Exploring the driving factors of urban flood at the catchment Scale: A case study of multitype megacities in China

Abstract

With the increasing frequency of extreme rainfall events worldwide, urban flood in densely populated and economically vibrant urban areas has become a pressing issue drawing global attention. Catchments are fundamental units for understanding urban hydrological processes and managing flood. Current research often lacks an investigation into the importance and spatial differences of driving factors for urban flood at the catchment scale, particularly for different types of cities. This study uses 15 megacities in China as case studies and establishes a comprehensive system of driving factors. It categorizes cities based on population levels, climate zones, and spatial locations. The study begins with hotspot analysis to identify areas with high occurrences of urban inundation. Subsequently, it employs Geographic Weighted Regression (GWR), a spatial analysis technique, to unveil the mechanisms driving the spatial distribution of inundation points in different types of cities and the spatial heterogeneity of driving factors at the urban catchment scale. The research findings, based on multiple cities, demonstrate that urban inundation points exhibit clear spatial clustering characteristics. While impervious surfaces and urban buildings are generally more important than factors such as landscape patterns and population density, the importance and explanatory power of driving factors for the spatial distribution of urban inundation vary across different cities and even within different catchments of the same city. The categorization of cities based on population levels and spatial locations demonstrates stronger regularities in driving mechanisms compared to categorization based on climate zones. The study’s results further underscore the complex driving mechanisms behind the spatial distribution of urban inundation. This highlights the need for flood management strategies that consider both universal patterns of inundation occurrence and the specific characteristics of local driving factors to develop targeted and effective mitigation measures.