Analysis of Spatial and Temporal Evolution of Regional Water Resources Carrying Capacity and Influencing Factors—Anhui Province as an Example

Global climate change has precipitated a surge in urban flooding challenges, prompting the imperative role of green infrastructure (GI) as the linchpin of sponge city construction to enhance urban sustainability and resilience. But the evaluation of urban stormwater resilience faces challenges due to the lack of a comprehensive evaluation framework taking the intrinsic features of the resilience system into account and the insufficient coverage of alternative scenarios’ performance under multiple rainfall return periods. This study, focusing on Fengxi New City, China, evaluates the suitability of GI (i.e., green roofs, rain gardens, and permeable pavements) and constructs a stormwater management model (SWMM) for urban stormwater hydrological simulation. This study also establishes a comprehensive urban stormwater resilience evaluation system and uses quantitative methods to unify the performances of scenarios under different rainfall return periods. Our analytical findings elucidate that the suitability of GI is predominantly concentrated in the northern and western areas of the study area, with the smallest suitable area observed for permeable pavements. Divergent GIs exhibit disparate performances, with rain gardens emerging as particularly efficacious. Importantly, the combination of multiple GIs yields a synergistic enhancement in resilience, underscoring the strategic advantage of adopting a diverse and integrated approach to GI implementation. This study facilitates a deeper understanding of urban stormwater resilience and assists in informed planning decisions for GI and sponge cities.

Section: Using the Entropy Methods To Determine Weights Of Different ...mentioning

confidence: 99%

Scenario-Based Green Infrastructure Installations for Building Urban Stormwater Resilience—A Case Study of Fengxi New City, China

Mao,

Li,

Bai

et al. 2024

“…The demand for regional water resources is closely tied to population and economic factors, requiring a proportional allocation of water resources to match population and GDP [42]. The Gini coefficient is employed to quantify how well regional water resources correspond to economic factors [43]. The Gini coefficient measures the distribution imbalance of water resources in a region, which can indicate the fairness of that distribution [44].…”

Section: Gini Coefficient Of Water Distributionmentioning

confidence: 99%

Joint Optimization of Urban Water Quantity and Quality Allocation in the Plain River Network Area

Zhao,

Fang,

Wang

et al. 2024

Cities located in the plain river network area possess abundant water resources. However, due to urbanization and industrialization, there is a severe water shortage problem caused by poor water quality. To overcome this issue, a multi-objective optimal allocation model of water quantity and quality is proposed. The model considers regional water resources, economic, social, and environmental requirements and uses the NSGA-II genetic algorithm for model solution. Furthermore, to evaluate and analyze the degree of spatial equilibrium of regional water resources and how it relates to economic factors, the study uses the spatial equilibrium theory of water resources and the Gini coefficient of water resources. Jingjiang, a city in Jiangsu Province characterized by a typical plain river network area, was selected as the study area. The results of the optimal allocation of water resources in Jingjiang City show that: (1) total water consumption and chemical oxygen demand (COD) emissions for the current planning period are within their respective limits. In addition, the implementation of the water conservation program has resulted in a 5% reduction in total water shortages and a reduction of COD emissions by 1276 tons, (2) the structure of the water supply in Jingjiang City has been optimized; more than 90% of Ⅳ~V surface water is used for agriculture, and the domestic water supply is mainly from transit water, which effectively ensures that high-quality water is used in the domestic water supply, (3) the spatial equilibrium coefficient of water resources per sub-area is between 0.33 and 0.74, indicating an unbalanced or almost unbalanced level. The application of a water conservation program has resulted in the improvement of the spatial equilibrium level of water resources in each sub-area, with an overall spatial equilibrium of 0.64, indicating a more balanced level; the degree of matching of water resources with population, GDP, and land area is at the matching level, (4) according to the Gini coefficient of the distribution of water resources, the plains river network area displays a better match between water resources and economic and social factors of each water receiving area, thanks to its unique geographical location and natural conditions. This study can serve as a decision-making reference for addressing the urban water quality water shortage problem in the plain river network area.

“…In terms of definition and connotation, early studies often used natural elements such as water scarcity, sustainable utilization levels, ecological limits, or biosphere limits to describe water resource carrying capacity, primarily guiding agricultural production [6]. With the acceleration of urbanization and the increasing proportion of national industrial output, the definition of water resource carrying capacity has expanded to include demographic and economic factors to better explain the urban environment [7,8]. Currently, water resource carrying capacity is mainly summarized as "the maximum amount of water resources that can be exploited in an ecological region at a given stage of socio-economic development, focusing on the interactions between water resources and socio-economics" [9].…”

Section: Introductionmentioning

confidence: 99%

Spatial and Temporal Divergence of Water Resource Carrying Capacity in Hubei Province, China, from the Perspective of Three Major Urban Agglomerations

Liu,

Bao

2024

Water resource carrying capacity is indispensable for sustainable development, acting as a crucial determinant for harmonizing ecological preservation with socio-economic development. This study centers on Hubei Province, which is an important water conservation area in the Yangtze River Basin and is one of the core water source areas for the South-to-North Water Diversion Project, and evaluates the water resource carrying capacity of the three major urban agglomerations in Hubei Province from 2005 to 2020 based on the four dimensions of water resources, economics, society, and ecology, using the entropy weighting method and the TOPSIS model to construct an evaluation index system. We then employ the kernel density estimation method, ArcGIS visualization, and the Dagum Gini coefficient method to perform a comprehensive analysis of spatial and temporal differences, dynamic evolution, and contribution sources. The results show that (1) the water resource carrying capacity of Hubei Province as a whole increased from a severe overload to overload level during the study period. The water resource carrying capacity of the three major urban agglomerations shows a regional distribution pattern where the Yi-Jing-Jing-En agglomeration’s capacity surpasses that of the Wuhan urban agglomeration, which is bigger than Xiang-Shi-Sui-Shen urban agglomeration. A lower ecological water use rate primarily constrains the enhancement of the carrying capacity of water resources in Hubei Province. (2) The kernel density estimation reveals an increase in the overall water resource carrying capacity across Hubei Province’s three major urban agglomerations during the study period, alongside a pronounced trend towards polarization. (3) While the overall Gini coefficient, indicating an imbalance in water resource carrying capacity in Hubei Province, remains high, it demonstrates a declining trend, suggesting a growing disparity in water resource carrying capacity across the province’s three major urban agglomerations. Hubei Province’s water resource carrying capacity faces challenges of an overall imbalance and localized vulnerability. Strategies should aim to enhance synergy, address these deficiencies directly, and devise targeted measures tailored to the distinct features of various urban clusters.