Water supply systems (WSSs) are one of the most important and expensive core public infrastructures. The primary objective of a water supply utility is to have this valuable asset operate at its maximum possible efficiency with minimum cost throughout its design period. To achieve this objective, the first step is to evaluate the existing efficiency of all the components of the WSS using suitable performance indicators (PIs). Various agencies and organizations worldwide have developed detailed performance evaluation frameworks including several indicators to comprehensively cover all the aspects (e.g., physical asset, staffing, operational, customer satisfaction, economical) of the WSSs. Most of these frameworks and indicators have been developed for large-sized WSSs. Small- and medium-sized water supply systems (SM-WSSs) have specific performance-related issues, ranging from difficulties in collecting the data required to use the available systems of PIs to lack of skilled personnel and financial resources for efficient operations. A comprehensive review of the literature has been carried out to assess the suitability of reported performance evaluation systems for SM-WSSs in terms of their simplicity (easy and simple data requirements) and comprehensiveness (i.e., all the components of a WSS). This review also evaluates the individual PI with respect to its understandability, measurability, and comparability (i.e., within and across utility comparisons). On the basis of this detailed review, a conceptual performance evaluation system for SM-WSSs, consisting of a list of PIs grouped into their respective categories, has been proposed. The proposed system provides a stepwise approach, starting the performance evaluation process with the most significant and easy to measure PIs for small-sized WSSs and moving to a relatively complex set of indicators for SM-WSS depending on the availability of resources and specific operating conditions.

Oil and gas (O&G) pipelines are expensive assets that cross through both the ecologically sensitive and densely populated urban areas. The pipeline failure may have potentially significant consequences for both the natural and human environments. In order to maintain the integrity of O&G pipelines, inspection and maintenance processes should be governed by efficient policies. The objective of this paper is to conduct a state-of-the-art review of maintenance policies of O&G pipelines to investigate their advantages, limitations, and associated implementation issues. Maintenance policies can be categorised into corrective, preventive, predictive and proactive. Corrective maintenance policies (1940s) were based on the ‘repair when broke’ philosophy. Economic considerations shifted practice towards preventive maintenance (1970s to 1990s); later with improved inspection techniques and environmental regulations, predictive and proactive or risk-based maintenance (RBM) policies were developed. This review explicates different methodologies for RBM and related issues, e.g. uncertainties and variability, conservative assumptions, etc. Uncertainties associated with investigation and prediction of defects have been more frequently reported in the literature so far. Moreover, existing studies primarily focused on reducing the likelihood and cost of failure, whereas consideration of environmental factors in overall risk has been a relatively less addressed issue.

Water and energy are interdependent on each other. Energy is required to supply water to a system while, at the same time, water is needed for power generation in any natural or artificial system. This relationship is often called the water–energy nexus (WEN). In a water supply system, energy is consumed for source water extraction, transmission, treatment, and distribution. About 7%–8% of the world’s total generated energy is used for drinking water production and distribution. A major portion of this energy is used for distribution, i.e., pumping, chlorination, and maintenance activities, and hence is the focus of this review. Most of the world’s energy is generated by fossil fuels (oil, gas, and coal), which results in greenhouse gas (GHG) emissions. Here we review studies conducted to assess and evaluate the energy consumption and the related GHG emissions in water distribution systems (WDSs). This review covers the basic concepts and studies on WEN, energy saving solutions, renewable energy resources for water pumping, optimization of design, and the life cycle assessment (LCA) of large WDSs. Most of the reviewed studies suggest a trade-off between energy cost and the associated GHG emissions when selecting fixed-speed pumps over variable-speed pumps for large WDSs. To mitigate CO₂ emissions, renewable energy resources like solar, wind, and mini-water turbines for water pumping have been discussed and mini-water turbines were found to be energy efficient solutions. The energy-focused LCA model has been studied to investigate the environmental impacts, GHG emissions, operational energy, and various life cycle stages of pipe manufacturing (embodied energy) in the network. Case studies of real world WDSs are reviewed and the potential research gaps are identified. Most life cycle studies have focused on the areas of pipe replacement, the life cycle cost of the system, the operational energy, and the reduction of GHG emissions, whereas less attention has been paid to the geographical and socio-economic issues along with the areas of human health, water resource diversity, and the hydraulic characteristics of WDSs.

Future predictions of rainfall patterns in water-scarce regions are highly important for effective water resource management. Global circulation models (GCMs) are commonly used to make such predictions, but these models are highly complex and expensive. Furthermore, their results are associated with uncertainties and variations for different GCMs for various greenhouse gas emission scenarios. Data-driven models including artificial neural networks (ANNs) and adaptive neuro fuzzy inference systems (ANFISs) can be used to predict long-term future changes in rainfall and temperature, which is a challenging task and has limitations including the impact of greenhouse gas emission scenarios. Therefore, in this research, results from various GCMs and data-driven models were investigated to study the changes in temperature and rainfall of the Qassim region in Saudi Arabia. Thirty years of monthly climatic data were used for trend analysis using Mann–Kendall test and simulating the changes in temperature and rainfall using three GCMs (namely, HADCM3, INCM3, and MPEH5) for the A1B, A2, and B1 emissions scenarios as well as two data-driven models (ANN: feed-forward-multilayer, perceptron and ANFIS) without the impact of any emissions scenario. The results of the GCM were downscaled for the Qassim region using the Long Ashton Research Station’s Weather Generator 5.5. The coefficient of determination (R²) and Akaike’s information criterion (AIC) were used to compare the performance of the models. Results showed that the ANNs could outperform the ANFIS for predicting long-term future temperature and rainfall with acceptable accuracy. All nine GCM predictions (three models with three emissions scenarios) differed significantly from one another. Overall, the future predictions showed that the temperatures of the Qassim region will increase with a specified pattern from 2011 to 2099, whereas the changes in rainfall will differ over various spans of the future.