Prediction of regional water resources carrying capacity based on stochastic simulation: A case study of Beijing-Tianjin-Hebei Urban Agglomeration

Abstract

Study region: Beijing-Tianjin-Hebei urban agglomeration in China Study focus: The prediction of water resources carrying capacity (WRCC) can provide an effective reference for the rational allocation and efficient utilization of water resources. Traditional prediction methods obtained a definite WRCC value but fail to reflect the uncertainty of WRCC changes and limit reference for the optimal allocation of water resources. To ensure the accuracy, availability and comprehensiveness of prediction, this paper adopts the improved principal component analysis (PCA) to screen indicators, and predicts the WRCC through the coupled model of Monte Carlo and Grey Wolf Optimization-Support Vector Machine(GWO-SVM), addressing single result issues and computational complexity. At the same time, various regulation schemes for sensitive indicators are designed to provide an effective guidance for the optimal allocation and sustainable use of water resources. New hydrological insights for the region: In 2025, the probability of WRCC in Tianjin, Handan, Xingtai, Hengshui, Cangzhou, Langfang to maintain grade III is more than 80 %, and that in Beijing, Baoding, Tangshan, Qinhuangdao, Zhangjiakou, Chengde to reach grade IV is more than 50 %. The sensitivity analysis shows that the sensitive indicators mainly focus on water supply and consumption, water use efficiency and pollutant gas emissions. The WRCC can be further improved under different schemes. The results can provide effective guidance for the optimal allocation of water resources and maintain sustainable economic and social development in Beijing-Tianjin-Hebei urban agglomeration.

Keywords

• Water resources carrying capacity
• GWO-SVM
• Monte-Carlo
• Regulation scheme
• Beijing-Tianjin-Hebei urban agglomeration