Spatio-Temporal Evaluation of Water Resources System Resilience and Identification of Its Driving Factors in the Yellow River Basin

被引:3
作者
Li, Jiaqi [1 ]
He, Weijun [1 ]
Jiang, Enhui [2 ,3 ]
Qu, Bo [2 ,3 ]
Yuan, Liang [1 ]
Degefu, Dagmawi Mulugeta [1 ,4 ]
Ramsey, Thomas Stephen [1 ]
机构
[1] China Three Gorges Univ, Sch Econ & Management, Yichang 443002, Peoples R China
[2] Yellow River Conservancy Commiss YRCC, Yellow River Inst Hydraul Res, Zhengzhou 450003, Peoples R China
[3] Minist Water Resources MWR, Key Lab Lower Yellow River Channel & Estuary Regul, Zhengzhou 450003, Peoples R China
[4] Toronto Metropolitan Univ, Fac Engn & Architectural Sci, Toronto, ON M5B 2K3, Canada
基金
中国国家自然科学基金;
关键词
water resources system; resilience evaluation; geographic detectors (GD); driving factors; Yellow River Basin; SOCIOECONOMIC DROUGHT; INDEX;
D O I
10.3390/w16030414
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Water resources are crucial for the development of ecosystems and humanity. The Yellow River Basin (YRB), as an important ecological area in China, is facing significant challenges in ecological protection and high-quality development due to global climate change and intense human activities. In order to alleviate the water resources crisis in the YRB, it is necessary to calculate the resilience of the water resources system and identify the main influencing factors. This paper considered the factors of water resources, social economy, and ecological environment, then constructed an evaluation framework of the water resources system resilience (WRSR) from three aspects: resistance, restoration, and adaptability. Taking nine provinces along the YRB as a case study, the WRSR was measured by using the entropy weight TOPSIS model, and its driving factors were analyzed with Geographical Detectors (GD). The results showed that: (1) From 2010 to 2022, the WRSR in the Yellow River Basin and various provinces was showing a fluctuating increasing trend, in which Ningxia had the highest average WRSR (0.646), while Shanxi had the lowest (0.168). (2) From three dimensions, the development trends of resistance, restoration, and adaptability in the YRB and various provinces from 2010 to 2022 were relatively stable. Shandong's resistance level far exceeded that of other provinces, having the highest average resistance value (0.692), and Ningxia had the highest average value of restoration (0.827) and adaptability (0.711). However, Gansu had the lowest average value of resistance (0.119), Sichuan had the lowest average value of restoration (0.097), and Shandong had the lowest average value of adaptability (0.110). (3) In terms of impact factors, the development and utilization rate of water resources (C13) and the development and utilization rate of surface water resources (C14) in the restoration subsystem consistently ranked in the top two of influencing factors. Similarly, the water consumption per 10,000 yuan of GDP (C26) in the adaptability subsystem consistently ranked within the top ten. On the other hand, the natural population growth rate (C6) in the resistance subsystem, as well as the impact of ammonia nitrogen emissions (C9) and total precipitation (C2) in wastewater, exhibited an upward trend. Based on these, this paper provides relevant suggestions for improving the WRSR in the YRB.
引用
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页数:23
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