大范围洪涝灾害影响下的交通网受损快速评估

Rapid Assessment of Traffic Inefficiency Under Flood Scenarios over Wide Regions

  • 摘要: 2021年7月,中国河南省遭遇极端强降雨天气,出现大范围洪涝灾害,河南省内各级交通网受到严重影响。为了快速、准确地获取极端气候条件下自然灾害对大范围交通网造成的损坏和运行影响情况,对哨兵1号、高分三号卫星遥感影像、欧洲中期天气预报中心的降雨和水汽数据、地质灾害调查数据等多源数据进行分析与融合,提出了大范围洪涝灾害影响下的交通网受损快速评估技术体系框架,解析了此轮河南暴雨引发的洪涝灾害发展过程,并评估了洪涝灾害对交通道路网危害及后续次生灾害的风险,利用空间可达性模型对路网受损后的连通性水平进行了评估。结果显示:此次洪涝灾害具有累计降雨量极大、高度集中且降水范围广的特点,河南省郑州市及周边地区的洪涝受灾面积约3 800 km2;三门峡市、登封市山区次生滑坡灾害发生可能性极高;公路交通网受洪涝灾害影响的道路总里程约为1 300.46 km;郑州等五市的公路网连通性下降了21.27%左右。其中,高速公路、国道、省道的连通性分别下降了34.22%、13.78%和14.86%。

     

    Abstract:
      Objectives  In July 2021, Henan Province of China suffered continuous extreme rainfalls, causing widespread flood and serious paralysis of the highway network. The flood caused by the heavy rainfalls brought enormous loss to people?s lives and properties in Henan Province.
      Methods  We collectively used a range of earth observations, namely remote sensing images from Sentinel-1 and Gaofen-3 satellites, rainfall and water vapor data from the European Centre for Medium-Range Weather Forecasts, and geohazard survey data to investigate and analyze the evolving process of the flood caused by the heavy rainfalls in Henan Province, and proposed a new technical framework for rapid assessment of traffic inefficiency under flood scenarios over wide regions.
      Results  The application of the new framework to the case in Henan Province suggests that: (1) The cumulative rainfall was highly concentrated, reaching a historic high level and affecting a wide region. (2) The total affected area in Zhengzhou City, Henan Province and its surrounding areas reached 3 800 km2. (3) The potential of secondary landslides in mountainous areas such as Sanmenxia City and Dengfeng City was highly increased.(4) About 1 300.46 km of major roads were affected by this flood, and the overall connectivity of major highway networks in Zhengzhou and its surrounding five cities decreased about 21.27%, with 34.22% for expressways, 13.78% for national highways, and 14.86% for provincial highways.
      Conclusions  This study shows how to integrate remote sensing, secondary landslide analysis, road network analysis, and other multi‑field technologies to construct a framework for traffic inefficiency assessment under natural hazards. It is believed that this framework could be applied to other hazardous events and provide valuable information for emergency rescue.

     

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