Explicit the urban waterlogging spatial variation and its driving factors: The stepwise cluster analysis model and hierarchical partitioning analysis approach

Qifei Zhang; Zhifeng Wu; Guanhua Guo; Hui Zhang; Paolo Tarolli

doi:10.1016/j.scitotenv.2020.143041

Explicit the urban waterlogging spatial variation and its driving factors: The stepwise cluster analysis model and hierarchical partitioning analysis approach

Sci Total Environ. 2021 Apr 1:763:143041. doi: 10.1016/j.scitotenv.2020.143041. Epub 2020 Oct 16.

Authors

Qifei Zhang¹, Zhifeng Wu², Guanhua Guo³, Hui Zhang⁴, Paolo Tarolli⁵

Affiliations

¹ Dept. of Land, Environment, Agriculture and Forestry, University of Padova, 35020 Legnaro, PD, Italy; School of Geographical Sciences, Guangzhou University, 510006 Guangzhou, Guangdong Province, China.
² School of Geographical Sciences, Guangzhou University, 510006 Guangzhou, Guangdong Province, China; Southern Marine Science and Engineering Guangdong Laboratory, 511458 Guangzhou, Guangdong Province, China.
³ School of Geographical Sciences, Guangzhou University, 510006 Guangzhou, Guangdong Province, China.
⁴ College of Surveying and Geo-informatics, North China University of Water Resources and Electric Power, 450046 Zhengzhou, Henan Province, China.
⁵ Dept. of Land, Environment, Agriculture and Forestry, University of Padova, 35020 Legnaro, PD, Italy. Electronic address: paolo.tarolli@unipd.it.

PMID: 33138988
DOI: 10.1016/j.scitotenv.2020.143041

Abstract

Urban waterlogging is a hydrological cycle problem that seriously affects people's life and property. Characterizing waterlogging variation and explicit its driving factors are conducive to prevent the damage of such disasters. Conventional methods, because of the high spatial heterogeneity and the non-stationary complex mechanism of urban waterlogging, are not able to fully capture the urban waterlogging spatial variation and identify the waterlogging susceptibility areas. A more robust method is recommended to quantify the variation trend of urban waterlogging. Previous studies have simulated the waterlogging variation in relatively small areas. However, the relationship between variables is often ignored, which cannot comprehensively reveal the dominant drivers affecting urban waterlogging. Therefore, a novel approach is proposed that combined stepwise cluster analysis model (SCAM) and hierarchical partitioning analysis (HPA) within a general framework and verifies the applicability through logistic regression, artificial neural network, and support vector machine. According to the dominant driving factors, different simulation scenarios are established to analyze waterlogging density variation. Results found that the SCAM provides accurate and detailed simulated results both in urban centers where waterlogging frequently occurs and urban fringe with few waterlogging events, which shows an excellent performance with a high classification accuracy and generalization capability. HPA detected that the impervious surface abundance (28.07%), vegetation abundance (20.80%), and cumulate precipitation (16.25%) are the dominant drivers of waterlogging. This result suggests that priority should be given to controlling these three factors to mitigate the risk of waterlogging. It is interesting to note that under different urbanization and rainfall scenarios, the urban waterlogging susceptibility has a considerable variation. The watershed spatial location and watershed characteristics are relevant aspects to be considered in identifying and assessing waterlogging susceptibility, which provides original insights that urban waterlogging mitigation strategies should be developed according to different local conditions and future scenarios.

Keywords: Hierarchical partitioning analysis; Natural and anthropogenic factors; Scenario simulation; Stepwise cluster analysis model; Urban waterlogging.