Assessment of urban flood resilience based on the socio-ecological composite index model: a case study in Wuhua District, Kunming City, China

Abstract

Global climate change and rapid urbanization have increased the frequency of flooding, making urban flood resilience a critical objective. This article introduces a methodology for assessing urban flood resilience, utilizing a social-ecological synthesis index that integrates geographical and temporal data with Geographic Information System (GIS). The study focuses on ten administrative subdistricts in Wuhua District, Kunming City, China, and selects 18 social-ecological indicators. These indicators, chosen from social and ecological perspectives, are weighted using the entropy weight method to determine their significance in the assessment system. By combining scores for each subdistrict, the study quantifies flood resilience and creates a spatial distribution map using ArcGIS. Key findings reveal that out of the ten administrative subdistricts, five in Wuhua District, particularly in the core urban area of Kunming, demonstrate strong overall flood resilience. Influenced by social-ecological indicators, there is significant spatial differentiation in flood resilience within Wuhua District, with a decreasing trend radiating from the city center to areas farther from the urban core. The research indicates that regions with well-established transportation infrastructure, a wide distribution of government institutions, improved water management facilities, and a substantial population with higher education levels contribute significantly to enhancing urban flood resilience. HIGHLIGHTS Analyzing urban flood resistance capacity from the perspective of resilience.; Considering the performance of both social and ecological dimensions in urban flood resilience.; Quantifying urban flood resilience through comprehensive index method.; Using geographic information technology to analyse the spatial characteristics of urban flood resilience.;

Keywords

• entropy weight method
• flood resilience
• gis analysis
• spatiotemporal analysis
• weighting indicators