唐清竹, 徐宗学, 王京晶, 陈浩, 杨芳. 深圳河流域城市洪涝风险分析[J]. 水力发电学报, 2023, 42(6): 13-22.
引用本文: 唐清竹, 徐宗学, 王京晶, 陈浩, 杨芳. 深圳河流域城市洪涝风险分析[J]. 水力发电学报, 2023, 42(6): 13-22.
TANG Qingzhu, XU Zongxue, WANG Jingjing, CHEN Hao, YANG Fang. Urban flood risk assessment for Shenzhen River basin[J]. JOURNAL OF HYDROELECTRIC ENGINEERING, 2023, 42(6): 13-22.
Citation: TANG Qingzhu, XU Zongxue, WANG Jingjing, CHEN Hao, YANG Fang. Urban flood risk assessment for Shenzhen River basin[J]. JOURNAL OF HYDROELECTRIC ENGINEERING, 2023, 42(6): 13-22.

深圳河流域城市洪涝风险分析

Urban flood risk assessment for Shenzhen River basin

  • 摘要: 日益严峻的城市洪涝灾害严重威胁着城市安全和可持续发展,准确评估城市洪涝风险对于保护人民群众生命财产安全至关重要。本研究基于元胞自动机洪水模拟模型构建深圳河流域洪涝仿真模型,采用综合主观层次分析法和客观权重赋权法的博弈论组合赋权法对各指标赋权,评估和预测不同降雨情景下深圳河流域城市洪涝风险。结果表明:深圳河流域城市洪涝高风险区存在典型的时空差异,随着暴雨重现期的增加,面积占比从2年一遇的0.20%增加到100年一遇的0.82%;中低风险区面积占比从2年一遇的67.4%减少到100年一遇的64.7%。研究结果可有效识别深圳河流域洪涝风险分布,为完善深圳河流域洪涝综合防治体系,提升城市洪涝防御能力和韧性提供借鉴与参考。

     

    Abstract: The increasing urban flooding threatens urban safety and sustainable development, and further improvement in the accuracy of urban flood risk simulation and assessment are essential to protect people’s lives and properties. In this study, a flood simulation model is developed for the Shenzhen River basin based on a Cellular Automata flood simulation model, and inundation depths are simulated for the design rainfall conditions of this basin. Using the Hazard-Vulnerability risk assessment framework, we consider its different rainfall scenarios and assign weights to its different urban flood risks, through a game theory-based combination of weights determined by the analytic hierarchy process and the Criteria Importance Though Intercrieria Correlation methods. Then, its urban flood risks are evaluated and predicted. The results show typical temporal and spatial differences occur over the high-risk areas of urban flooding in the basin. With an increasing rainfall recurrence period, the ratio of the risky area increases,such as from 0.20% of a 2-year rainfall event to 0.82% of a 100-year event; the ratio of the medium-low risk area decreases, such as from 67.4% to 64.7% in terms of the two events. The results of this study can be visualized to show the distribution of flood risks over the basin, helping improve its flood control system and enhance the urban flood prevention capability and resilience of the city.

     

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