A probabilistic assessment of urban flood risk and impacts of future climate change

气候变化 大洪水 环境科学 洪水(心理学) 洪水风险评估 概率逻辑 风险评估 百年一遇洪水 概率风险评估 风险分析(工程) 地理 地质学 计算机科学 统计 数学 业务 海洋学 考古 计算机安全 心理治疗师 心理学
作者
Wen Liu,Qi Feng,Bernard A. Engel,Tengfei Yu,Xin Zhang,Yuguo Qian
出处
期刊:Journal of Hydrology [Elsevier BV]
卷期号:618: 129267-129267 被引量:62
标识
DOI:10.1016/j.jhydrol.2023.129267
摘要

Climate change has resulted in the increased frequency of extreme storms and associated flood risk uncertainties in urban areas, which brings more challenges to future urban flood prevention. Assessing future trends in probabilistic urban flood risk can improve uncertainty estimation of urban floods under climate change, and thus it is becoming increasingly urgent and important. In this study, a probabilistic risk assessment approach of urban flooding was developed to quantify the probabilistic flood risk in urban areas and assess the impacts of future climate change on flood risk. Results showed probabilistic urban flood risk under the historical condition ranged from 0 to 0.508 with a mean value of 0.104, indicating probabilistic urban flood risk was at the medium risk level. The areas where probabilistic flood risk reached the medium risk level under historical condition accounted for 34.3 % of the study area. The ranges of probabilistic flood risk in the study area were 0–0.588 and 0–0.601 with mean values of 0.126 and 0.131 under SSP2-4.5 and SSP5-8.5 climate change scenarios, respectively. The areas where probabilistic flood risk reached the high risk level respectively accounted for 17.1 % and 32.7 % of the study area for SSP2-4.5 and SSP5-8.5 scenarios. Compared with the historical period, probabilistic risk of urban flooding in the study area was projected to increase by 51.3 % and 67.4 % on average for SSP2-4.5 and SSP5-8.5 scenarios, respectively. The largest increase of probabilistic flood risk was approximately 200 % and 250 % for the SSP2-4.5 and SSP5-8.5 scenarios, respectively, which mainly occurred for forestlands and grasslands. These results can effectively improve the uncertainty estimation of risk trends for urban floods under future climate change, and thus better-informed decision making for urban flood prevention.
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