PROGRESS IN GEOGRAPHY ›› 2021, Vol. 40 ›› Issue (6): 1048-1059.doi: 10.18306/dlkxjz.2021.06.014

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A review on urban pluvial floods: Characteristics, mechanisms, data, and research methods

HUANG Huabing1,2,3(), WANG Xianwei1,2,3, LIU Lin4,5   

  1. 1. School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China
    2. Guangdong Provincial Engineering Research Center for Public Security and Disasters, Sun Yat-sen University, Guangzhou 510275, China
    3. Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519080, Guangdong, China
    4. Center of GeoInformatics for Public Security, School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
    5. Department of Geography and Geographic Information Science, University of Cincinnati, Cincinnati, OH 45221, USA
  • Received:2020-09-07 Revised:2020-12-10 Online:2021-06-28 Published:2021-08-28
  • Supported by:
    National Natural Science Foundation of China(41871085);The Science and Technology Program of Guangzhou(201707010098)


The spatial heterogeneity of urban built environments is a primary challenge in the research of urban pluvial floods in terms of the model representativeness, computational efficiency, and data requirements. The development of new technologies, including artificial intelligence, big data, and remote sensing, provides opportunities for the research of urban pluvial floods, such as efficient approaches and high-resolution data. This study conducted a comprehensive review of the research progress on urban pluvial floods from four perspectives—flood characteristics, mechanisms, data, and research methods, and finally came to four conclusions: 1) Urban pluvial floods have typical features such as short duration, scattered and evolving spatial distribution, chain effect, and sharp increase of losses at the critical scenario. 2) Micro-topography plays an important role in the spatial distribution of urban pluvial floods, and the topographic control index shows the potential of identifying frequently flooded areas. 3) The highly variable rainfall processes are the bottleneck in the near-real-time flood simulation, and the radar rainfall data provide a solution. Internet-based big data provide a new way to extract flood inundation data with high spatial coverage, but still face the problems of quality control and fusion with multi-sources data. 4) Machine learning could be coupled with hydrodynamic models to improve the efficiency of near-real-time flood simulation.

Key words: urban pluvial floods, artificial intelligence, big data, remote sensing