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Driving Factors and Risk Assessment of Rainstorm Waterlogging in Urban Agglomeration Areas: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area, China

1
School of Geography, South China Normal University, Guangzhou 510631, China
2
Surveying and Mapping Institute, Lands and Resource Department of Guangdong Province, Guangzhou 510500, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Enrico Creaco
Water 2021, 13(6), 770; https://doi.org/10.3390/w13060770
Received: 25 January 2021 / Revised: 1 March 2021 / Accepted: 7 March 2021 / Published: 12 March 2021
(This article belongs to the Section Urban Water Management)
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. View Full-Text
Keywords: Guangdong-Hong Kong-Macao Greater Bay Area; rainstorm waterlogging; driving factor; interaction detection; spatial non-stationarity Guangdong-Hong Kong-Macao Greater Bay Area; rainstorm waterlogging; driving factor; interaction detection; spatial non-stationarity
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MDPI and ACS Style

Liu, F.; Liu, X.; Xu, T.; Yang, G.; Zhao, Y. Driving Factors and Risk Assessment of Rainstorm Waterlogging in Urban Agglomeration Areas: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area, China. Water 2021, 13, 770. https://doi.org/10.3390/w13060770

AMA Style

Liu F, Liu X, Xu T, Yang G, Zhao Y. Driving Factors and Risk Assessment of Rainstorm Waterlogging in Urban Agglomeration Areas: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area, China. Water. 2021; 13(6):770. https://doi.org/10.3390/w13060770

Chicago/Turabian Style

Liu, Fan; Liu, Xiaoding; Xu, Tao; Yang, Guang; Zhao, Yaolong. 2021. "Driving Factors and Risk Assessment of Rainstorm Waterlogging in Urban Agglomeration Areas: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area, China" Water 13, no. 6: 770. https://doi.org/10.3390/w13060770

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