Understanding the dynamics of floods in dry environments and predicting an accurate flood hazard map considering multiple standards and conflicting objectives is of great political and planning importance in the Kingdom of Saudi Arabia’s vision for the year 2030, in order to reduce losses in lives, property, and infrastructure. The objectives of this study are (1) to develop a flood vulnerability map identifying flood-prone areas along the Al-Shamal train railway pathway; (2) to forecast the vulnerability of urban areas, agricultural land, and infrastructure to possible future floods hazard; and (3) to introduce strategic solutions and recommendations to mitigate and protect such areas from the negative impacts of floods. In order to achieve these objectives, multicriteria decision analysis based on geographic information systems (GIS-MCDA) is used to build a flood hazard map of the study area. The analytic hierarchy process (AHP) is applied to extract the weights of eight criteria which affect the areas which are prone to flooding hazards, including flow accumulation, distance from the wadi network, slope, rainfall density, drainage density, and rainfall speed. Furthermore, the receiver operating characteristic (ROC Curve) method is used to validate the presented flood hazard model. The results of the study reveal that there are five degrees of flooding hazard along the Al-Shamal train path, ranging from very high to very low. The high and very high hazard zones comprise 19.2 km along the path, which constitutes about 26.45% of the total path length, and are concentrated at the intersections of the Al-Shamal train pathway with the Bayer and Al-Makhrouk wadis. Moderate, low, and very low flood severity areas constitute nearly 53.39 km, representing 73.55% of the total length (72.59 km) of the track. These areas are concentrated at the intersection of the Al-Shamal train track with the Haseidah Al-Gharbiyeh and Hsaidah Umm Al-Nakhleh wadis. Urban and agricultural areas that are vulnerable to high and very high flooding hazards are shown to have areas of 29.23 km² (22.12%) and 59.87 km² (46.39%), respectively.

Drainage basins in dry and semiarid environments are exposed to sudden, irregular flooding that poses a threat to urban areas and infrastructure. The associated risk is exacerbated by land use changes. Geomorphometric analyses of drainage basins based on geographic information systems (GIS) are essential tools for assessing conceptual flood hazards. Geomorphological data extracted from high-precision digital elevation models (DEMs) provide valuable information for modeling the geomorphic, surface classifications of the earth, and for flood hazard mapping. This study aimed to develop an integrative approach to the mapping of flood hazards along the Al-Shamal train pathway in the city of Qurayyat in the Kingdom of Saudi Arabia (KSA) using GIS and hazard modeling for geomorphological ranking. Furthermore, we propose strategic solutions to provide mitigation and protection from negative impacts with the aim of improving the level of awareness of flood geomorphology. The hazard model of geomorphological ranking was used in mapping and calculating the degree of hazards using 24 geomorphometric criteria. These criteria were divided into formal criteria, terrain criteria, and criteria related to the drainage network. The results of the study revealed that the drainage sub-basins are exposed to flood hazards along the Al-Shamal train pathway in the city of Qurayyat. The very high flood hazard constituted 4228.3 km², accounting for 70.3% and 65.7%, respectively, of the drainage basins of the wadis of Makhrouq and Bayer. The high flood hazard represented 61% (4712.4 km²) of the basin of the wadis of Sarmadaa. The medium flood hazard was concentrated in the drainage basin of the wadi of Hasidah, accounting for nearly 57.7% (1271.3 km²). The very low flood hazard was present in 46.5% of the drainage basin of the wadis of Hasidah Umm Nakhla, accounting for an area of 799.4 km². The methodology applied in this study can be used in the estimation of flood hazards in different drainage basins throughout Saudi Arabia and in similar arid regions.

The current study aimed at measuring the impact of the change in land-use morphology on the increase of flood risk through its application to the case of the Riyadh–Dammam train track in Saudi Arabia. The track was exposed to drift on 18 February 2017, over a length of 10 km, in the district of Dhahran in the capital of Dammam. Flooding caused the train to drift off its track and resulted in damage to lives, property, and infrastructure. This resulted from human interventions in the preplanning land uses and changing the morphology of the land by encroaching on the valleys, which resulted in the loss of the environmental and ecological balance in the study area. In order to achieve these goals, land-use changes in the study area were monitored by analyzing successive images from the GEO-I-1 satellite with a resolution of 60 cm for the years 2011 and 2017, before and after the train drift, using the maximum likelihood classification process provided in ERDAS IMAGINE 2016. GIS was used in the processing of 1 m digital elevation models to extract the morphological changes of the wadies between 2011 and 2017. A hydrological model (HEC–HMS) was used in calculating the (flood) hydrograph curve of the wadies basins and estimating the calculation of flood water quantities and its flow rates based on the Soil Conservation Services (SCS) Unit Hydrograph Method. Rain depth was analyzed and estimated for different return periods. The HEC–RAS hydraulic modeling program was employed in developing a 2D model to calculate the velocity, depth, and spread of the flood in order to apply the risk matrix method.

The present work focuses on a comprehensive hydrochemical assessment of groundwater within a shallow aquifer located in the central region of Saudi Arabia. This aquifer serves as the principal source of groundwater supply for agricultural irrigation purposes. A total of twelve groundwater samples were systematically collected and subjected to thorough analysis to determine various physicochemical parameters. These parameters encompassed electrical conductivity (EC), pH, temperature, total dissolved solids (TDS), as well as concentrations of major ions. Analysis of major ion analysis was employed to elucidate the chemical attributes of groundwater within the research area. This approach facilitated an understanding of the geochemical evolution and the potential suitability of the groundwater for agriculture irrigation. The recorded range of electrical conductivity (EC) for the groundwater in this region falls between 3283 and 11,000 µS/cm, with an average value of 6709.4 µS/cm. The pH levels of the groundwater exhibited a slightly alkaline nature, ranging from 7.8 to 8.6. All sampled wells exhibited brackish water conditions (TDS > 1000 mg/L) based on TDS concentrations. The findings of this investigation demonstrated that the average ion concentration followed the order: Na⁺ > Ca⁺ > Mg²⁺ > K⁺ and Cl⁻ > SO₄²⁻ > HCO₃⁻ > NO₃²⁻. Applying Piper’s classification system, the groundwater samples were classified into two predominant categories: Ca-Cl and mix-Ca-Mg-Cl. The predominance of Ca²⁺ and Mg²⁺ over alkalis indicated mixed groundwater facies influenced by processes of reverse ion exchange and extensive interactions between water and rock formations. The distinct chemical characteristics of the groundwater were attributed to a combination of factors, including the percolation of irrigation-returned water, chemical transformations within the vadose zone, and extensive interactions with subsurface lithology. Despite major ion concentrations surpassing the permissible limits outlined by the World Health Organization (WHO) for drinking water, the assessment of quality indices was conducted to ascertain the suitability of the groundwater for irrigation purposes. These quality indices encompassed the permeability index (PI), magnesium hazard (MH), Kelly’s ratio (KR), sodium-adsorption ratio (SAR), residual sodium carbonate (RSC), sodium percentage (Na%), and magnesium ratio (MR). A meticulous evaluation of groundwater quality for agricultural utilization indicated that all sampled groundwater sources were deemed suitable for irrigation purposes.

It might be difficult to find possible groundwater reservoir zones, especially in arid or hilly regions. In the twenty-first century, remotely sensed satellite imagery may present a new opportunity to locate surface and subsurface water resources more quickly and affordably. In order to identify groundwater potential zones, the current study was conducted in Central Saudi Arabia, southwest of Riyadh. The present analysis employed a multi-criteria approach that relies on remote sensing and geographic information systems. The variables employed in this technique include geology, rainfall, elevation, slope, aspect, hillshade, drainage density, lineaments density, and Land Use/Land Cover (LULC). The Analytical Hierarchical Process (AHP) was used for assigning weights to the parameters, and the corresponding significance of each parameter’s several classes for groundwater potentiality. Different groundwater potential zones were identified by the study: very high (16.8%), high (30%), medium (26.7%), low (18.6%), and very low (7.9%). Only two of the observation wells were located in the “medium” potential zone, but the other ten wells were observed in the “very high and high” potential zones, according to the validation survey. Consequently, the results may demonstrate that the current approach, which combines improved conceptualization with AHP to define and map groundwater potential zones, has a greater chance of producing accurate results and can be used to reduce the threat of drought in broader arid regions.

The spatiotemporal analysis of land use/land cover change and monitoring, modeling, and forecasting the future of land uses are considered challenges facing planners and decision-makers in developing countries. These challenges are increased in neighborhood areas surrounding large cities, which are known as the “rural–urban continuum”. These areas have become the preferred areas for resettlement for most urban residents. The objectives of the present study were to (1) monitor the land cover change in the rural–urban continuum axis between the Ar-Riyadh and Al-Kharj cities during the period 1988–2020, (2) simulate the future growth of land cover up to the year 2030 using the Cellular Automated Markov Model (CA-Markov), and (3) improve the ability of CA-Markov to predict the future by integrating multi-criteria analysis based on geographic information systems (GIS-MCA) and analytic hierarchy process (AHP) method. The results of the study revealed large changes in the land cover in the rural–urban continuum axis between the Ar-Riyadh and Al-Kharj cities. About 60 km² of agricultural land has been lost, with an average annual decrease of 2 km². The industrial and urban areas were increased with growth rate of 4%. There were five categories of spatial suitability, ranging between 32 and 86%, and 70% or higher is the recommended percentage for future land uses. The industrial use was the most likely land use in 2030, as it recorded an increase of 27.1 km² over the year 2020.