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Seasonal streamflow forecasts are becoming widely used to improve water reservoir operations, especially in areas where climate teleconnections enable predictability on medium and long lead times. Most existing studies have focused on the assimilation of forecasts into operational decision models, an approach that typically banks on predeveloped forecasts to optimize water release decisions. However, this approach may overlook the potential synergies that stand in co-developing forecast and decision-making models. In other words, the opportunities that lie in coupling both forecast and operational decision models have not yet been explored. Here, we address this gap and contribute a novel approach building on the Evolutionary Multi-Objective Direct Policy Search algorithm to design forecast and decision-making models together. The proposed approach is benchmarked against operating policies not informed by any forecast, as well as by forecast-informed policies relying on predeveloped forecasts (data-driven and perfect). Numerical experiments are conducted on the Angat-Umiray water resources system, Philippines, which is operated primarily for ensuring municipal water supply to Metro Manila and irrigation supply to a large agricultural district. Our results show that the integrated design of forecast models and control policies provides a performance gain with respect to policies informed by predesigned forecasts. This result is particularly interesting because the skill of the integrated forecast models is lower than that of the predeveloped ones, thus suggesting that more accurate forecasts do not necessarily produce better water system operations. Overall, our analysis represents a step towards a deeper integration of streamflow forecast and reservoir operation models. [ABSTRACT FROM AUTHOR]

A resurgence of dam planning and construction is under way in river basins where untapped hydropower potential could meet growing energy demands. Despite calls for more comprehensive evaluation of dam projects, most dams continue to be planned with traditional methods that neglect interdependencies between planning and management and cumulative impacts of multiple new dams. Using the transboundary Zambezi Watercourse as a case study where competing demands for water, energy, and food are increasing, we contribute to a novel dam planning approach that integrates sequencing of planned reservoirs with adaptive operations. While additional hydropower capacity reduces structural energy deficits, operating polices emerge as the main driver of human‐environmental tradeoffs, so much so that single‐objective operating policy selection may lead to erroneous perceptions of tradeoffs across infrastructure options. Furthermore, compared to an operation and sequencing strategy that singularly maximizes hydropower, seeking compromise through operations while constructing dams early improves environmental and irrigation objectives by 50% and 80% with an 8% loss in hydropower. Alternatively, seeking compromise only through delayed dam construction yields modest environmental and irrigation improvements of 6% and 9%, respectively, with a 22% loss in hydropower. The robustness of this result is tested under an ensemble of stochastic streamflow where environmental flow and irrigation deficits are found more sensitive to operations than shifts in water availability. The predominance of operating policies is relevant for improving multi‐objective dam planning in other river basins already fragmented by dams built in the twentieth century. Key Points: A two‐part multi‐objective framework is presented that jointly optimizes the sequencing and operations of multipurpose reservoirsOne or two existing reservoirs in a river basin may dominate human‐environmental tradeoffs with implications for planning new damsReservoir operating policies eclipse reservoir network configurations and sequencing in balancing conflicting objectives [ABSTRACT FROM AUTHOR]

Participatory decision-making is a well-established approach to address the increasing pressure on water systems induced by growing multi-sectoral demands and increased competition among different water users. However, most existing approaches search for system-wise efficient solutions and do not quantify their distributional effect among the stakeholders. In this work, we investigate how to operationalize equity principles to design improved water systems operations that better balance efficiency and justice. More specifically, we explore the extent to which the inclusion of equity principles reshapes the space of efficient solutions. Numerical experiments are conducted on the Lake Como system, Italy, operated primarily for flood control and irrigation water supply while also providing recreation and river ecosystem services. Our results show how incorporating equity considerations into the design of water system operations enriches the solution space by generating more compromise solutions than those obtained using a traditional multi-objective optimization. Moreover, we find that including equity in the operating policy design can indirectly improve the performance of marginalized sectors, such as recreation and ecosystem, which are not explicitly considered by the current lake operation. Lastly, we illustrate how the aggregation of multi-sectoral interests into an equity index strongly shapes our results. Thus, eliciting the preference structure of stakeholders and policymakers becomes paramount for the identification of a fair balance across competing interests. This work bridges the gap between multi-objective optimization approaches and equity-informed decision-making for real-world water resources planning and management, providing an effective tool to promote efficient and equitable policies. [ABSTRACT FROM AUTHOR]

Forum papers are thought-provoking opinion pieces or essays founded in fact, sometimes containing speculation, on a civil engineering topic of general interest and relevance to the readership of the journal. The views expressed in this Forum article do not necessarily reflect the views of ASCE or the Editorial Board of the journal. [ABSTRACT FROM AUTHOR]

Participatory decision-making is a well-established approach to address the increasing 5 pressure on water systems induced by growing multi-sectoral demands and increased competition among different water users. Yet, most existing approaches search for system-wise efficient solutions and do not quantify their distributional effect among the stakeholders. In this work, we investigate how to operationalize equity principles to design improved water systems operations that better balance efficiency and justice. More specifically, we explore to which extent the inclusion of equity principles reshapes the space of efficient solutions. Numerical experiments are conducted on the Lake Como system, Italy, operated primarily for flood control and irrigation water supply while also providing recreation and river ecosystem services. Our results show how incorporating equity considerations into the design of water system operations enriches the solution space by generating more compromise solutions than those obtained using a traditional multi-objective optimization. Moreover, we find that including equity in the operating policy design can indirectly improve the performance of marginalized sectors, such as recreation and ecosystem, which are not explicitly considered by the current lake operation. Lastly, we illustrate how the aggregation of multisectoral interests into an equity index strongly shapes our results. Eliciting the preference structure of stakeholders and policymakers thus becomes paramount for the identification of a fair balance across competing interests. This work bridges the gap between multi-objective optimization approaches and equity-informed decision-making for real-world water resources planning and management, providing an effective tool to promote efficient and equitable policies. [ABSTRACT FROM AUTHOR]

In state-of-the-art energy systems modelling, reservoir hydropower is represented as any other thermal power plant: energy production is constrained by the plant's installed capacity and a capacity factor calibrated on the energy produced in previous years. Natural water resource variability across different temporal scales and the subsequent filtering effect of water storage mass balances are not accounted for, leading to biased optimal power dispatch strategies. In this work, we aim at introducing a novelty in the field by advancing the representation of reservoir hydropower generation in energy systems modelling by explicitly including the most relevant hydrological constraints, such as time-dependent water availability, hydraulic head, evaporation losses, and cascade releases. This advanced characterization is implemented in an open-source energy modelling framework. The improved model is then demonstrated on the Zambezi River Basin in the South Africa Power Pool. The basin has an estimated hydropower potential of 20,000 megawatts (MW) of which about 5,000 MW has been already developed. Results show a better alignment of electricity production with observed data, with a reduction of estimated hydropower production up to 35% with respect to the baseline Calliope implementation. These improvements are useful to support hydropower management and planning capacity expansion in countries richly endowed with water resource or that are already strongly relying on hydropower for electricity production. [ABSTRACT FROM AUTHOR]

Most water reservoir operators make use of forecasts to inform their decisions and enhance water systems flexibility and resilience by anticipating hydrological extremes. Yet, despite numerous candidate hydro‐meteorological variables and forecast horizons may potentially be beneficial to operations, the best information set for a given problem is often not evident. Additionally, in multipurpose systems characterized by multiple demands with varying vulnerabilities and temporal scales, this information set might change according to the objective tradeoff. In this work, we contribute a novel method to learn the optimal policy representation (i.e., policy input set) by combining a feature selection routine with a multiobjective Direct Policy Search framework in order to retrieve the best policy input set online (i.e., while learning the policy) and dynamically with the objective trade‐off. The selected policy search routine is the Neuro‐Evolutionary Multi‐Objective Direct Policy Search (NEMODPS) which generates flexible policy shapes adaptive to online changes in the input set. This approach is demonstrated on the case study of Lake Como (Italy), where the operating objectives are highly heterogeneous in their dynamics (fast and slow) and vulnerabilities (wet and dry extremes). We show how varying objectives, and tradeoffs therein, benefit from a different policy representation, ultimately yielding remarkable results in terms of conflict mitigation between different users. More informed policies, moreover, show higher robustness when re‐evaluated across a suite of different hydrological conditions. Key Points: We introduce a novel method to define an optimal input set for a multipurpose dam operating policy that varies with the objective trade‐offBetter informed policies are able to mitigate conflicts between water users and achieve system‐wide benefitsThe addition of information in policy design increases the policies robustness toward extreme hydrological conditions [ABSTRACT FROM AUTHOR]

Recent improvements in initialization procedures and representation of large-scale hydrometeorological processes have contributed to advancing the accuracy of hydroclimatic forecasts, which are progressively more skillful over seasonal and longer timescales. These forecasts are potentially valuable for informing strategic multisector decisions, including irrigated agriculture, for which they can improve crop choices and irrigation scheduling. In this operational context, the accuracy associated with the forecast system setup does not necessarily yield proportional marginal benefit, as this is also affected by how forecasts are employed by end users. This paper aims at quantifying the value of hydroclimatic forecasts in terms of potential economic benefit to the end users, which allows for the inference of a relation between gains in forecast skill and gains in end user profit. We also explore the sensitivity of this benefit to both forecast system setup and end user behavioral factors. These analyses are supported by an evaluation framework demonstrated on the Lake Como system (Italy), a regulated lake operated for flood protection and irrigation supply. Our framework relies on an integrated modeling chain composed of three building blocks: bias-adjusted seasonal meteorological forecasts are used as input to the continentally calibrated E-HYPE hydrological model; predicted lake inflows are used for conditioning the daily lake operations; and the resulting lake releases feed an agricultural model to estimate the net profit of the farmers in a downstream irrigation district. Results suggest that despite the gain in average conditions being negligible, informing the operations of Lake Como based on seasonal hydrological forecasts during intense drought episodes allows about 15 % of the farmers' profit to be gained with respect to a baseline solution not informed by any forecast. Moreover, our analysis suggests that behavioral factors capturing different perceptions of risk and uncertainty significantly impact the quantification of the benefit to the end users, whereby the estimated forecast value is potentially undermined by different levels of end user risk aversion. Lastly, our results show an intricate skill-to-value relation modulated by the underlying hydrologic conditions, which is well aligned over an exponential function in dry years, while the gains in profit are almost insensitive to the improvements in forecast skill in wet years. [ABSTRACT FROM AUTHOR]

Worldwide, conflict over shared water resources is exacerbated by population growth, economic development, and climate change. In multipurpose water systems, stakeholders can face higher financial risks as a consequence of the higher costs or lower revenues arising from increased hydrological variability and recurrent extreme events. In this context, financial risk hedging tools able to bundle together the uncorrelated risks of wet and dry periods faced by different stakeholders may be an improved solution to both foster cooperation and manage the financial losses associated with extreme events. In this work, we explore the potential of risk diversification strategies, involving an index insurance contract solution, to simultaneously manage financial risk in a multipurpose water system prone to both drought and flood risk. Risk diversification can allow for reduced insurance premiums in situations in which the bundled risks are uncorrelated. Using an integrated operational and financial model, we characterize the financial risk associated with the water management policies and design a risk management strategy that bundles together the uncorrelated risk of drought and flood events. The approach is demonstrated using a case study on Lake Maggiore, a subalpine transboundary regulated lake whose management is controversial due to numerous and competing human activities. In particular, we focus on the ongoing conflict among the lake shore population, affected by flood risk, and the downstream farmers' districts, facing drought related losses. Results indicate that bundling uncorrelated risks from competing users is beneficial to both promoting insurance premium affordability and facilitating collaboration schemes at the catchment scale. Key Points: Index insurance can reduce financial vulnerability of competing water users to extreme weather eventsA novel framework bundling uncorrelated flood and drought financial risk in a shared water system is proposedA bundled contract can push a water system to a higher level of insurance coverage through cooperation [ABSTRACT FROM AUTHOR]

Atmospheric oscillations are known to drive the large‐scale variability of hydrometeorological extremes in Europe, which can trigger flood events and losses. However, to date there are no studies that have assessed the combined influence of different large‐scale atmospheric oscillations on the probabilities of flood losses occurring. Therefore, in this study we examine the relationship between five indices of atmospheric oscillation and four classes of flood losses probabilities at subregional European scales. In doing so, we examine different combinations of atmospheric oscillations, both synchronous and seasonally lagged. By applying logistic regressions, we aim to identify regions and seasons where probabilities of flood losses occurring can be estimated by indices of atmospheric oscillation with higher skill than historical probabilities. We show that classes of flood losses can be predicted by synchronous indices of atmospheric oscillation and that in some seasons and regions lagged relationships may exist between the indices of atmospheric oscillation and the probability of flood losses. Furthermore, we find that some models generate increased (or decreased) probability of flood losses occurring when the indices are at their extreme positive or negative phases. A better understanding of the effects of atmospheric oscillations on the likelihood of flood losses occurring represents a step forward in achieving flood resilience in Europe. For instance, improved early predictions of the indices that represent such atmospheric oscillations, or the evidence of a lagged relationship between their teleconnections and floods, can significantly contribute to mitigating the socioeconomic burden of floods. Plain Language Summary: From season to season or year to year, the climate in Europe varies. Some years we see above (or below) average rainfall and river flows in different locations, resulting from atmospheric and oceanic circulations. Every year, such variability in the climate can cause extreme events such as flooding, which accounts for high economic losses in Europe. The impact of flooding can be reduced when reliable risk information is available to steer preventative risk reduction measures. However, to date, we have a limited understanding of the links between atmospheric oscillations and the impacts of floods. Therefore, we examine the relationship between multiple indices of atmospheric oscillations and flood losses. We show that the indices of atmospheric oscillations have links with flood losses in several seasons and that some of the flood losses can be predicted one season ahead. The results provide a better understanding of the combined effect of atmospheric oscillation on flood losses and show how impact‐based information can be used to improve flood risk management practices. Key Points: We aim to assess whether the probability of flood losses occurring can be estimated by indices of atmospheric oscillations in EuropeWe show that the probability of flood losses occurring can change by 100% depending on the phase of the index of the atmospheric oscillationSome of the flood losses can be predicted one season ahead because a lagged relationship may exist between the variables investigated [ABSTRACT FROM AUTHOR]

Climate change introduces substantial uncertainty to water resources planning and raises the key question: when, or under what conditions, should adaptation occur? A number of recent studies aim to identify policies mapping future observations to actions—in other words, framing climate adaptation as an optimal control problem. This paper uses the control paradigm to review and classify recent dynamic planning studies according to their approaches to uncertainty characterization, policy structure, and solution methods. We propose a set of research gaps and opportunities in this area centered on the challenge of characterizing uncertainty, which prevents the unambiguous application of control methods to this problem. These include exogenous uncertainty in forcing, model structure, and parameters propagated through a chain of climate and hydrologic models; endogenous uncertainty in human‐environmental system dynamics across multiple scales; and sampling uncertainty due to the finite length of historical observations and future projections. Recognizing these challenges, several opportunities exist to improve the use of control methods for climate adaptation, namely, how problem context and understanding of climate processes might assist with uncertainty quantification and experimental design, out‐of‐sample validation and robustness of optimized adaptation policies, and monitoring and data assimilation, including trend detection, Bayesian inference, and indicator variable selection. We conclude with a summary of recommendations for dynamic water resources planning under climate change through the lens of optimal control. [ABSTRACT FROM AUTHOR]

Increasingly variable hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide. These trends emphasize the need of accurate medium‐ to long‐term predictions to timely prompt anticipatory operations. Despite in some locations global climate oscillations and particularly the El Niño Southern Oscillation (ENSO) may contribute to extending forecast lead times, in other regions there is no consensus on how ENSO can be detected, and used as local conditions are also influenced by other concurrent climate signals. In this work, we introduce the Climate State Intelligence framework to capture the state of multiple global climate signals via artificial intelligence and improve seasonal forecasts. These forecasts are used as additional inputs for informing water system operations and their value is quantified as the corresponding gain in system performance. We apply the framework to the Lake Como basin, a regulated lake in northern Italy mainly operated for flood control and irrigation supply. Numerical results show the existence of notable teleconnection patterns dependent on both ENSO and the North Atlantic Oscillation over the Alpine region, which contribute in generating skilful seasonal precipitation and hydrologic forecasts. The use of this information for conditioning the lake operations produces an average 44% improvement in system performance with respect to a baseline solution not informed by any forecast, with this gain that further increases during extreme drought episodes. Our results also suggest that observed preseason sea surface temperature anomalies appear more valuable than hydrologic‐based seasonal forecasts, producing an average 59% improvement in system performance. Key Points: We introduce the Climate State Intelligence framework to capture the state of multiple climate signals and improve seasonal forecastStrong influence of divergent SST patterns dependent on ENSO and NAO phases is detected over the Alpine regionPreseason SST anomalies appear more valuable than hydrologic‐based seasonal forecasts for informing reservoir operations [ABSTRACT FROM AUTHOR]