This paper aimed to map areas prone to flooding in the Wadi Hanifah drainage basin located in the Riyadh region, and identify the most important factors that contribute to flooding through examining the influence of ten topographical, hydrological, and environmental variables affecting flood occurrence. Remote sensing data from Landsat-8, Shuttle Radar Topography Mission (SRTM), and other ancillary datasets were used to map relevant variables. Two weighted overlay techniques were used, including: analytical hierarchy process (AHP) and weighted product model (WPM). A correlation matrix and optimum index factor (OIF) were employed to identify the relative importance of each factor. The two derived flood susceptibility maps were assessed through validation by comparing the locations of historical flood events to susceptibility zones. The results confirmed the validity of the WPM map. The results also showed that nearly 50% of the study area was dominated by the “moderate” flood susceptibility zone, while about 33% of the total land area was classified as a “high” flood susceptibility zone. The “slope” factor was found to be the most effective variable for flood occurrence, followed by the “geology” variable, while the “distance to the drainage network” was the least important variable. The results of the OIF indicated that the best combination of factors dictating the variability of all flood susceptibility areas were “geology”, “land use/cover (LULC)”, and “soil type”. The study findings are expected to be useful in understanding the effects of each factor on the spatial variation in flood occurrence and in improving flood control, and can be reapplied to other regions with similar climatic and environmental conditions worldwide.

Searching for new sources of water is becoming one of the most important aspects of scientific research, especially in areas prone to drought, like Saudi Arabia. The study aim was to delineate groundwater potential zones within the Oasis of Al-Ahsa, in Saudi Arabia’s eastern region, and to identify the optimum factors that control the availability of groundwater zones. This was achieved through examining the effect of ten environmental variables on groundwater recharge, namely: slope, topographic wetness index (TWI), land cover (LC), elevation, lineament density (Ld), drainage density (Dd), rainfall, geology, and soil texture. The variables were prepared from a variety of data sources, including spatial data (i.e., DEM and Landsat-8 image), in addition to other complementing data sources for appropriate parameters extraction. Two weighted overlay methods were used, namely the simple additive weight (SAW) as well as the optimum index factor (OIF) in order to categorize the optimal set of parameters for computing GWP and identifying its zones. Two GWP maps were obtained and validated through comparison with the locations of existing wells at GWP zones. The study findings have assured the cogency of the SAW map, where it was found that nearly 45–48% of the resultant zones were characterized as in the “moderate” class, whereas around 21–37% of the entire zones area were classified within the “high” class. The soil texture parameter was determined as being the most influencing parameter for GWP mapping followed by the “geology” parameter; however, the “lineament density” (Ld) was the least important factor. Furthermore, the OIF method has facilitated the identification of the optimal parameter combination for delineating groundwater potential (GWP) zones, which included “Ld”, “land cover”, and “TWI”. The study findings and methodology can serve as a potential model for other similar regions, supporting sustainable water resource management locally as well as globally.

The rapid expansion of urban areas is a major driver of deforestation and other associated damage to the local ecosystem and environment in arid and semi-arid oases, especially in the eastern region of Saudi Arabia. It is therefore necessary to accurately map and monitor urban areas to maintain the ecosystem services in these oases. In this study, built-up areas were mapped using a spectral mixture analysis (SMA) model in the Al-Ahsa Oasis in the eastern region of Saudi Arabia by analyzing Landsat images, including Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), Operational Land Imager (OLI), and Sentinel-2A images, acquired between 1990 and 2020. Principle component analysis (PCA) was used to build and select endmembers, and then SMA was applied to each image to extract urban/built-up fractions. In addition, this study also discusses the possible driving forces of the urban dynamics. SMA classification performance was assessed using fraction error maps and a confusion matrix. The results show that the Al-Ahsa Oasis’ urban area had been rapidly expanding during 2010–2020 with an expansion rate of nearly 9%. The results also indicated that the SMA model provides high precisions (overall accuracy = ~95% to 100%) for an oasis urban mapping in an arid and semi-arid region that is disturbed by the mixed-pixel problem, such as the Al-Ahsa Oasis in eastern Saudi Arabia.

Rapid urbanization has recently led to a significant propagation of heat islands. This study aimed to analyze the spatio-temporal variation in urban heat islands (UHIs) at Al-Ahsa Oasis in Saudi Arabia, in addition to exploring the urbanization influence on UHI distribution over the last 30 years. The spatial variability in UHIs was assessed, the key determinant elements were identified, and the forms of distribution were delineated. Change detection, hot spots, and spatial autocorrelation were employed to study UHI distribution and intensity and to identify the clustering and correspondence between heat and urbanization. The results revealed a considerable increase in built-up areas from 17.15% to 45.8% of total land use/cover (LULC) from 1990 to 2020. No significant variations in UHI intensity were observed (10.4 °C in 1990 and 8.7 °C for 2020). However, a remarkable link was found between urbanization and heat, confirmed by hot spot clustering over intense urban complexes, while cold spot clustering was observed over date and palm tree areas, with 99% confidence for both. Lastly, the link between temperature and urbanization was also confirmed through spatial autocorrelation, producing Moran’s indices of 0.41 and 0.45 for 1990 and 2020, respectively, with an overall significance (p-value) of 0.001. The mechanisms applied have proven their robustness in assessing the effect of urbanization on heat island distribution and quantification.

Floods in southwestern Saudi Arabia, especially in the Asir region, are among the major natural disasters caused by natural and human factors. In this region, flash floods that occur in the Wadi Hail Basin greatly affect human life and activities, damaging property, the built environment, infrastructure, landscapes, and facilities. A previous study carried out for the same basin has effectively revealed zones of flood risk using such an approach. However, the utilization of the HEC–HMS (Hydrologic Engineering Center–Hydrologic Modeling System) model and IMERG data for delineating areas prone to flash floods remain unexplored. In response to this advantage, this work primarily focused on flood generation assessment in the Wadi Hail Basin, one of the major basins in the region that is frequently prone to severe flash flood damage, from a single extreme rainfall event. We employed a fully physical-based, distributed hydrological model run with HEC–HMS software version 4.11 and Integrated Multi-satellite Retrievals of Global Precipitation Measurement (IMERG V.06) data, as well as other geo-environmental variables, to simulate the water flow within the Wadi Basin, and predict flash flood hazard. Discharge from the wadi and its sub-basins was predicted using 1 mm rainfall over an 8-h occurrence time. Significant peak discharge (3.6 m³/s) was found in eastern and southern upstream sub-basins and crossing points, rather than those downstream, due to their high-density drainage network (0.12) and CNs (88.4). Generally, four flood hazard levels were identified in the study basin: ‘low risk’, ‘moderate risk’, ‘high risk’, and ‘very high risk’. It was found that 43.8% of the total area of the Wadi Hail Basin is highly prone to flooding. Furthermore, medium- and low-hazard areas make up 4.5–11.2% of the total area, respectively. We found that the peak discharge value of sub-basin 11 (1.8 m³/s) covers 13.2% of the total Wadi Hail area; so, it poses more flood risk than other Wadi Hail sub-basins. The obtained results demonstrated the usefulness of the methods used to develop useful hydrological information in a region lacking ungagged data. This study will play a useful role in identifying the impact of extreme rainfall events on locations that may be susceptible to flash flooding, which will help authorities to develop flood management strategies, particularly in response to extreme events. The study results have potential and valuable policy implications for planners and decision-makers regarding infrastructural development and ensuring environmental stability. The study recommends further research to understand how flash flood hazards correlate with changes at different land use/cover (LULC) classes. This could refine flash flood hazards results and maximize its effectiveness.