Search Results

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.

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.