TY - JOUR
T1 - Unprecedented rainfall in the United Arab Emirates
T2 - hydrologic and flood impact analysis of the April 2024 event
AU - Hussein, Khalid
AU - Alhosani, Naeema
AU - Al-Areeq, Ahmed M.
AU - Al Aghbari, Amran A.
AU - Elkamali, Muhagir
AU - Alsumaiti, Tareefa
AU - Sharif, Hatim O.
AU - Almurshidi, Ahmed M.G.
AU - Abdalati, Waleed
N1 - Publisher Copyright:
© The Author(s) 2025.
PY - 2025/5
Y1 - 2025/5
N2 - In mid-April 2024, an extreme rainfall event struck Oman and the United Arab Emirates (UAE), causing unprecedented flooding, significant infrastructure damage, and loss of life. Characterized by intense and sustained rainfall over a few days, this event highlighted the region’s vulnerability to extreme weather phenomena. This study examines the rainfall estimated by two satellite rainfall products during this event, with a detailed analysis focusing on the watershed encompassing the city of Al Ain. A rain gauge in this watershed recorded a historic 24-hour rainfall of 254.8 mm, approximately 75% of the area’s previously estimated probable maximum precipitation (PMP). Other gauges in the watershed also recorded substantial rainfall amounts, breaking previous records, while satellite products significantly underestimated the actual rainfall. A physically-based distributed hydrologic model simulated the resulting flood, indicating a low runoff ratio of about 7.14% due to high infiltration rates. Despite this, significant flooding occurred in urbanized parts of the watershed. This discrepancy highlights the complexity of urban hydrology and the challenges of predicting flood extents in urban areas. The findings underscore the need for improved flood forecasting systems in the UAE, emphasizing enhanced satellite rainfall estimation accuracy and advanced modeling approaches for better urban flood management and mitigation. Integrating new technologies and methodologies in urban flood forecasting is crucial for enhancing resilience against future extreme weather events, ensuring the safety and preparedness of vulnerable regions like the UAE.
AB - In mid-April 2024, an extreme rainfall event struck Oman and the United Arab Emirates (UAE), causing unprecedented flooding, significant infrastructure damage, and loss of life. Characterized by intense and sustained rainfall over a few days, this event highlighted the region’s vulnerability to extreme weather phenomena. This study examines the rainfall estimated by two satellite rainfall products during this event, with a detailed analysis focusing on the watershed encompassing the city of Al Ain. A rain gauge in this watershed recorded a historic 24-hour rainfall of 254.8 mm, approximately 75% of the area’s previously estimated probable maximum precipitation (PMP). Other gauges in the watershed also recorded substantial rainfall amounts, breaking previous records, while satellite products significantly underestimated the actual rainfall. A physically-based distributed hydrologic model simulated the resulting flood, indicating a low runoff ratio of about 7.14% due to high infiltration rates. Despite this, significant flooding occurred in urbanized parts of the watershed. This discrepancy highlights the complexity of urban hydrology and the challenges of predicting flood extents in urban areas. The findings underscore the need for improved flood forecasting systems in the UAE, emphasizing enhanced satellite rainfall estimation accuracy and advanced modeling approaches for better urban flood management and mitigation. Integrating new technologies and methodologies in urban flood forecasting is crucial for enhancing resilience against future extreme weather events, ensuring the safety and preparedness of vulnerable regions like the UAE.
KW - Arabian peninsula
KW - Extreme rainfall
KW - Flooding
KW - Hydrologic modeling
KW - Satellite rainfall products
KW - UAE
KW - Urban flood management
UR - http://www.scopus.com/inward/record.url?scp=85218702402&partnerID=8YFLogxK
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U2 - 10.1007/s11069-025-07156-9
DO - 10.1007/s11069-025-07156-9
M3 - Article
AN - SCOPUS:85218702402
SN - 0921-030X
VL - 121
SP - 9363
EP - 9385
JO - Natural Hazards
JF - Natural Hazards
IS - 8
ER -