard deviation of attribution values by 11.33 % and 20.74 % under SSP2–4.5 and SSP5–8.5, respectively. Regionally, climate change dominates the Kherlen and Argun Rivers, whereas human activity -particularly land-cover change -remains the primary driver in the Zeya and Shilka Rivers. Projections based on CMIP6 data indicate that Russia contributes the largest proportion of water resources (63 % and 67 % under SSP2–4.5 and SSP5–8.5, respectively), followed by China (26 % and 21 %) and Mongolia (11 % and 12 %). Climate-driven stress increases spring floods along the Argun, Heilong-Amur, and Zeya Rivers and intensifies summer flooding in the Shilka and Zeya Rivers, exacerbating transboundary water security challenges.

Keywords

Hydrological response attribution / VIC model / Future streamflow projections / International Rivers / Transboundary water security risks / Upper Heilong-Amur River Basin

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Kaiwen Zhang, Kai Ma, Changlei Dai, Miao Yu, Jiwei Leng, Ziyue Xu, Daming He. Transboundary water security challenges in the Upper Heilong-Amur River Basin under climate stress: Insights from a hydrological perspective. Geography and Sustainability, 2026, 7 (3) : 100450 DOI:10.1016/j.geosus.2026.100450

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Data availability

Data will be made available on request.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

CRediT authorship contribution statement

Kaiwen Zhang: Writing – original draft, Visualization, Investigation, Data curation. Kai Ma: Visualization, Methodology, Formal analysis, Conceptualization, Funding acquisition, Supervision, Writing – review & editing. Changlei Dai: Writing – review & editing, Project administration. Miao Yu: Investigation, Data curation. Jiwei Leng: Software, Data curation. Ziyue Xu: Investigation, Data curation. Daming He: Writing – review & editing, Supervision, Project administration, Funding acquisition.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 42201040 ), the Yunnan Scientist Workstation on International River Research (Grant No. KXJGZS-2019–005 ), the China Postdoctoral Science Foundation (Grant No. 2023M733006 ), the Yunnan Provincial Key Laboratory of International Rivers and Transboundary Ecological Security Open Fund (Grant No. 2022KF03 ), and the Yunnan University Graduate Research Innovation Fund Project (Grant No. KC-22221269 ).

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