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Disaster mapping and assessment of Pakistan’s 2022 mega-flood based on multi-source data-driven approach

Author

Listed:
  • Juanle Wang

    (Chinese Academy of Sciences
    China-Pakistan Earth Science Research Center
    University of Chinese Academy of Sciences
    Jiangsu Province Geography Collaborative Innovation Center for Information Resource Development and Utilization)

  • Kai Li

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    China University of Mining and Technology (Beijing))

  • Lina Hao

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences
    Jiangsu Ocean University)

  • Chen Xu

    (Chinese Academy of Sciences
    Jiangsu Ocean University)

  • Jingxuan Liu

    (Chinese Academy of Sciences
    Jiangsu Ocean University)

  • Zheng Qu

    (Chinese Academy of Sciences
    Shandong University of Technology)

  • Xinrong Yan

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Meer Muhammad Sajjad

    (Chinese Academy of Sciences
    University of Chinese Academy of Sciences)

  • Yamin Sun

    (Chinese Academy of Sciences
    School of Resources and Environment, Institute of Disaster Prevention Science and Technology)

Abstract

Climate change-induced mega-floods have become increasingly frequent worldwide. The rapid mapping and assessment of flood disasters pose urgent challenges for developing countries with poor data facilities or databases. In this study, the characteristics of the 2022 mega-flood in Pakistan were monitored and analyzed based on multi-resources data. The extent of inundation throughout Pakistan and its impact on farmlands, buildings, and roads were mapped using Synthetic Aperture Radar remote sensing data processing technology. The results showed that a 10-m resolution flooding map could be achieved using the Google Earth Engine platform in a timely manner with reasonable precision. A GIS-based bluespot model was used to evaluate the risk of dam-failure floods. The zone risk distribution map of the dam-failure flood was produced with five risk levels, which contribute to the safety of the key infrastructure for flooding control. The potential influencing factors of snow melting in northern Pakistan induced by heat waves and disasters was detected using Earth observations and long-record historical data. The study provides data-driven approach options for monitoring flood hazards over large areas in emergency using multi-available data sources, where in situ monitoring is difficult. This study not only provided direct data products and risk maps for mega-flooding control in Pakistan, but also proposed five aspects of flood prevention and control recommendations for this region and its neighborhood areas to cope with flood disasters effectively under worsening climate change conditions.

Suggested Citation

  • Juanle Wang & Kai Li & Lina Hao & Chen Xu & Jingxuan Liu & Zheng Qu & Xinrong Yan & Meer Muhammad Sajjad & Yamin Sun, 2024. "Disaster mapping and assessment of Pakistan’s 2022 mega-flood based on multi-source data-driven approach," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(4), pages 3447-3466, March.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:4:d:10.1007_s11069-023-06337-8
    DOI: 10.1007/s11069-023-06337-8
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    References listed on IDEAS

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    1. Hessel C. Winsemius & Jeroen C. J. H. Aerts & Ludovicus P. H. van Beek & Marc F. P. Bierkens & Arno Bouwman & Brenden Jongman & Jaap C. J. Kwadijk & Willem Ligtvoet & Paul L. Lucas & Detlef P. van Vuu, 2016. "Global drivers of future river flood risk," Nature Climate Change, Nature, vol. 6(4), pages 381-385, April.
    2. Takuro Ashizawa & Nao Sudo & Hiroki Yamamoto, 2022. "How Do Floods Affect the Economy? An Empirical Analysis using Japanese Flood Data," Bank of Japan Working Paper Series 22-E-6, Bank of Japan.
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