Development of an MPE-BMA Ensemble Model for Runoff Prediction Under Future Climate Change Scenarios: A Case Study of the Xiangxi River Basin

Wenjie Li, Huabai Liu, Pangpang Gao (), Aili Yang, Yifan Fei, Yizhuo Wen, Yueyu Su and Xiaoqi Yuan
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Wenjie Li: Key Laboratory of Environmental Biotechnology, Xiamen University of Technology, Xiamen 361024, China
Huabai Liu: Xiamen Ocean Vocational College, Xiamen 361100, China
Pangpang Gao: Key Laboratory of Environmental Biotechnology, Xiamen University of Technology, Xiamen 361024, China
Aili Yang: Key Laboratory of Environmental Biotechnology, Xiamen University of Technology, Xiamen 361024, China
Yifan Fei: Key Laboratory of Environmental Biotechnology, Xiamen University of Technology, Xiamen 361024, China
Yizhuo Wen: Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
Yueyu Su: Key Laboratory of Environmental Biotechnology, Xiamen University of Technology, Xiamen 361024, China
Xiaoqi Yuan: Key Laboratory of Environmental Biotechnology, Xiamen University of Technology, Xiamen 361024, China

Sustainability, 2025, vol. 17, issue 10, 1-22

Abstract: Accurate runoff simulation and prediction are crucial for water resources management, especially under the impact of climate change. In this study, a multi-physics ensemble Bayesian model averaging (MPE-BMA) model is developed to improve runoff prediction accuracy by integrating a soil and water assessment tool (SWAT), hydrologiska byråns vattenbalansavdelning (HBV) model, and Bayesian model averaging (BMA) into a general framework. The MPE-BMA model integrates the strengths of the SWAT and HBV models. This approach enhances the robustness of simulation outputs and reduces uncertainties from single-model methods. MPE-BMA is subsequently employed to simulate and predict runoff for the upper reaches of Xiangxi River Basin (XXRB) in China, where four general circulation models (GCMs) and three shared socioeconomic pathways (SSP126, SSP245, and SSP585) are considered. Multiple statistical metrics (R 2 , NSE, and RMSE) prove that the MPE-BMA model outperforms the single models of SWAT and HBV. Results reveal that higher-emission scenarios generally lead to significant decreases in runoff, particularly by the 2080s. Specifically, under SSP585, runoff is projected to decrease by approximately 4.61–12.68% by the 2040s and 5.96–11.28% by the 2080s compared to the historical period. From the perspective of monthly and seasonal runoff changes, the peak runoff is projected to shift from June to May by the 2080s. Additionally, under SSP585, spring and summer runoffs tend to significantly increase, while winter runoff decreases sharply, leading to wetter summers and drier winters. These findings underscore the importance of enhancing water use efficiency, upgrading hydropower stations, and implementing watershed management practices to ensure sustainable water resources management in the XXRB amidst climate change.

Keywords: Bayesian model averaging; climate change; HBV; runoff prediction; SWAT; water resources management (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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