Assessing diverse parameters like water quality, quantity, and occurrence of hydrological extremes and their management is crucial to perform efficient water resource management (WRM). A successful WRM strategy requires a three-pronged approach: monitoring historical data, predicting future trends, and taking controlling measures to manage risks and ensure sustainability. Artificial intelligence (AI) techniques leverage these diverse knowledge fields to a single theme. This review article focuses on the potential of AI in two specific management areas: water supply-side and demand-side measures. It includes the investigation of diverse AI applications in leak detection and infrastructure maintenance, demand forecasting and water supply optimization, water treatment and water desalination, water quality monitoring and pollution control, parameter calibration and optimization applications, flood and drought predictions, and decision support systems. Finally, an overview of the selection of the appropriate AI techniques is suggested. The nature of AI adoption in WRM investigated using the Gartner hype cycle curve indicated that the learning application has advanced to different stages of maturity, and big data future application has to reach the plateau of productivity. This review also delineates future potential pathways to expedite the integration of AI-driven solutions and harness their transformative capabilities for the protection of global water resources.

Calibration is an important phase in the hydrological modelling process. In this study, an automated calibration framework is developed for estimating Manning9s roughness coefficient. The calibration process is formulated as an optimization problem and solved using a genetic algorithm (GA). A heuristic search procedure using GA is developed by including runoff simulation process and evaluating the fitness function by comparing the experimental results. The model is calibrated and validated using datasets of Watershed Experimentation System. A loosely coupled architecture is followed with an interface program to enable automatic data transfer between overland flow model and GA. Single objective GA optimization with minimizing percentage bias, root mean square error and maximizing Nash–Sutcliffe efficiency is integrated with the model scheme. Trade-offs are observed between the different objectives and no single set of the parameter is able to optimize all objectives simultaneously. Hence, multi-objective GA using pooled and balanced aggregated function statistic are used along with the model. The results indicate that the solutions on the Pareto-front are equally good with respect to one objective, but may not be suitable regarding other objectives. The present technique can be applied to calibrate the hydrological model parameters.

Understanding precipitation projections on the grounds of climate change in arid environments is crucial for water resource planners. This study investigates the behavior of precipitation projections over the Kingdom of Bahrain using six Global Circulation Models (GCMs) from the current Coupled Model Intercomparison Project (CMIP6) archive. The ranking of six GCMs is assessed using a compromise programming technique and validated using a comprehensive rating index. Considering the results from the performance statistics, a multi-model ensemble is made for monthly and annual assessments. The 62-year period (1961–2023) is chosen as the observed data period, and further evaluations were conducted by dividing 46 years into three future periods (near-future (2024–2040), medium-future (2041–2055), and far-future (2056–2070). Precipitation projections indicate a chance of an increase of 12% in mean annual precipitation, although not statistically significant. The number of days with at least 10 mm precipitation is projected to increase, whereas there is little expectation of continuous wet days for Bahrain. The country can expect a marginal increase in the intensity of precipitation. More intense precipitation will occur for 5–10 years of return period interval. The study highlights the importance of suitable climatic model selections and the expected changes in the magnitude and intensity of precipitation in the country. This will help to formulate context-specific adaptation strategies for climate change in the Kingdom of Bahrain. Graphical Abstract