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The pervasive diffusion of information and communication technologies that has characterized the end of the 20th and the beginning of the 21st centuries has profoundly impacted the way water management issues are studied. The possibility of collecting and storing large data sets has allowed the development of new classes of models that try to infer the relationships between the variables of interest directly from data rather than fit the classical physical and chemical laws to them. This approach, known as “data-driven,” belongs to the broader area of machine learning (ML) methods and can be applied to many water management problems. In hydrological modeling, ML tools can process diverse data sets, including satellite imagery, meteorological data, and historical records, to enhance predictions of streamflow, groundwater levels, and water availability and thus support water allocation, infrastructure planning, and operational decision-making. In water demand management, ML models can analyze historical water consumption patterns, weather data, and socioeconomic factors to predict future water demands. These models can support water utilities and policymakers in optimizing water allocation, planning infrastructure, and implementing effective conservation strategies. In reservoir management, advanced ML tools may be used to determine the operating rule of water structures by directly searching for the management policy or by mimicking a set of decisions with some desired properties. They may also be used to develop surrogate models that can be rapidly executed to determine the optimal course of action as a component of a decision-support system. ML methods have revolutionized water management studies by showing the power of data-driven insights. Thanks to their ability to make accurate forecasts, enhanced monitoring, and optimized resource allocation, adopting these tools is predicted to expand and consistently modify water management practices. Continued advancements in ML tools, data availability, and interdisciplinary collaborations will further propel the use of ML methods to address global water challenges and pave the way for a more resilient and sustainable water future.

The desire of policymakers and public finance institutions to understand the contribution of water infrastructure to the wider economy, rather than the value of project-level outputs in isolation, has spawned a multidisciplinary branch of water resource planning that integrates traditional biophysical modeling of water resource systems with economy-wide models, including computable general equilibrium models. Economy-wide models include several distinct approaches, including input–output models, macro-econometric models, hybrid input–output macro-econometric models, and general equilibrium models—the term “economy-wide” usually refers to a national level analysis, but could also apply to a sub-national region, multi-nation regions, or the world. A key common characteristic of these models is that they disaggregate the overall economy of a country or region into a number of smaller units, or optimizing agents, who in turn interact with other agents in the economy in determining the use of inputs for production, and the outcomes of markets for goods. These economic agents include industries, service providers, households, governments, and many more. Such a holistic general equilibrium modeling approach is particularly useful for understanding and measuring social costs, a key aim in most cost–benefit analyses (CBAs) of water infrastructure investments when the project or program will have non-marginal impacts and current market prices will be impacted and an appropriately detailed social accounting matrix is available. This article draws on examples from recent work on low- and middle-income countries (LMICs) and provides an outline of available resources that are necessary to conduct an economy-wide modeling analysis. LMICs are the focus of larger water resource investment potential in the 21st century, including large-scale hydropower, irrigation, and drinking water supply. A step-by-step approach is illustrated and supports the conclusion that conditions exist to apply these models much more broadly in LMICs to enhance CBAs.

Humanity has been modifying the natural water cycle by building large-scale water infrastructure for millennia. For most of that time, the principles of hydraulics and control theory were only imperfectly known. Moreover, the feedback from the artificial system to the natural system was not taken into account, either because it was too small to notice or took too long to appear. In the 21st century, humanity is all too aware of the effects of our adaptation of the environment to our needs on the planetary system as a whole. It is necessary to see the environment, both natural and hman-made as one integrated system. Moreover, due to the legacy of the past, the behaviour of the man-madeparts of this system needs to be adapted in a way that leads to a sustainable ecosystem. The water cycle plays a central role in that ecosystem. It is therefore essential that the behaviour of existing and planned water infrastructure fits into the natural system and contributes to its well-being. At the same time, it must serve the purpose for which it was constructed. As there are no natural feedbacks to govern its behaviour, it will be necessary to create such feedbacks, possibly in the form of real-time control systems. To do so, it would be beneficial if all persons involved in the decision process that establishes the desired system behaviour understand the basics of control systems in general and their application to different water systems in particular. This article contains a discussion of the prerequisites for and early development of automatic control of water systems, an introduction to the basics of control theory with examples, a short description of optimal control theory in general, a discussion of model predictive control in water resource management, an overview of key aspects of automatic control in water resource management, and different types of applications. Finally, some challenges faced by practitioners are mentioned.