Risk analysis of rainstorm waterlogging on residences in Shanghai based on scenario simulation

Abstract

Due to special geographical location and climate, the waterlogging has always been one of the most serious hazards in Shanghai. Residences in the inner city are prone to be damaged by waterlogging hazards. This paper describes the risk analysis of rainstorm waterlogging on residences in Shanghai. First, a rainstorm scenario of 50-year return period was simulated with the rainstorm simulation model from Shanghai Flood Risk Information Center. Each residence was ranked according to its degree of exposure indicated by the inundation depth of that residence, and an exposure analysis model was then built. It is found from the exposure analysis that residences in the sub-districts like Linfen Road, Pengpu Village, Gonghe New Village, Hongqiao Road, Xianxia Road, Xinhua Road, and Zhenru Town are at high-exposure level. Whereas residences in other sub-districts like Gaojing Town, Siping Road, Huaihai Road, Yuyuan, Waitan, Caojiadu, Nanjing East Road, etc. are at low-exposure level. Second, given the characteristics of residences in waterlogging, the vulnerability of residences was expressed as the proportion of old-style residences to total residences. The results show that residences in Yuyuan, Xiaodongmen, Waitan, Nanjing East Road, Laoximen, Zhapu Road, North Station, and Tilanqiao are the most vulnerable ones, while there is no vulnerability in Fenglin Road, Kongjiang Road, Liangcheng New Village, Quyang Road, Siping Road, and Xianxia Road due to the absence of old-style residences. Finally, a model has been built from a systematic perspective and then waterlogging risk analysis was quantified by multiplying the exposure value with vulnerability value of residences. The results reveal that Laoximen, Tilanqiao, Dinghai Road, North Station, Tianping Road, Hongmei Road, Hunan Road, and Xiaodongmen are at high-risk level. The systemic risk model is a simple tool that can be used to assess the relative risk of waterlogging in different regions and the results of risk analysis are applicable to prevention and mitigation of waterlogging for Shanghai Municipal Government.