A spatiotemporal framework for the joint risk assessments of urban flood and urban heat island

Abstract

Urban Flood (UF) and Urban Heat Island (UHI) become prevalent “urban diseases” currently. Previous studies usually regarded UF and UHI as separate issues that requires respective concentration; however, UF and UHI are inextricably connected so that they should be considered together. Hence, this paper proposes a spatiotemporal framework to link UF and UHI by developing an Urban Flood Heat Island (UFHI) index, taking the Greater Bay Area (GBA) in China as the study case. Random Forest (RF) classifier and Urban-Rural Dichotomy were selected to compute the spatiotemporal distribution of UF susceptibility (UFS) and UHI intensity (UHII), respectively. Theil-Sen Median Slope, Mann-Kendall Test, and Hurst analysis were used to explore the spatiotemporal variation pattern of the UF-UHI joint risk. Spatial Durbin Model was used to compute the correlation between UFS and UHII. RF regressor was employed to quantify the contribution of the common driving factors. We found: (1) UFS and UHII are spatiotemporally correlated, thus UHII can be used as a control variable for the refined UFS assessments, (2) the areas with the UF-UHI joint risk of persistently significant increase (∼7.9 %) majorly distributed in the central region, (3) according to the contributions of factors, UF and UHI in the GBA could be effectively alleviated by breaking the continuous impervious surfaces by introducing blue-green structures. This study offers a new perspective for the effective alleviation of UF and UHI, which would be helpful for the sustainable development of cities in developing countries where human and material sources are limited.

Keywords

• Urban Flood Heat Island index
• Urban Flood Susceptibility
• Urban Heat Island Intensity
• Random Forest
• Spatiotemporal analysis