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.
School of Geographical Sciences, Guangzhou University, 510006 Guangzhou, Guangdong Province, China; Southern Marine Science and Engineering Guangdong Laboratory, 511458 Guangzhou, Guangdong Province, China.
Sci Total Environ. 2021 Apr 1;763:143041. doi: 10.1016/j.scitotenv.2020.143041. Epub 2020 Oct 16.
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.
城市内涝是一种严重影响人们生活和财产的水文循环问题。描述内涝变化并明确其驱动因素有利于预防此类灾害的破坏。由于城市内涝的空间异质性高和非平稳复杂机制，常规方法无法充分捕捉城市内涝的空间变化并识别易涝区。因此，建议采用更稳健的方法来量化城市内涝的变化趋势。先前的研究已经在相对较小的区域内模拟了内涝的变化。然而，这些研究往往忽略了变量之间的关系，无法全面揭示影响城市内涝的主导因素。因此，提出了一种新的方法，该方法将逐步聚类分析模型（SCAM）和分层分区分析（HPA）结合在一个通用框架内，并通过逻辑回归、人工神经网络和支持向量机验证了其适用性。根据主导驱动因素，建立不同的模拟情景来分析内涝密度的变化。结果表明，SCAM 在内涝频繁发生的城市中心和内涝事件较少的城市边缘都提供了准确和详细的模拟结果，表现出了出色的性能，具有较高的分类精度和泛化能力。HPA 检测到不透水面丰度（28.07%）、植被丰度（20.80%）和累积降水量（16.25%）是内涝的主导驱动因素。这一结果表明，应优先控制这三个因素，以降低内涝风险。有趣的是，在不同的城市化和降雨情景下，城市内涝易感性有相当大的变化。流域的空间位置和流域特征是识别和评估内涝易感性的相关方面，这提供了原始的见解，即应根据不同的当地条件和未来情景制定城市内涝缓解策略。
