ArticleScientific reports2025
Remote sensing and climatic data for flood impact assessment in Al-Ain (UAE).
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.