Integrated Assessment of Climate-Driven Streamflow Changes in a Transboundary Lake Basin Using CMIP6-SWAT+-BMA: A Sustainability Perspective

Feiyan Xiao, Yaping Wu, Xunming Wang (), Ping Wang, Congsheng Fu and Jing Zhang
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Feiyan Xiao: School of Civil Engineering and Architecture, Shaanxi University of Technology, 1# East Ring Rd., Hantai District, Hanzhong 723001, China
Yaping Wu: Yunnan Key Laboratory of Plateau Geographical Process and Environmental Change, Faculty of Geography, Yunnan Normal University, Kunming 650500, China
Xunming Wang: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China
Ping Wang: Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 11A Datun Road, Chaoyang District, Beijing 100101, China
Congsheng Fu: Key Laboratory of Watershed Geographic Sciences, Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Jing Zhang: School of Civil Engineering and Architecture, Shaanxi University of Technology, 1# East Ring Rd., Hantai District, Hanzhong 723001, China

Sustainability, 2025, vol. 17, issue 17, 1-19

Abstract: Estimating the impacts of climate change on streamflow in the Xiaoxingkai Lake Basin is vital for ensuring sustainable water resource management and transboundary cooperation across the entire Xingkai Lake Basin, a transboundary lake system shared between China and Russia. In this study, 11 Global Climate Models (GCMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under two Shared Socioeconomic Pathways (SSP245 and SSP585) were used to drive the Soil and Water Assessment Tool Plus (SWAT+) model. Streamflow projections were made for two future periods: the 2040s (2021–2060) and the 2080s (2061–2100). To correct for systematic biases in the GCM outputs, we applied the Delta Change method, which significantly reduced root mean square error (RMSE) in both precipitation and temperature by 3–35%, thereby improving the accuracy of SWAT+ simulations. To better capture inter-model variability and enhance the robustness of streamflow projections, we used the Bayesian Model Averaging (BMA) technique to generate a weighted ensemble, which outperformed the simple arithmetic mean by reducing uncertainty across models. Our results indicated that under SSP245, greater increases were projected in annual streamflow as well as in wet and normal-flow seasons (e.g., streamflow in normal-flow season in the 2080s increased by 13.0% under SSP245, compared to 7.0% under SSP585). However, SSP585 produced a much larger relative amplification in the dry season, with percentage changes relative to the historical baseline reaching up to +171.7% in the 2080s, although the corresponding absolute increases remained limited due to the low baseline flow. These findings quantify climate-driven hydrological changes in a cool temperate lake basin by integrating climate projections, hydrological modeling, and ensemble techniques, and highlight their implications for understanding hydrological sustainability under future climate scenarios, providing a critical scientific foundation for developing adaptive, cross-border water management strategies, and for further studies on water resource resilience in transboundary basins.

Keywords: CMIP6; Xiaoxingkai Lake Basin; SWAT+; streamflow; hydrological sustainability; climate adaptation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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