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<p>On April 16, 2024, Al Ain City in the United Arab Emirates experienced a record-breaking flash flood, with over 250&#xa0;mm of rainfall in a few hours among the most extreme hydroclimatic events recorded in the region. This study presents a detailed geospatial assessment of the event, integrating optical satellite imagery, climatic data, and topographic analysis to quantify flood extent, depth, and impact. Sentinel-2 imagery acquired 12&#xa0;h after peak rainfall enabled high-resolution flood mapping. To improve floodwater detection, we applied the newly developed Index of Turbid Waters (ITW), a spectral index designed to distinguish turbid floodwaters from permanent water bodies. The integration of ITW with rainfall data from 16 meteorological stations and digital elevation models revealed a strong relationship between flood extent and geomorphological vulnerability. Water depth estimations identified critical inundation zones, with depths exceeding 8&#xa0;m in urban districts such as Rowdah. GIS-based infrastructure and land use analysis highlighted high-risk areas, including transportation networks and informal flood defenses. The findings underscore the increasing risk of extreme flash floods in arid environments and demonstrate how rapid, multi-source geospatial analysis can inform emergency response and urban resilience planning.</p>

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Remote sensing and climatic data for flood impact assessment in Al-Ain (UAE)

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月18日

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  • Mohand Bersi,
  • Hakim Saibi,
  • Kamal Abdelrahman,
  • Mohammed S. Fnais,
  • Mohamed Saber

摘要

On April 16, 2024, Al Ain City in the United Arab Emirates experienced a record-breaking flash flood, with over 250 mm of rainfall in a few hours among the most extreme hydroclimatic events recorded in the region. This study presents a detailed geospatial assessment of the event, integrating optical satellite imagery, climatic data, and topographic analysis to quantify flood extent, depth, and impact. Sentinel-2 imagery acquired 12 h after peak rainfall enabled high-resolution flood mapping. To improve floodwater detection, we applied the newly developed Index of Turbid Waters (ITW), a spectral index designed to distinguish turbid floodwaters from permanent water bodies. The integration of ITW with rainfall data from 16 meteorological stations and digital elevation models revealed a strong relationship between flood extent and geomorphological vulnerability. Water depth estimations identified critical inundation zones, with depths exceeding 8 m in urban districts such as Rowdah. GIS-based infrastructure and land use analysis highlighted high-risk areas, including transportation networks and informal flood defenses. The findings underscore the increasing risk of extreme flash floods in arid environments and demonstrate how rapid, multi-source geospatial analysis can inform emergency response and urban resilience planning.