The changes in catchments can be analyzed through the generation of DEM, which is important as input data in hydrologic modeling. This study aims to analyze the effect of anthropogenic activities on hydrological studies based on DEM comparison and GIUH hydrographs. The four DEM datasets (SRTM, ALOS, Copernicus, Sentinel-1) were compared to the topographic map of Makkah City and GPS data in order to assess the quality of the DEM elevation. The GIS Arc Hydro toolbox was used to extract morphometric and Horton–Strahler ratio characteristics to generate a GIUH hydrograph of the catchments of Wadi Nouman and Wadi Ibrahim inside Makkah City. Based on the DEM comparison, Copernicus and SRTM have the highest accuracy, with R2 = 0.9788 and 0.9765, and the lowest RMSE, 3.89 m and 4.23 m, respectively. ALOS and Sentinel-1 have the lowest R2, 0.9687 and 0.9028, and the highest RMSE, 4.27 m and 6.31 m, respectively. GIUH Copernicus DEM on Wadi Nouman has a higher qp and lower tp (0.21 1/h and 2.66 h) than SRTM (0.20 1/h and 2.75 h), respectively. On Wadi Ibrahim, the SRTM has a greater qp and lower tp than Copernicus due to the wadi having two shapes. Based on the anthropogenic effect, the stream network in the mountain area is quite similar for SRTM and Copernicus due to the dominant influence of the mountainous relief and relatively inconsequential influence of anthropogenic activities and DEM noise. In the urban area, the variation of the stream network is high due to differing DEM noise and significant anthropogenic activities such as urban redevelopment. The Copernicus DEM has the best performance of the others, with high accuracy, less RMSE, and stream flow direction following the recent condition.

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Actual flood mapping and quantification in an area provide valuable information for the stakeholder to prevent future losses. This study presents the actual flash flood quantification in Al-Lith Watershed, Saudi Arabia. The study is divided into two steps: first is actual flood mapping using remote sensing data, and the second is the flood volume calculation. Two Sentinel-1 images are processed to map the actual flood, i.e., image from 25 May 2018 (dry condition), and 24 November 2018 (peak flood condition). SNAP software is used for the flood mapping step. During SNAP processing, selecting the backscatter data representing the actual flood in an arid region is challenging. The dB range value from 7.23–14.22 is believed to represent the flood. In GIS software, the flood map result is converted into polygon to define the flood boundary. The flood boundary that is overlaid with Digital Elevation Map (DEM) is filled with the same elevation value. The Focal Statistics neighborhood method with three iterations is used to generate the flood surface elevation inside the flood boundary. The raster contains depth information is derived by subtraction of the flood surface elevation with DEM. Several steps are carried out to minimize the overcalculation outside the flood boundary. The flood volume can be derived by the multiplication of flood depth points with each cell size area. The flash flood volume in Al-Lith Watershed on 24 November 2018 is 155,507,439 m3. Validity checks are performed by comparing it with other studies, and the result shows that the number is reliable.

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One of the major issues in the arid region is the availability of hydrological data for hydrological studies of the basins for water resources projects. Since the Kingdom of Saudi Arabia (KSA) is a huge country and contains many arid basins it is awfully expensive and time-consuming to make hydrological networks for studying all these basins. Therefore, the Affinity Propagation (AP) clustering technique is proposed to cluster basins into groups that are similar in morphological, hydrological, and landcover characteristics and defining an exemplar (a representative basin) to each group. This basin is utilized for the installation of a detailed hydrological network. The hydrological response of that basin can be transferred and scaled appropriately to other basins in the cluster since they are hydrologically and morphologically similar. A pilot study is performed on 18 sub-basins in the southwestern part of KSA. GIS software is used to extract basin attributes and the clustering process is performed using the AP cluster packages in R software. The results show that four clusters are obtained based on the morphological attributes (twenty-eight attributes), five clusters based on hydrological attributes (twelve attributes), and three clusters based on land cover and CN (three kinds of landcover as attributes). The AP clustering technique was evaluated by the construction of a correlation matrix that shows a high correlation of 0.817 to 0.999. This study provides a robust technique that is effective and efficient to identify the similarity of catchments and can help hydrologists to develop a catchment management application in arid regions.