Flash Flood Hazard Using Optical, Radar, and Stereo-Pair Derived DEM: Eastern Desert, Egypt

Flash floods are classified among the Earth’s most deadly and destructive natural hazards, particularly in arid regions. Wadi El-Ambagi, one of the largest drainage basins in the Eastern Desert of Egypt, is frequently subjected to severe flash flood damage following intense, short-lived rainstorms. This wadi is home to one of the few road networks which connects the Nile River Valley to the Red Sea Coast. At its outlet lies Quseir, one of the major coastal towns in the area. Quseir is a developing tourism and scuba diving town, and is known for its historical importance as an ancient port; thus, efforts are in place to preserve the town’s heritage. The lack of hydrological and meteorological data in this region necessitates the use of a hydrological modeling approach to predict the spatial extent, depth, and velocity of the flood waters, and hence locate sites at risk of flood inundation. This was accomplished by understanding the characteristics of surface runoff through modeled hydrographs. Here, elevation data were extracted from Shuttle Radar Topography Mission (SRTM) and a two-meter digital elevation model (DEM) derived from WorldView-2 stereo pair imagery. The land use/land cover and soil properties were mapped from fused ASTER multispectral and ALOS-PALSAR Synthetic Aperture Radar (SAR) data to produce a hybrid image that combines spectral properties and surface roughness, respectively. The results showed that storm events with rainfall intensities of 30 mm and ~60 mm over a two-hour period would generate maximum peak flows of 165 m³ s⁻¹ and 875 m³ s⁻¹ , respectively. The latter peak flow would generate floods with depths of up to 2 m within the town of Quseir. A flood of this magnitude would inundate 217 buildings, 7 km of the highway, and 1.43 km of the railroad in the downstream area of Wadi El-Ambagi. Findings from this work indicate that the integration of remote sensing and hydrological modeling can be a practical and quick approach to predict flash flood hazards in arid regions where data are scarce.