TY - JOUR
T1 - Remote sensing and climatic data for flood impact assessment in Al-Ain (UAE)
AU - Bersi, Mohand
AU - Saibi, Hakim
AU - Abdelrahman, Kamal
AU - Fnais, Mohammed S.
AU - Saber, Mohamed
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/12
Y1 - 2025/12
N2 - 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.
AB - 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.
KW - Al Ain (UAE)
KW - Flash floods risk assessment
KW - Geographic information system (GIS)
KW - Geospatial flood impact analysis
KW - Index of turbid waters (ITW)
KW - Remote sensing
UR - https://www.scopus.com/pages/publications/105010948809
UR - https://www.scopus.com/pages/publications/105010948809#tab=citedBy
U2 - 10.1038/s41598-025-12234-w
DO - 10.1038/s41598-025-12234-w
M3 - Article
C2 - 40681727
AN - SCOPUS:105010948809
SN - 2045-2322
VL - 15
JO - Scientific reports
JF - Scientific reports
IS - 1
M1 - 26182
ER -