The development of information infrastructures that make ecological research data available has increased in recent years, contributing to fundamental changes in ecological research. Science and Technology Studies (STS) and the subfield of Infrastructure Studies, which aims at informing infrastructures’ design, use, and maintenance from a social science point of view, provide conceptual tools for understanding data infrastructures in ecology. This perspective moves away from the language of engineering, with its discourse on physical structures and systems, to use a lexicon more “social” than “technical” to understand data infrastructures in their informational, sociological, and historical dimensions. It takes a holistic approach that addresses not only the needs of ecological research but also the diversity and dynamics of data, data work, and data management. STS research, having focused for some time on studying scientific practices, digital devices, and information systems, is expanding to investigate new kinds of data infrastructures and their interdependencies across the data landscape. In ecology, data sharing and data infrastructures create new responsibilities that require scientists to engage in opportunities to plan, experiment, learn, and reshape data arrangements. STS and Infrastructure Studies scholars are suggesting that ecologists as well as data specialists and social scientists would benefit from active partnerships to ensure the growth of data infrastructures that effectively support scientific investigative processes in the digital era.
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Data Infrastructures in Ecology: An Infrastructure Studies Perspective
Florence Millerand and Karen S. Baker
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Machine Learning Tools for Water Resources Modeling and Management
Giorgio Guariso and Matteo Sangiorgio
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.
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Moving to General Equilibrium: The Role of CGEs for Economic Analysis of Water Infrastructure Projects
Kenneth M. Strzepek and James E. Neumann
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.
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Optimal and Real-Time Control of Water Infrastructures
Ronald van Nooijen, Demetris Koutsoyiannis, and Alla Kolechkina
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.
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Sustainable Management of Groundwater
Stephen Foster and John Chilton
This chapter first provides a concise account of the basic principles and concepts underlying scientific groundwater management, and it then both summarises the policy approach to developing an adaptive scheme of management and protection for groundwater resources that is appropriately integrated across relevant sectors and assesses the governance needs, roles and planning requirements to implement the selected policy approach.
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Wastewater Reclamation and Recycling
Soyoon Kum and Lewis S. Rowles
Across the globe, freshwater scarcity is increasing due to overuse, climate change, and population growth. Increasing water security requires sufficient water from diverse water resources. Wastewater can be used as a valuable water resource to improve water security because it is ever-present and usually available throughout the year. However, wastewater is a convoluted solution because the sources of wastewater can vary greatly (e.g., domestic sewage, agricultural runoff, waste from livestock activity, and industrial effluent). Different sources of wastewater can have vastly different pollutants, and mainly times, it is a complex mixture. Therefore, wastewater treatment, unlike drinking water treatment, requires a different treatment strategy. Various wastewater sources can be reused through wastewater reclamation and recycling, and the required water quality varies depending on the targeted purpose (e.g., groundwater recharge, potable water usage, irrigation). One potential solution is employing tailored treatment schemes to fit the purpose. Assorted physical, chemical, and biological treatment technologies have been established or developed for wastewater reclamation and recycle. The advancement of wastewater reclamation technologies has focused on the reduction of energy consumption and the targeted removal of emerging contaminants. Beyond technological challenges, context can be important to consider for reuse due to public perception and local water rights. Since the early 1990s, several global wastewater reclamation examples have overcome challenges and proved the applicability of wastewater reclamation systems. These examples showed that wastewater reclamation can be a promising solution to alleviate water shortages. As water scarcity becomes more widespread, strong global initiatives are needed to make substantial progress for water reclamation and reuse.
