Groundwater quality assessment is crucial for the sustainable management of water resources in arid regions, where groundwater is the primary source of water supply and increasing demand raises concerns. The study area in Southwest Algeria relies heavily on groundwater as a source of water supply, and the increasing demand for freshwater raises concerns about the quality of groundwater. To assess the hydrochemical characteristics and water quality of groundwater in the Ain Sefra region, multivariate statistical methods, geochemical modeling and water quality indices were employed. The study revealed that the groundwater samples could be classified into four water groups using hierarchical cluster analysis Q mode (HCA), namely Ca–Mg–HCO₃, Ca–Mg–Cl–SO₄, Ca–SO₄ and Na–Cl. Factor analysis was used to identify the main factors controlling the study area’s hydrochemical processes. The results indicated that water–rock interaction, reverse ion exchange and anthropogenic pollution were the main hydrochemical processes affecting groundwater chemistry. The water quality index indicated that the groundwater was suitable for human consumption, with only 2.32% of the samples being unsuitable. Additionally, the groundwater was suitable for agricultural use, but salinity control was necessary. The saturation index values showed that the groundwater was supersaturated with aragonite, calcite, dolomite, anhydrite and gypsum, and undersaturated with halite. Ca-smectite, Mg-smectite and kaolinite were identified as the primary processes controlling the chemical composition of groundwater. The application of multivariate statistical methods, geochemical modeling and water quality indices provided a comprehensive understanding of the hydrochemical characteristics and water quality of groundwater in the Ain Sefra region. The findings of the study can serve as a useful basis for future studies on groundwater quality assessment in the region.

Assessing water quality is crucial for improving global water resource management, particularly in arid regions. This study aims to assess and monitor the status of groundwater quality based on hydrochemical parameters and by using artificial intelligence (AI) approaches. The irrigation water quality index (IWQI) is predicted by using support vector machine (SVM) and k-nearest neighbors (KNN) classifiers in Matlab’s classification learner toolbox. The classifiers are fed with the following hydrochemical input parameters: sodium adsorption ratio (SAR), electrical conductivity (EC), bicarbonate level (HCO₃), chloride concentration (Cl), and sodium concentration (Na). The proposed methods were used to assess the quality of groundwater extracted from the desertic region of Adrar in Algeria. The collected groundwater samples showed that 9.64% of samples were of very good quality, 12.05% were of good quality, 21.08% were satisfactory, and 57.23% were considered unsuitable for irrigation. The IWQI prediction accuracies of the classifiers with the standardized, normalized, and raw data were 100%, 100%, and 90%, respectively. The cubic SVM with the normalized data develops the highest prediction accuracy for training and testing samples (94.2% and 100%, respectively). The findings of this work showed that the multiple regression model and machine learning could effectively assess water quality in desert zones for sustainable water management.

Saudi Arabia is one of the most water-scarce nations in the world, with a huge demand-supply gap, and the situation is expected to worsen due to climate change. Conventional surface water resources are limited, while nonrenewable groundwater sources are depleted. To build a more resilient and sustainable water sector, the production of non-conventional water resources, specifically desalinated seawater and treated domestic wastewater, has steadily increased in recent years. As the country lacks perennial water resources, such as rivers or water bodies, it relies mainly on nonrenewable groundwater and desalinated water to meet its daily requirements. Although the government is attempting to regulate the agricultural sector, water consumption in agriculture remains relatively high. It presents an environmental challenge due to its heavy reliance on non-renewable groundwater resources. The anticipated increase in temperature and highly uncertain changes in the rainfall patterns in Saudi Arabia could lead to greater uncertainty when attempting to develop effective water resource management plans. In this work, we review the status of the present and future of water resources and the challenges local authorities face in managing water resources amidst a changing climate in Saudi Arabia. This study employed a narrative research methodology, utilizing various databases, including Scopus, Web of Science, and Science Direct, to extract relevant articles within the subject area. This study proposes a number of recommendations and conclusions aimed at improving decision-makers’ ability to adapt to and mitigate the anticipated adverse impacts of climate change to manage scarce water resources sustainably.

Countries in arid regions are presently facing challenges in managing their limited water resources. Assessing the evaporation losses from various sources of water is a daunting task that is inevitable for the sustainability of water resource management schemes in these regions. Although several techniques are available for simulating evaporation rates, identifying the parameters of various evaporation equations still needs to be further investigated. The main goal of this research was to develop a framework for determining the parameters influencing the evaporation rate of evaporation pans. Four different equations, including those of Hamon, Penman, Jensen–Haise, and Makkink, were chosen to estimate evaporation from the evaporation pans installed in the Qassim Region of Saudi Arabia. The parameters of these four equations were identified by a state-of-the-art optimization technique, known as the general reduced gradient (GRG). Three types of objective functions used for optimization were tested. Forty-year monitoring records for pan evaporation, temperature, relative humidity, and sunshine hours were collected from the Municipality of Buraydah Al Qassim, for the period of 1976 to 2016. These data were mainly manually recorded at a weather station situated in the Buraydah city. Preliminary data analysis was performed using the Mann–Kendall and Sen’s slope tests to study the trends. The first 20-year (1976–1995) data were used for calibrating the equations by employing an optimization technique and the remaining data were used for validation purposes. Four new equations were finally developed and their performance, along with the performance of the four original equations, was evaluated using the Nash and Sutcliffe Efficiency (NSE) and the Mean Biased Error (MBE). The study revealed that among the original equations, the Penman equation performed better than the other three equations. Additionally, among the new equations, the Hamon method performed better than the remaining three equations.

A quantitative assessment of the likelihood of all possible future states is lacking in both the traditional top-down and the alternative bottom-up approaches to the assessment of climate change impacts. The issue is tackled herein by generating a large number of representative climate projections using weather generators calibrated with the outputs of regional climate models. A case study was performed on the South Nation River Watershed located in Eastern Ontario, Canada, using climate projections generated by four climate models and forced with medium- to high-emission scenarios (RCP4.5 and RCP8.5) for the future 30-year period (2071–2100). These raw projections were corrected using two downscaling techniques. Large ensembles of future series were created by perturbing downscaled data with a stochastic weather generator, then used as inputs to a hydrological model that was calibrated using observed data. Risk indices calculated with the simulated streamflow data were converted into probability distributions using Kernel Density Estimations. The results are dimensional joint probability distributions of risk-relevant indices that provide estimates of the likelihood of unwanted events under a given watershed configuration and management policy. The proposed approach offers a more complete vision of the impacts of climate change and opens the door to a more objective assessment of adaptation strategies.