Urban rainstorm and waterlogging scenario simulation based on SWMM under changing environment

Wang, Simin; Jiang, Rengui; Yang, Mingxiang; Xie, Jiancang; Wang, Yinping; Li, Wen

doi:10.1007/s11356-023-31027-0

Cited by 4 publications

(1 citation statement)

References 38 publications

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“…The assessment of urban infrastructure resilience (Table 1) was conducted by combining six indicators: number of hospital beds per ten thousand population (C1), per capita refuge area (C2), density of road network in built district (C3), communication coverage (C4), density of sewers in built district (C5), and central city pumping capacity (C6). Density of sewers and pumping capacity are crucial indicators representing the capability to discharge rainwater effectively [50,51]. In urban areas, rainfall-induced flooding directly impacts the traffic condition of roads.…”

Section: Secondary Indicatorsmentioning

confidence: 99%

Characteristics of Urban Flood Resilience Evolution and Analysis of Influencing Factors: A Case Study of Yingtan City, China

Yuan,

Wang,

Wang

et al. 2024

Water

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Intense climate change and rapid urbanization have increased the risk of urban flooding, seriously affecting urban economic and social stability. Enhancing urban flood resilience (UFR) has required a new solution to cope with urban flood disasters. In this study, taking Yingtan city as an example, a system of indicators for evaluating UFR was constructed, with 17 representative indicators, comprising three subsystems: socio-economic, ecological, and infrastructural. A hybrid model combining Fuzzy Analytic Hierarchy Process (FAHP), Entropy Weight Method (EWM), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was applied, to develop an index-based measurement to compare and evaluate UFR, and Gray Relational Analysis (GRA) was used to discover the main factors affecting UFR. In addition, the natural discontinuous method was innovatively used to divide the UFR grade interval into levels, and the grade change was evaluated based on the TOPSIS method. The results showed that (1) From 2010 to 2022, the UFR in Yingtan City increased by 80.69%, and the factors affecting UFR were highly correlated with urban infrastructure development; however, the ecological resilience in the subsystem showed a fluctuating downward trend because of the influence of the surface area of lakes and rivers; (2) The grades of UFR for Yingtan City increased from Level III (2010 and 2016) to Level IV (2022), with local financial expenditures and the age structure of the population being the main factors currently limiting the development of UFR. The study provides a theoretical basis for the construction of an indicator system for assessing the UFR of Yingtan and proposes practical improvement directions for UFR.

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Section: Secondary Indicatorsmentioning

confidence: 99%