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Article

Bioeconomic Models  

Ihtiyor Bobojonov

Bioeconomic models are analytical tools that integrate biophysical and economic models. These models allow for analysis of the biological and economic changes caused by human activities. The biophysical and economic components of these models are developed based on historical observations or theoretical relations. Technically these models may have various levels of complexity in terms of equation systems considered in the model, modeling activities, and programming languages. Often, biophysical components of the models include crop or hydrological models. The core economic components of these models are optimization or simulation models established according to neoclassical economic theories. The models are often developed at farm, country, and global scales, and are used in various fields, including agriculture, fisheries, forestry, and environmental sectors. Bioeconomic models are commonly used in research on environmental externalities associated with policy reforms and technological modernization, including climate change impact analysis, and also explore the negative consequences of global warming. A large number of studies and reports on bioeconomic models exist, yet there is a lack of studies describing the multiple uses of these models across different disciplines.

Article

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.

Article

Multi-Objective Robust Planning Tools  

Jazmin Zatarain Salazar, Andrea Castelletti, and Matteo Giuliani

Shared water resource systems spark a number of conflicts related to their multi sectorial, regional, and intergenerational use. They are also vulnerable to a myriad of uncertainties stemming from changes in the hydrology, population demands, and climate change. Planning and management under these conditions are extremely challenging. Fortunately, our capability to approach these problems has evolved dramatically over the last few decades. Increased computational power enables the testing of multiple hypotheses and expedites the results across a range of planning alternatives. Advances in flexible multi-objective optimization tools facilitate the analyses of many competing interests. Further, major shifts in the way uncertainties are treated allow analysts to characterize candidate planning alternatives by their ability to fail or succeed instead of relying on fallible predictions. Embracing the fact that there are indeterminate uncertainties whose probabilistic descriptions are unknown, and acknowledging relationships whose actions and outcomes are not well-characterized in planning problems, have improved our ability to perform diligent analysis. Multi-objective robust planning of water systems emerged in response to the need to support planning and management decisions that are better prepared for unforeseen future conditions and that can be adapted to changes in assumptions. A suite of robustness frameworks has emerged to address planning and management problems in conditions of deep uncertainty. That is, events not readily identified or that we know so little about that their likelihood of occurrence cannot be described. Lingering differences remain within existing frameworks. These differences are manifested in the way in which alternative plans are specified, the views about how the future will unfold, and how the fitness of candidate planning strategies is assessed. Differences in the experimental design can yield diverging conclusions about the robustness and vulnerabilities of a system. Nonetheless, the means to ask a suite of questions and perform a more ambitious analysis is available in the early 21st century. Future challenges will entail untangling different conceptions about uncertainty, defining what aspects of the system are important and to whom, and how these values and assumptions will change over time.

Article

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.

Article

Review of the State of the Art in Analysis of the Economics of Water Resources Infrastructure  

Marc Jeuland

Water resources represent an essential input to most human activities, but harnessing them requires significant infrastructure. Such water control allows populations to cope with stochastic water availability, preserving uses during droughts while protecting against the ravages of floods. Economic analysis is particularly valuable for helping to guide infrastructure investment choices, and for comparing the relative value of so called hard and soft (noninfrastructure) approaches to water management. The historical evolution of the tools for conducting such economic analysis is considered. Given the multimillennial history of human reliance on water infrastructure, it may be surprising that economic assessments of its value are a relatively recent development. Owing to the need to justify the rapid deployment of major public-sector financing outlays for water infrastructure in the early 20th century, government agencies in the United States—the Army Corps of Engineers and the Bureau of Reclamation—were early pioneers in developing these applications. Their work faced numerous technical challenges, first addressed in the drafting of the cost-benefit norms of the “Green Book.” Subsequent methodological innovation then worked to address a suite of challenges related to nonmarket uses of water, stochastic hydrology, water systems interdependencies, the social opportunity cost of capital, and impacts on secondary markets, as well as endogenous sociocultural feedbacks. The improved methods that have emerged have now been applied extensively around the world, with applications increasingly focused on the Global South where the best infrastructure development opportunities remain today. The dominant tools for carrying out such economic analyses are simulation or optimization hydroeconomic models (HEM), but there are also other options: economy wide water-economy models (WEMs), sociohydrological models (SHMs), spreadsheet-based partial equilibrium cost-benefit models, and others. Each of these has different strengths and weaknesses. Notable innovations are also discussed. For HEMs, these include stochastic, fuzz, and robust optimization, respectively, as well as co-integration with models of other sectors (e.g., energy systems models). Recent cutting-edge work with WEMs and spreadsheet-based CBA models, meanwhile, has focused on linking these tools with spatially resolved HEMs. SHMs have only seen limited application to infrastructure valuation problems but have been useful for illuminating the paradox of flood management infrastructure increasing the incidence and severity of flood damages, and for explaining the co-evolution of water-based development and environmental concerns, which ironically then devalues the original infrastructure. Other notable innovations are apparent in multicriteria decision analysis, and in game-theoretic modeling of noncooperative water institutions. These advances notwithstanding, several issues continue to challenge accurate and helpful economic appraisal of water infrastructure and should be the subject of future investigations in this domain. These include better assessment of environmental and distributional impacts, incorporation of empirically based representations of costs and benefits, and greater attention to the opportunity costs of infrastructure. Existing tools are well evolved from those of a few decades ago, supported by enhancements in scientific understanding and computational power. Yet, they do appear to systematically produce inflated estimations of the net benefits of water infrastructure. Tackling existing shortcomings will require continued interdisciplinary collaboration between economists and scholars from other disciplines, to allow leveraging of new theoretical insights, empirical data analyses, and modeling innovations.