Morocco often faces significant intense rainfall periods that can generate flash floods and raging torrents, causing serious damage in a very short period of time. This study aims to monitor wetland areas after a flash-flood event in an arid region, Saquia El hamra Saharan of Morocco, using a technique that combines hydraulic modeling and remote sensing technology, namely satellite images. The hydrological parameters of the watershed were determined by the WMS software. Flood flow was modeled and simulated using HEC HMS and HEC-RAS software. To map the flooded areas, two satellite images (Sentinel-2 optical images) taken before and after the event were used. Three classifications were carried out using two powerful classifiers: support vector machines and decision tree. The first classifier was applied on both dates’ images, and the resulting maps were used as input for a constructed decision tree model as a post-classification change detection process.

Floods are the most visible and destructive hydrologic phenomenon in terms of human and economic loss. Typically, flash floods are caused by large amounts of runoff due to short duration and high-intensity rainfall. Floods also lead to environmental and social problems, such as damage to roads, farms, and infrastructures and sometimes pollute surface water resources via the transfer of industrial waste, creating many health problems. In late October 2016, a flash flood severely damaged the surroundings of the city of Laayoune in the Saquia El Hamra basin in southern Morocco. The country’s climate is arid and semi-arid and is prone to destructive floods. The purpose of this study is to evaluate this flash flood and monitor wetland areas after this event using a technique that relies on remote sensing technology. This work was accomplished using Sentinel 2 satellite images, from the European Space Agency, based on classification methods and change detection techniques. Before and after the occurrence, the SVM classifier was employed to map land cover and land use. The overall accuracy (Kappa coefficient) was 94.41 % (0.91), and 87.33 % (0.81), respectively for both dates, when compared to the ground-truth data. The decision tree was built with the maps produced by the SVM classification for both dates as inputs, producing a change detection map with increased detail. The remote sensing technology has enabled us to monitor the damage that has been done to the area following the catastrophe with details on the buildings affected, farms flooded, and the extent of the river.