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极端暴雨威胁下的城市内涝风险预警系统研究

曹雪健 戚友存 李梦迪 杨志达 倪广恒

曹雪健, 戚友存, 李梦迪, 等. 2022. 极端暴雨威胁下的城市内涝风险预警系统研究[J]. 大气科学, 46(4): 1−12 doi: 10.3878/j.issn.1006-9895.2112.21191
引用本文: 曹雪健, 戚友存, 李梦迪, 等. 2022. 极端暴雨威胁下的城市内涝风险预警系统研究[J]. 大气科学, 46(4): 1−12 doi: 10.3878/j.issn.1006-9895.2112.21191
CAO Xuejian, QI Youcun, LI Mengdi, et al. 2022. Early Warning System of Urban Waterlogging under the Threat of Extreme Rainfall Events [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 1−12 doi: 10.3878/j.issn.1006-9895.2112.21191
Citation: CAO Xuejian, QI Youcun, LI Mengdi, et al. 2022. Early Warning System of Urban Waterlogging under the Threat of Extreme Rainfall Events [J]. Chinese Journal of Atmospheric Sciences (in Chinese), 46(4): 1−12 doi: 10.3878/j.issn.1006-9895.2112.21191

极端暴雨威胁下的城市内涝风险预警系统研究

doi: 10.3878/j.issn.1006-9895.2112.21191
基金项目: 国家重点研发计划项目2018YFC1507505,中国科学院百人计划
详细信息
    作者简介:

    曹雪健,男,1993年出生,博士研究生,主要从事城市水文气象研究。E-mail: caoxj17@163.com

    通讯作者:

    戚友存,E-mail: youcun.qi@igsnrr.ac.cn;倪广恒,E-mail: ghni@tsinghua.edu.cn

  • 中图分类号: P338

Early Warning System of Urban Waterlogging under the Threat of Extreme Rainfall Events

Funds: National Key Research and Development Program of China (Grant 2018YFC1507505), Hundred Talents Program of Chinese Academy of Sciences
  • 摘要: 传统内涝风险预报系统多基于单一降雨产品驱动城市水文水动力模型的模式,难以解决由于暴雨观测或数值模拟带来的不确定性问题。综合利用多源降雨(雷达、地面雨量计,地面雨滴谱)、积水观测数据,有利于提高内涝预报精度,改善风险空间描述。因此,为了进一步加强洪涝预测能力以更好地应对极端暴雨威胁,本研究提出了基于综合观测的城市内涝风险预警系统,并在北京市清河流域进行了初步实践和检验。该系统包含六个模块,融合了新兴的降雨积水观测技术,引入了主流的降雨临近预报方法,采用了成熟的城市雨洪模拟手段,可为道路交通提供实时的积水深度和风险等级,为城市内涝灾害应急管理提供内涝风险预测和预警产品。
  • 图  1  城市降雨综合观测设备:(a)雨量计;(b)雨滴谱仪;(c)天气雷达

    Figure  1.  Urban rainfall observation equipment: (a) Rain gauge; (b) disdrometer; (c) weather radar

    图  2  城市内涝灾害风险预警系统技术框架

    Figure  2.  Technical framework of the urban waterlogging warning system

    图  3  基于X波段双偏振雷达的混合降雨定量估计流程

    Figure  3.  Flowchart of the mixed quantitative rainfall estimation, based on X-band dual-polarization radar

    图  4  北京清河流域“7·15”暴雨(2018年)内涝灾害风险模拟及评估

    Figure  4.  Simulation and assessment of the rainstorm on 15 July 2018 waterlogging risk in the Qing River basin in Beijing

    图  5  X波段双偏振天气雷达降水反演的7个典型对流性降雨过程的降雨总量(彩色阴影,单位:mm)空间分布

    Figure  5.  Spatial distribution of total rainfall retrieved from the X-band dual-polarization weather radar in seven typical convective rainfall processes

    图  6  X波段双偏振雷达降雨反演产品评估

    Figure  6.  Evaluation of the X-band dual-polarization radar rainfall retrieval products

    图  7  7个典型对流性降雨过程的最大积水深度空间分布

    Figure  7.  Spatial distribution of the maximum ponding depth for seven convective rainfall processes

    图  8  7个典型对流性降雨过程的内涝灾害风险分级

    Figure  8.  Waterlogging risk distribution for seven convective rainfall processes

    表  1  基于积水深度的内涝灾害等级划分标准

    Table  1.   Classification standard of risk based on waterlogging depth

    风险等级积水深度/m危险程度
    1≥0.5可能造成人员伤亡
    2[0.3, 0.5)显著影响行人交通,大部分机动车无法通行
    3[0.15, 0.3)影响行人通行,车辆行驶缓慢
    4<0.15积水不会淹没路边路缘石,基本不影响行人和机动车通行
    下载: 导出CSV

    表  2  基于积水深度和积水时间的内涝灾害等级划分标准

    Table  2.   Classification standard of risk based on waterlogging depth and duration

    风险等级积水深度/m积水时间/min危险程度
    1>0.4城市交通、基础设施和各类建筑受到威胁
    2[0.3, 0.4)>15城市交通受到严重影响
    3[0.15, 0.3)>30城市交通不便
    4<0.15一般积水
    下载: 导出CSV

    表  3  2017~2018年北京清河流域典型降雨个例特性汇总

    Table  3.   Characteristic summary of typical rainfall cases in the Qing River basin in Beijing during 2017–2018

    降雨个例日期(协调世界时)流域平均降雨总量/mm
    流域平均降雨峰值/mm h−1
    风暴移动方向
    E12017年7月13~14日51.3220.95
    E22017年8月8日11.8223.54
    E32017年8月11~12日34.5922.78
    E42017年8月22日35.0511.12
    E52018年7月15~17日116.6429.42
    E62018年8月7~8日18.3621.30
    E72018年8月10~11日20.8926.62
    下载: 导出CSV
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  • 收稿日期:  2021-10-09
  • 录用日期:  2022-03-03
  • 网络出版日期:  2022-03-07

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