Abstract
Understanding the driving factors and assessing the risk of rainstorm waterlogging are crucial in the sustainable development of urban agglomerations. Few studies have focused on rainstorm waterlogging at the scale of urban agglomeration areas. We used the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China as a case study. Kernel density estimation (KDE) and spatial autocorrelation analysis were applied to study the spatial distribution characteristics of rainstorm waterlogging spots during 2013–2017. A geographical detector (GD) and geographically weighted regression (GWR) were used to discuss the driving mechanism of rainstorm waterlogging by considering eight driving factors: impervious surface ratio (ISR), mean shape index of impervious surface (Shape_MN), aggregation index of impervious surface (AI), fractional vegetation cover (FVC), elevation, slope, river density, and river distance. The risk of rainstorm waterlogging was assessed using GWR based on principal component analysis (PCA). The results show that the spatial distribution of rainstorm waterlogging in the GBA has the characteristics of multicenter clustering. Land cover characteristic factors are the most important factors influencing rainstorm waterlogging in the GBA and most of the cities within the GBA. The rainstorm waterlogging density increases when ISR, Shape_MN, and AI increase, while it decreases when FVC, elevation, slope, and river distance increase. There is no obvious change rule between rainstorm waterlogging and river density. All of the driving factors enhance the impacts on rainstorm waterlogging through their interactions. The relationships between rainstorm waterlogging and the driving factors have obvious spatial differences because of the differences in the dominant factors affecting rainstorm waterlogging in different spatial positions. Furthermore, the result of the risk assessment of rainstorm waterlogging indicates that the southwest area of Guangzhou and the central area of Shenzhen have the highest risks of rainstorm waterlogging in GBA. These results may provide references for rainstorm waterlogging mitigation through urban renewal planning in urban agglomeration areas.
Highlights
The geographically weighted regression (GWR) model was used to reflect the non-stationarity of the driving factors impacting urban rainstorm waterlogging and to assess the risk of rainstorm waterlogging in the Greater Bay Area (GBA) based on principal component analysis (PCA)
This paper took into account the complex geographic spatial distribution characteristics of large-scale regions and analyzed the impact mechanisms of rainstorm waterlogging in GBA comprehensively
The rainstorm waterlogging is positively related to impervious surface ratio (ISR), Shape_MN, and AI, while negatively related to fractional vegetation cover (FVC), elevation, slope, and river distance
Summary
With the development of economic globalization and regional economic integration, the urbanization level has led to a new stage of urban agglomeration, which is a highly developed spatial form of integrated cities instead of a single city [1,2], and the natural surfaces of urban areas have undergone a significant and dramatic transformation, usually as a result of decreases in the natural vegetation cover and increases in impervious surfaces, which prevents rainwater from infiltrating into the soil [3,4,5]. Coupled with global climate change, subsequent waterlogging following rainstorms has become a serious problem in urban agglomeration areas worldwide [6]; waterlogging badly affects human life and property, and causes economic losses and environmental pollution [7,8].
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