Municipalities in the Kingdom of Saudi Arabia (KSA) are putting serious efforts into reducing the water losses to around 8% (i.e., national benchmark) of supplied water through active leakage control (ALC). In this research, a framework is conceptualized and developed to establish economic level of leakage (ELL), keeping in view the specific conditions of arid regions. In general, water loss control actions, including ALC, passive leakage control, pressure management, and infrastructure asset management, are performed by the municipalities up to the service connections until the cost of these actions becomes equal to the cost of the water lost. For the explicit scenario of KSA, the framework also investigates the possibilities of water losses beyond the property line where water is intermittently supplied for a certain duration in a week and stored in underground tanks to ensure a continuous supply within the house. Night flow analyses were conducted to establish the relationship between pressure and water losses. The framework is implemented on a case study in the Alryan neighborhood of Buraydah (Qassim, KSA) to evaluate its practicality. Scenario analyses are conducted to appraise the economic viability of different management actions for establishing ELL. The study results reveal that conventional ALC can reduce the infrastructure leakage index from around 20 to 7.28, and an additional 43% decrease can be economically achieved by implementing pressure management and control measures on the homeowner side. Environmental valuation analysis demonstrates that upgrading the effectiveness of ALC with a small increase in water prices could further improve the ELL.

In arid regions, the groundwater drawdown consistently increases, and even for a constant pumping rate, long-term predictions remain a challenge. The present research applies the modular three-dimensional finite-difference groundwater flow (MODFLOW) model to a unique aquifer facing challenges of undefined boundary conditions. Artificial neural networks (ANN) and adaptive neuro fuzzy inference systems (ANFIS) have also been investigated for predicting groundwater levels in the aquifer. A framework is developed for evaluating the impact of various scenarios of groundwater pumping on aquifer depletion. A new code in MATLAB was written for predictions of aquifer depletion using ANN/ANFIS. The geotechnical, meteorological, and hydrological data, including discharge and groundwater levels from 1980 to 2018 for wells in Qassim, were collected from the ministry concerned. The Nash–Sutcliffe efficiency and mean square error examined the performance of the models. The study found that the existing pumping rates can result in an alarming drawdown of 105 m in the next 50 years. Appropriate water conservation strategies for maintaining the existing pumping rate can reduce the impact on aquifer depletion by 33%.

Stormwater drainage systems in urban areas located in arid environmental regions generally consist of storm-sewer networks and man-made ponds for the collection and disposal of runoff, respectively. Due to expansion in cities’ boundaries as a result of population growth, the capacity of existing drainage systems has been exhausted. Therefore, such systems overflow even during the smaller (than the design) return period floods. At the same time, changing rainfall patterns and flash floods due to climate change are other phenomena that need appropriate attention. Consequently, the municipalities in arid environmental regions are facing challenges for effective decision-making concerning (i) improvement needs for drainage networks for safe collection of stormwater, (ii) selection of most feasible locations for additional ponds, and (iii) evaluation of other suitable options, such as micro-tunneling. In this research, a framework has been developed to evaluate different stormwater drainage options for urban areas of arid regions. Rainfall-runoff modeling was performed with the help of Hydrological-Engineering-Centre, Hydrological-Modelling-System (HEC-HMS). To evaluate the efficacy of each option for handling a given design flood, hydraulic-modeling was performed using SewerGEMS. Meteorological and topographical data was gathered from the Municipality of Buraydah and processed to generate different inputs required for hydraulic modeling. Finally, multicriteria decision-making (MCDM) was performed to evaluate all the options on the basis of four sustainability criteria, i.e., flood risk, economic viability, environmental impacts, and technical constraints. Criteria weights were established through group decision-making using the Analytic Hierarchy Process (AHP). Preference-Ranking-Organization-Method for Enrichment-Evaluation (PROMETHEE II) was used for final ranking of stormwater drainage options. The proposed framework has been implemented on a case of Buraydah City, Qassim, Saudi Arabia, to evaluate its pragmatism. Micro-tunnelling was found to be the most sustainable option.

Limited groundwater resources in arid regions have been found polluted for drinking purpose due to the presence of natural minerals and radioactive substances, in the sub-soils, higher than the drinking water quality standards. Municipalities in these regions are spending extensive resources to transport (from well fields) and treat this raw water to provide safe water to the community. Regular monitoring of various physical, chemical, and radioactive water quality parameters (WQPs) in raw water generates large datasets, which makes it difficult to come up with convenient findings for both the decision-makers and general public. A hierarchical water quality assessment framework develops three sub-indices, an overall water quality index, and a system for ranking of groundwater wells. Fuzzy analytical hierarchy process (fuzzy-AHP) establishes the importance weights of different WQPs and the sub-indices based on their impacts on human health, treatment processes, distribution system infrastructure, and irrigation applications. Fuzzy VIekriterijumsko KOmpromisno Rangiranje (fuzzy-VIKOR) method aggregates the WQPs’ performance for each well and ranks all the wells in a well field based on their overall pollution levels, i.e., remoteness from the applicable standards. For evaluating the pragmatism of the framework, data of 11 WQPs were obtained for 39 wells operating in three different well fields located along the boundaries and the central part of Buraydah, Qassim, Saudi Arabia. Spatial water quality maps showing physical, chemical, radioactive, and overall water quality assessment results revealed that the oldest well field located in the middle of the city outperforms the other two more recently developed well fields with lesser anthropogenic activities in their catchments. These findings testify that the primary source of contamination in deep aquifers is the natural sub-soil condition. The water quality indices will be useful to demonstrate the current situation of groundwater quality in Qassim Region and will facilitate the decision-makers for defining the intended uses of raw water sources (i.e., drinking, unrestricted irrigation, and restricted irrigation) and rehabilitation and renewal planning of the groundwater wells. The framework is applicable in the Kingdom of Saudi Arabia (KSA), Gulf Region, and elsewhere for groundwater quality assessment with desired modifications.

The evaporation losses are very high in warm-arid regions and their accurate evaluation is vital for the sustainable management of water resources. The assessment of such losses involves extremely difficult and original tasks because of the scarcity of data in countries with an arid climate. The main objective of this paper is to develop models for the simulation of pan-evaporation with the help of Penman and Hamon’s equations, Artificial Neural Networks (ANNs), and the Artificial Neuro Fuzzy Inference System (ANFIS). The results from five types of ANN models with different training functions were compared to find the best possible training function. The impact of using various input variables was investigated as an original contribution of this research. The average temperature and mean wind speed were found to be the most influential parameters. The estimation of parameters for Penman and Hamon’s equations was quite a daunting task. These parameters were estimated using a state of the art optimization algorithm, namely General Reduced Gradient Technique. The results of the Penman and Hamon’s equations, ANN, and ANFIS were compared. Thirty-eight years (from 1980 to 2018) of manually recorded pan-evaporation data regarding mean daily values of a month, including the relative humidity, wind speed, sunshine duration, and temperature, were collected from three gauging stations situated in Al Qassim, Saudi Arabia. The Nash and Sutcliffe Efficiency (NSE) and Mean Square Error (MSE) evaluated the performance of pan-evaporation modeling techniques. The study shows that the ANFIS simulation results were better than those of ANN and Penman and Hamon’s equations. The findings of the present research will help managers, engineers, and decision makers to sustainability manage natural water resources in warm-arid regions.