An Evaluation of Urban Resilience to Flooding

Abstract

Flooding has become the natural disaster that causes the greatest losses, with urban flooding restricting the healthy development of cities. The ability to assess a city’s resilience to flooding is very important and would contribute to improving resilience and also help to inform planning and development. The aim of this study was to determine the key urban flood resilience indicators for three different Chinese cities (Wuhan, Nanjing, and Hefei) and to prioritize these for each city. A combined interpretive structure and network analysis method (ISM-ANP) model was used to evaluate and analyze the selected evaluation indicators. A four-level urban flood resilience evaluation network model was constructed to determine the interdependence between indicators and to calculate the priorities of the flood resilience indicators for the three cities. Overall, rescue capacity was found to be extremely important and was defined as the most important index. For Wuhan, indicators related to the distribution of waters were found to be more important, while for Nanjing, spatial planning and spatial structure of land use were found to be key priorities. In Hefei, the level of investment in infrastructure and the level of public resources occupy a more important position. The framework presented in this study contributes to the understanding of urban flood resilience and has the potential to be extended to other natural hazards.

Evaluation of Factors Found to Influence Urban Flood Resilience in China

“…The analysis of indicator hierarchy and importance is more adequate, and the ISM-ANP model relies on the personal experience, knowledge, and professional judgment of decision makers and lacks objective and realistic analysis [39] The flood resilience of 31 key flood control cities was assessed…”

“…The principle of the interpretative structural model is to establish an adjacency matrix according to the direct correlation between the elements, calculate the reachable matrix, and finally form a hierarchical multi-level recursive structural model. Its core is to use tools such as directed graphs and matrices to perform mathematical operations, analyze complex structures, and transform fuzzy ideas and opinions into intuitive models with good structural relationships [39]. The final model is expressed in the form of a simple directed topology graph.…”

“…Ecological resilience refers to the ability of the ecological environment system to resist, adapt, and restore the original structure by its own resilience when it is damaged [39]. The indicators selected in this dimension include the green coverage rate of built-up areas [18,57], the average annual rainfall [40,58], and the spatial structure of land use [59].…”

Review of Urban Flood Resilience: Insights from Scientometric and Systematic Analysis

“…In 2021, Xu WP used a model combining the interpretive-structure and network-analysis methods (ISM-ANP) to evaluate and analyze the selected evaluation indicators. The ranking of key indicators of flood resistance for Wuhan, Nanjing and Hefei in China is helpful to understand urban flood resilience [ 97 ]. Park K proposed a method to reduce urban flood damage and improve urban resilience, taking land use and building characteristics as evaluation indexes for urban flood-vulnerability analysis.…”

An Impacts-Based Flood Decision Support System for a Tropical Pacific Island Catchment with Short Warnings Lead Time

“…Flood-related events have been the most frequent natural hazard disaster type between 2000-2019, incurring the largest average annualized losses at a global scale compared with any other natural hazard [1][2][3][4]. Soft adaptation approaches such as early warnings systems are considered effective tools that enhance the safety and resilience of people to the impacts of flood events (e.g., [5][6][7][8]).…”