Evaluate cities’ urban water resources system resilience along a river and identify its critical driving factors

An, Malaviya; Song, Mengfei; He, Weijun; Huang, Jin; Fang, Xue

doi:10.1007/s11356-022-23271-7

Cited by 8 publications

(2 citation statements)

References 84 publications

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“…It comprises the following four main modules: the factor detector, risk detector, interaction detector, and ecological detector (Wang & Hu, 2012). Currently, the GDM has been utilized in a variety of fields, such as public services (Lan et al, 2018), land use research (Peng et al, 2020), ecological environment research (An et al, 2022; Chen et al, 2022). We divided the Jinghe River Basin into 512 grids, extracted the above river network structures, topographic features, vegetation features, and IC values, and quantified the explanatory power and interaction of the influencing factors on connectivity with the assistance of the GD package in R4.2.1 (Song et al, 2020).…”

Section: Methodsmentioning

confidence: 99%

Elucidating the response mechanisms of hydrological and sediment connectivity to the river network structure, vegetation, and topographic features in the Jinghe River Basin

Liu,

Wu,

Guo

et al. 2024

Land Degrad Dev

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The management of basin water and soil resources greatly benefits from investigating the spatial changes and primary determinants of hydrological and sediment connectivity from perspectives of topographic features, vegetation characteristics, structural features of river networks. However, quantifying the effects of influencing factors and their interactions on connectivity is still a challenge in the field of studies of surface processes. To address this challenge, we applied the geographical detector model (GDM) and random forest (RF) to quantify the relative importance and explanatory power of topographic factors, vegetation features, and river network structure on the hydrological connectivity and sediment connectivity and to clarify the interaction mechanism of various factors in the Jinghe River Basin. Results indicate that from 2005 to 2020, the mean value of hydrological connectivity (ICH) witnessed a decreasing trend from −5.47 to −5.58. Similarly, the mean value of sediment connectivity (ICS) declined over the same period from −9.74 to −10.05, with high values registered in valleys and low values in plains. The river density (D) and hydrological weight factor (normalized SCS‐CN) exhibited a greater spatial explanatory power on ICH than other parameters, reaching 0.149 and 0.182, respectively. The vegetation factor (0.299) and sediment weight factor (0.410) manifested considerably more influence over ICS. Spatial integration between relevant elements can enhance our understanding of basin connectivity. Interactions between vegetation, topography, and river network structure provide more explanatory power compared to interactions among same‐type features. Ultimately, this study offers a theoretical template for understanding the management of soil and water resources in the Jinghe River Basin, as well as the spatial variability in soil erosion.

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Section: Methodsmentioning

confidence: 99%

Elucidating the response mechanisms of hydrological and sediment connectivity to the river network structure, vegetation, and topographic features in the Jinghe River Basin

Liu,

Wu,

Guo

et al. 2024

Land Degrad Dev

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“…Zhou [33] used the entropy weight method to calculate the level of WRSR in 11 provinces and cities in the Yangtze River Basin based on the process attributes of resilience and its logical correlation with the PSR model. An [34] constructed an "economy-social development-infrastructure-water resources-ecological environment" WRSR assessment framework from three stages: resistance, resilience, and adaptability. Sun and Meng [35] further improved the evaluation index system of WRSR, taking into account culture, system, and other "soft power" factors.…”

Section: Introductionmentioning

confidence: 99%

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

Li,

He,

Jiang

et al. 2024

Water

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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.

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Study on spring drought in cold and arid regions based on the ANOVA projection pursuit model

Pei,

Hao,

et al. 2024

Ecological Indicators

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Evaluate cities’ urban water resources system resilience along a river and identify its critical driving factors

Cited by 8 publications

References 84 publications

Elucidating the response mechanisms of hydrological and sediment connectivity to the river network structure, vegetation, and topographic features in the Jinghe River Basin

Elucidating the response mechanisms of hydrological and sediment connectivity to the river network structure, vegetation, and topographic features in the Jinghe River Basin

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

Study on spring drought in cold and arid regions based on the ANOVA projection pursuit model

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