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<p>Water resources carrying capacity (WRCC) constitutes a pivotal metric for evaluating regional water systems’ capacity to support sustainable socio-economic and ecological development. Previous studies exhibit deficiencies in the dynamic analysis of determinant drivers and insufficient exploration of associated uncertainties. This study innovatively integrates an AHP-EWM weighted TOPSIS model to reconcile subjective and objective weighting paradigms, while establishing a system dynamics simulation framework incorporating grey prediction GM (1,1) for dynamic multi-scenario modeling. Through empirical analysis of Turpan and Hami cities in Xinjiang, China (2012–2022), this study elucidates the operational mechanisms through which hydraulic engineering, economic development, and water resource endowments govern WRCC dynamics. The principal findings are as follows: (1) In 2022, Turpan’s WRCC evaluation value reached 0.35 (overloaded), while Hami reached 0.45 (critical) on the carrying capacity scale. (2) Key obstacle factors include annual precipitation (11.31%), per capita regional GDP (7.86%), GDP growth rate (7.50%), population density (6.37%), water yield modulus (5.80%), beneficiary rate of urban–rural water supply projects (5.30%), and per capita water resources (5.10%), ordered by their obstacle degree. (3) Scenario projections indicate Turpan’s optimal WRCC evaluation value under S5 (integrated coordination scenario) will rise to 0.39 by 2035, whereas Hami’s peak performance under S4 (water infrastructure revitalization scenario) is projected to reach 0.60. Future policy formulation necessitates heightened focus on developing regionally tailored hydraulic engineering systems to establish multi-source water replenishment mechanisms, while sustaining water conservation strategies. This research provides empirical support for achieving sustainable development in arid zones under rigid water resource redline management constraints.</p>

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Multi-scenario Simulation and Prediction Based on Water Resources Carrying Capacity Constraints

  • 期刊论文

  • 发表日期：

    2025年11月10日

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  • Xiaoming Zheng,
  • Junfeng Li,
  • Dongbao Song,
  • Chengxiao Ma,
  • Qiong Feng

摘要

Water resources carrying capacity (WRCC) constitutes a pivotal metric for evaluating regional water systems’ capacity to support sustainable socio-economic and ecological development. Previous studies exhibit deficiencies in the dynamic analysis of determinant drivers and insufficient exploration of associated uncertainties. This study innovatively integrates an AHP-EWM weighted TOPSIS model to reconcile subjective and objective weighting paradigms, while establishing a system dynamics simulation framework incorporating grey prediction GM (1,1) for dynamic multi-scenario modeling. Through empirical analysis of Turpan and Hami cities in Xinjiang, China (2012–2022), this study elucidates the operational mechanisms through which hydraulic engineering, economic development, and water resource endowments govern WRCC dynamics. The principal findings are as follows: (1) In 2022, Turpan’s WRCC evaluation value reached 0.35 (overloaded), while Hami reached 0.45 (critical) on the carrying capacity scale. (2) Key obstacle factors include annual precipitation (11.31%), per capita regional GDP (7.86%), GDP growth rate (7.50%), population density (6.37%), water yield modulus (5.80%), beneficiary rate of urban–rural water supply projects (5.30%), and per capita water resources (5.10%), ordered by their obstacle degree. (3) Scenario projections indicate Turpan’s optimal WRCC evaluation value under S5 (integrated coordination scenario) will rise to 0.39 by 2035, whereas Hami’s peak performance under S4 (water infrastructure revitalization scenario) is projected to reach 0.60. Future policy formulation necessitates heightened focus on developing regionally tailored hydraulic engineering systems to establish multi-source water replenishment mechanisms, while sustaining water conservation strategies. This research provides empirical support for achieving sustainable development in arid zones under rigid water resource redline management constraints.