How to develop site-specific waterlogging mitigation strategies? Understanding the spatial heterogeneous driving forces of urban waterlogging

Abstract

Urban waterlogging seriously threatens urban sustainable development and human life. The effects of various landscape elements on urban waterlogging have been extensively documented. However, less attention is deserved to the spatial heterogeneity effects of urban landscape elements on urban waterlogging. The spatial pattern of dominant driving forces and how the interactive effects of landscape elements affect urban waterlogging with different environmental configurations have not been well examined. These shortcomings have hindered the development of target-specific urban waterlogging mitigation strategies. To shed some light on this topic, an innovative method that integrated the boruta algorithm, cubist regression tree, and geographical detector model is presented to investigate the spatial heterogeneous mechanisms of urban waterlogging and map the waterlogging dominant driving forces with different local conditions. The results show that the boruta algorithm proposed in this study introduces shadow variables as a benchmark, thus enabling an unbiased and stable selection of representative waterlogging driving factors based on local conditions. By comparing with two other commonly used regression methods (global regression model, spatial lag model), the cubist regression tree divides the urban waterlogging space into multiple homogeneous subgroups to quantify the spatial non-stationarity relationship and spatially explicit the local driving forces in Guangzhou and Shenzhen, with the adjusted R2 of 0.79 and 0.88. The geographical detector model denotes that waterlogging magnitude within different subgroups is affected by different dominant factors. Even for the same dominant factor, its contribution to waterlogging varies considerably in different subgroups. The independent contribution of the dominant factor in Guangzhou was 23.28%–57.82%, while in Shenzhen it ranged from 25.95% to 53.59%. In addition to the dominant factor of each subgroup, it is noteworthy that in some subgroups the combined effect of different representative factors on waterlogging is significantly stronger than the contribution of their dominant factors. In view of this, urban planners and local authorities need to comprehensively consider the interaction effect between representative factors, which develop urban waterlogging mitigation strategies that integrate multiple factors. The results from this study extend our scientific understanding of the site-specific mechanism of urban waterlogging, which facilitates the implementation of more targeted and effective mitigation strategies, rather than a “one-size-fits-all” policy.