Li Jingge, Yuan Lina, Hu Yuchao, Xu Ao, Cheng Zhixiang, Song Zijiang, Zhang Xiaowen, Zhu Wantian, Shang Wenbo, Liu Jiaye, Liu Min
Key Laboratory of Geographic Information Science, Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, China; Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China.
Key Laboratory of Geographic Information Science, Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, China; Key Laboratory of Spatial-temporal Big Data Analysis and Application of Natural Resources in Megacities, Ministry of Natural Resources, Shanghai 200241, China; School of Geospatial Artificial Intelligence, East China Normal University, Shanghai 200241, China.
Sci Total Environ. 2024 Dec 1;954:176372. doi: 10.1016/j.scitotenv.2024.176372. Epub 2024 Sep 21.
Urban flooding threatens residents and their property, necessitating timely and accurate flood simulations to enhance prevention measures. However, as a megacity, Shanghai presents a complex underlying surface that proves challenging to assess accurately in existing studies. To simulate the dynamic flooding caused by Typhoon In-Fa in Shanghai from July 23rd to 28th 2021, we employed the LISFLOOD hydrodynamic model with multi-source data and validated the flooded area using the S1FLOOD deep learning model with Sentinel-1 satellite imagery. Based on simulated flood results and a flood depth classification system, we quantified the impacts of flood inundation on population, land use, and buildings. Key findings include: (1) The most severe flooding period in Shanghai occurred on July 25th and 26th 2021. (2) The LISFLOOD model effectively captured the extent of inundation, with the very-high flood depth zone covering 98.07 % of the area identified as flooded by the S1FLOOD and Sentinel-1. (3) Peak-affected individuals were recorded on July 25th 2021. (4) Farmland experienced the most extensive flooding among land use types, while residential buildings were notably affected among building types. Our study reconstructed the spatiotemporal dynamics of Typhoon In-Fa-induced flooding in Shanghai. We mapped the spatial extent and water depths, revealing the dynamic impacts of inundation on population, land use, and buildings across urban areas. This comprehensive framework for flood simulation and inundation impact analysis offers a valuable approach to improve urban flood emergency response.
城市内涝威胁着居民及其财产安全,因此需要及时、准确的洪水模拟来加强预防措施。然而,作为一座特大城市,上海的下垫面情况复杂,在现有研究中难以准确评估。为了模拟2021年7月23日至28日台风“烟花”在上海引发的动态洪水,我们使用了具有多源数据的LISFLOOD水动力模型,并利用S1FLOOD深度学习模型和哨兵-1号卫星图像对淹没区域进行了验证。基于模拟的洪水结果和洪水深度分类系统,我们量化了洪水淹没对人口、土地利用和建筑物的影响。主要发现包括:(1)上海最严重的洪水期发生在2021年7月25日和26日。(2)LISFLOOD模型有效地捕捉了淹没范围,极高洪水深度区域覆盖了S1FLOOD和哨兵-1号识别出的淹没区域的98.07%。(3)受影响人数峰值出现在2021年7月25日。(4)在土地利用类型中,农田遭受的洪水最为广泛,而在建筑类型中,住宅建筑受到的影响尤为显著。我们的研究重建了台风“烟花”在上海引发的洪水的时空动态。我们绘制了空间范围和水深图,揭示了淹没对城市地区人口、土地利用和建筑物的动态影响。这种洪水模拟和淹没影响分析的综合框架为改善城市洪水应急响应提供了一种有价值的方法。