{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T15:12:37Z","timestamp":1773328357274,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T00:00:00Z","timestamp":1691712000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006013","name":"United Arab Emirates University","doi-asserted-by":"publisher","award":["G00003989"],"award-info":[{"award-number":["G00003989"]}],"id":[{"id":"10.13039\/501100006013","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Arid regions are prone to unprecedented extreme rainfall events that often result in severe flash floods. Using near-real-time precipitation data in hydrological modelling can aid in flood preparedness. This study analyzed rainfall data obtained from Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG V. 06) since 2001 to highlight recent trends of extreme rainfall indices for three selected watersheds in the UAE. Additionally, to validate the trends, the present study incorporated CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) into the analysis. Furthermore, for the first time, this study assessed the performance of the three products of IMERG in modelling flash flood events in the selected watersheds of UAE. A physical-based, fully distributed model was used to simulate the heaviest storm event. Also, a sensitivity analysis of the model\u2019s output to variations in the input parameters was conducted using the one-factor-at-a-time method. The result of the trend analysis indicated that IMERG and CHIRPS show similar trends in both datasets, indicating agreement and reliability in their observations. However, there are a few instances where IMERG and CHIRPS show slight discrepancies in the nature of the trend. In general, the trend analysis results indicated an increasing trend of total precipitation (mm) and consecutive wet days, which suggests a rise in the risk of flash floods. The simulation of the flash flood event showed that the IMERG final product outperformed the other two products, closely matching the model output of the gauge rainfall data with mean absolute error (MAE) of 1.5, 2.37, and 0.5 for Wadi Ham, Wadi Taween, and Wadi Maidaq, respectively. The model\u2019s performance was positively correlated with the size of the watershed. The sensitivity analysis results demonstrated that the model\u2019s output was most sensitive to infiltration parameters. The study\u2019s outcomes provide a good opportunity to improve near-real-time impact evaluation of flash flood events in the watersheds of the UAE.<\/jats:p>","DOI":"10.3390\/rs15163991","type":"journal-article","created":{"date-parts":[[2023,8,11]],"date-time":"2023-08-11T10:33:23Z","timestamp":1691750003000},"page":"3991","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Reliability of GPM IMERG Satellite Precipitation Data for Modelling Flash Flood Events in Selected Watersheds in the UAE"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1797-4904","authenticated-orcid":false,"given":"Mohamed A.","family":"Hamouda","sequence":"first","affiliation":[{"name":"Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates"},{"name":"National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3685-8482","authenticated-orcid":false,"given":"Gilbert","family":"Hinge","sequence":"additional","affiliation":[{"name":"Department of Civil Engineering, National Institute of Technology Durgapur, Durgapur 713209, India"}]},{"given":"Henok S.","family":"Yemane","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates"}]},{"given":"Hasan","family":"Al Mosteka","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates"}]},{"given":"Mohammed","family":"Makki","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1412-7219","authenticated-orcid":false,"given":"Mohamed M.","family":"Mohamed","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates"},{"name":"National Water and Energy Center, United Arab Emirates University, Al Ain P.O. Box 15551, United Arab Emirates"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Costache, R., Arabameri, A., Blaschke, T., Pham, Q.B., Pham, B.T., Pandey, M., Arora, A., Linh, N.T.T., and Costache, I. (2021). Flash-Flood Potential Mapping Using Deep Learning, Alternating Decision Trees and Data Provided by Remote Sensing Sensors. 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