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Highlights • We extract typical behaviors of water reservoir operators from observational data. • We cluster the reservoir operators based on their behavioral profile. • We discover behavioral profiles that are vulnerable to drought conditions. Abstract The large number of dammed rivers worldwide emphasizes the need to couple models of natural processes with models describing human behaviors. However, such behavioral models are often simplistic and lack proper validation against observational data. In this work, we contribute a new approach to infer the typical operations of water reservoirs from historical observations, using data-driven behavioral modeling based on eigenbehavior analysis. The approach is demonstrated using monthly storage data from 172 reservoirs in California, USA. Results show that the proposed method identifies four typical behavioral profiles, which are strongly linked to key features of the reservoirs. Moreover, we show how the identified models can be used for discovering behavioral profiles, and associated reservoir characteristics, that are vulnerable to drought conditions. [ABSTRACT FROM AUTHOR]

Water resources systems face irreducible uncertainty in supply and demand, requiring policies to respond to changing conditions on multiple timescales. For both short-term operation and long-term adaptation, thresholds or “decision triggers”, where a policy links observed indicators to actions, have featured prominently in recent studies. There remains a need for a general method to conceptualize threshold-based policies in an easily interpretable structure, and a corresponding search algorithm to design them. Here we propose a conceptual and computational framework where policies are formulated as binary trees, using a simulation-optimization approach. Folsom Reservoir, California serves as an illustrative case study, where policies define the thresholds triggering flood control and conservation actions. Candidate operating rules are generated across an ensemble of climate scenarios, incorporating indicator variables describing longer-term climate shifts to investigate opportunities for adaptation. Policy tree optimization and corresponding open-source software provide a generalizable, interpretable approach to policy design under uncertainty. [ABSTRACT FROM AUTHOR]

Worldwide, reservoirs are used to buffer against both extreme floods and extreme droughts. However, operations favoring each of these and other management objectives conflict. Fortunately, tradeoffs in operations can often be mitigated by using hydrologic forecasts to condition release decisions, and significant research has investigated the value of using forecasts for this purpose. However, these studies have struggled with how to quantify forecast value in multi-objective contexts where the benefits may accrue unevenly to different objectives that stakeholders value differently. To address this research gap, we introduce a new metric for quantifying forecast value on multi-objective problems: the average improvement on each objective when moving from a solution that does not use forecast information (a baseline solution) to solutions that do use forecast information and outperform the baseline solution on all objectives. We call this metric "average domination" and use it to investigate whether and how the value of forecast information for multi-objective reservoir operations changes with different flood protection constraints, using the Red River Basin in Vietnam as an example. To assess this, we design multi-objective operations at four reservoirs in the basin both with and without forecast information conditioning release decisions under constraints that the operations ensure protection to downstream water levels with either 100-year or 500-year return levels. Based on the average domination metric, we find that in the Red River Basin, the value of forecast information for all objectives is the same or greater under the more severe 500-year flood design requirement. These findings, identified by our new metric, illustrate that it can be especially beneficial to condition operations on forecast information when stakeholders strongly favor higher levels of risk aversion for flood protection. • We develop a new metric for valuing forecast information on multi-objective problems. • We use this metric to assess forecast value for reservoir operations. • We find forecasts hold more value when constrained to protect to more severe floods. [ABSTRACT FROM AUTHOR]

In river basin systems, power asymmetry is often responsible of inefficient and unbalanced water allocations. Climate change and anthropogenic pressure will possibly exacerbate such disparities as the dominant party controls an increasingly limited shared resource. In this context, the deployment of cooperation mechanisms giving greater consideration to a balanced distribution of the benefits, while improving system-wide efficiency, may be desirable. This often implies the intervention of a third party (e.g., the river basin water authority) imposing normative constraints (e.g., a minimum release) on the party in the dominant position. However, this imposition will be more acceptable to the dominant party if coupled with some form of compensation. For a public agency, compensation may be burdensome, especially when the allowance is triggered by natural events whose timing and magnitude are highly uncertain. In this context, index-based insurance contracts may represent a viable alternative and reduce the cost of achieving socially desirable outcomes. In this paper, we develop a hybrid cooperation mechanism composed of i) a direct normative constraint imposed by a regulator, and ii) an indirect financial tool, an index-based insurance contract, to be used as a compensation measure. The approach is developed for the Lake Como multi-purpose water system, Italy: a complex Alpine river basin, supporting several hydropower reservoirs and finally flowing into a regulated lake which supplies water to several downstream uses, mostly irrigated agriculture. The system is characterized by a manifest geographic power asymmetry: the upstream hydropower companies are free to release their stored water in time irrespective of the timing of the downstream demands. This situation can lead to financial losses by the downstream users and undesirable social outcomes. Results suggest that financial instruments may offer a reliable and relatively inexpensive alternative to other forms of compensation, and thereby favor more balanced management of multi-purpose water systems characterized by power asymmetry. This finding is especially relevant in times when granting of licenses to use/withdrawal water are often being reviewed with attention to environmental protection and equity issues. [ABSTRACT FROM AUTHOR]

Water reservoir systems may become more adaptive and reliable to external changes by enlarging the information sets used in their operations. Models and forecasts of future hydro-climatic and socio-economic conditions are traditionally used for this purpose. Nevertheless, the identification of skillful forecasts and models might be highly critical when the system comprises several processes with inconsistent dynamics (fast and slow) and disparate levels of predictability. In these contexts, the direct use of observational data, describing the current conditions of the water system, may represent a practicable and zero-cost alternative. This paper contrasts the relative contribution of state observations and perfect forecasts of future water availability in improving multipurpose water reservoirs operation over short- and long-term temporal scales. The approach is demonstrated on the snow-dominated Lake Como system, operated for flood control and water supply. The Information Selection Assessment (ISA) framework is adopted to retrieve the most relevant information to be used for conditioning the operations. By explicitly distinguishing between observational dataset and future forecasts, we quantify the relative contribution of current water system state estimates and perfect streamflow forecasts in improving the lake regulation with respect to both flood control and water supply. Results show that using the available observational data capturing slow dynamic processes, particularly the snow melting process, produces a 10% improvement in the system performance. This latter represents the lower bound of the potential improvement, which may increase to the upper limit of 40% in case skillful (perfect) long-term streamflow forecasts are used. [ABSTRACT FROM AUTHOR]

Model Predictive Control (MPC) has recently gained increasing interest in the adaptive management of water resources systems due to its capability of incorporating disturbance forecasts into real-time optimal control problems. Yet, related literature is scattered with heterogeneous applications, case-specific problem settings, and results that are hardly generalized and transferable across systems. Here, we systematically review 149 peer-reviewed journal articles published over the last 25 years on MPC applied to water reservoirs, open channels, and urban water networks to identify common trends and open challenges in research and practice. The three water systems we consider are inter-connected, multi-purpose and multi-scale dynamical systems affected by multiple hydro-climatic uncertainties and evolving socioeconomic factors. Our review first identifies four main challenges currently limiting most MPC applications in the water domain: (i) lack of systematic benchmarking of MPC with respect to other control methods; (ii) lack of assessment of the impact of uncertainties on the model-based control; (iii) limited analysis of the impact of diverse forecast types, resolutions, and prediction horizons; (iv) under-consideration of the multi-objective nature of most water resources systems. We then argue that future MPC applications in water resources systems should focus on addressing these four challenges as key priorities for future developments. [ABSTRACT FROM AUTHOR]

Deep uncertainty in future climate, socio-economic and technological conditions poses a great challenge to medium-long term decision making. Recently, several approaches have been proposed to identify solutions that are robust with respect to a large ensemble of deeply uncertain future scenarios. In this paper, we introduce ROSS (Robust Optimal Scenario Selection), a novel algorithm that uses an active learning approach for adaptively selecting the smallest scenario subset to be included into a robust optimization process. ROSS contributes a twofold novelty in the field of robust optimization under deep uncertainty. First, it allows the computational requirements for the generation of robust solutions to be considerably reduced with respect to traditional optimization methods. Second, it allows the identification of the most informative regions of the scenario set containing the scenarios to be included in the optimization process for generating a robust solution. We test ROSS on the real case study of robust planning of an off-grid hybrid energy system, combining diesel generation with renewable energy sources and storage technologies. Results show that ROSS enables computational requirements to be reduced between 23% to 84% compared with traditional robust optimization methods, depending on the complexity of the robustness metrics considered. It is also able to identify very small regions of the scenario set containing the most informative scenarios for generating a robust solution. • A novel algorithm for robust planning under deep uncertainty is proposed. • The algorithm uses active learning for selecting the smallest subset of informative scenarios. • Computational time is significantly reduced with respect to traditional methods. • Most informative scenarios are highlighted and exploited within the optimization process. [ABSTRACT FROM AUTHOR]

• A dynamic, multi-objective approach to optimize off-grid water-energy systems is proposed. • Interdependency between system design and operations is considered. • The nexus between water and energy systems is modelled dynamically. • The approach outperforms the state-of-the-art non-dynamic, least cost approach. • The optimal solutions identified limit investment costs and environmental impacts. Small Mediterranean islands are remote, off-grid communities characterized by carbon intensive electricity systems coupled with high energy consuming desalination technologies to produce potable water. The aim of this study is to propose a novel dynamic, multi-objective optimization approach for improving the sustainability of small islands through the introduction of renewable energy sources. The main contributions of our approach include: (i) dynamic modelling of desalination plant operations, (ii) joint optimization of system design and operations, (iii) multi-objective optimization to explore trade-offs between potentially conflicting objectives. We test our approach on the real case study of the Italian Ustica island by means of a comparative analysis with a traditional non-dynamic, least cost optimization approach. Numerical results show the effectiveness of our approach in identifying optimal system configurations, which outperform the traditional design with respect to different sustainability indicators, limiting the structural interventions, the investment costs and the environmental impacts. In particular, the optimal dynamic solutions able to satisfy the whole water demand allow high levels of penetration of renewable energy sources (up to more than 40%) to be reached, reducing the net present cost by about 2–3 M€ and the CO 2 emissions by more than 200 tons/y. [ABSTRACT FROM AUTHOR]

Advanced metering technologies coupled with informatics creates an opportunity to form digital multi-utility service providers. These providers will be able to concurrently collect a customers’ medium-high resolution water, electricity and gas demand data and provide user-friendly platforms to feed this information back to customers and supply/distribution utility organisations. Providers that can install low-cost integrative systems will reap the benefits of derived operational synergies and access to mass markets not bounded by historical city, state or country limits. This paper provides a vision of the required transformative process and features of an integrated multi-utility service provider covering the system architecture, opportunities and benefits, impediments and strategies, and business opportunities. The heart of the paper is focused on demonstrating data modelling processes and informatics opportunities for contemporaneously collected demand data, through illustrative examples and four informative water-energy nexus case studies. Finally, the paper provides an overview of the transformative R&D priorities to realise the vision. [ABSTRACT FROM AUTHOR]

Reservoir operations are central to our ability to manage river basin systems serving conflicting multi-sectoral demands under increasingly uncertain futures. These challenges motivate the need for new solution strategies capable of effectively and efficiently discovering the multi-sectoral tradeoffs that are inherent to alternative reservoir operation policies. Evolutionary many-objective direct policy search (EMODPS) is gaining importance in this context due to its capability of addressing multiple objectives and its flexibility in incorporating multiple sources of uncertainties. This simulation-optimization framework has high potential for addressing the complexities of water resources management, and it can benefit from current advances in parallel computing and meta-heuristics. This study contributes a diagnostic assessment of state-of-the-art parallel strategies for the auto-adaptive Borg Multi Objective Evolutionary Algorithm (MOEA) to support EMODPS. Our analysis focuses on the Lower Susquehanna River Basin (LSRB) system where multiple sectoral demands from hydropower production, urban water supply, recreation and environmental flows need to be balanced. Using EMODPS with different parallel configurations of the Borg MOEA, we optimize operating policies over different size ensembles of synthetic streamflows and evaporation rates. As we increase the ensemble size, we increase the statistical fidelity of our objective function evaluations at the cost of higher computational demands. This study demonstrates how to overcome the mathematical and computational barriers associated with capturing uncertainties in stochastic multiobjective reservoir control optimization, where parallel algorithmic search serves to reduce the wall-clock time in discovering high quality representations of key operational tradeoffs. Our results show that emerging self-adaptive parallelization schemes exploiting cooperative search populations are crucial. Such strategies provide a promising new set of tools for effectively balancing exploration, uncertainty, and computational demands when using EMODPS. [ABSTRACT FROM AUTHOR]

The Soil & Water Assessment Tool (SWAT) is a useful model for evaluating socio-ecological tradeoffs and analyzing coupled natural-human system dynamics in agricultural watersheds. However, reservoir operating options in SWAT are limited. This study advances the representation of reservoir operations in SWAT by adding an option for closed-loop, multi-reservoir operating policies that can be optimized using Evolutionary Multi-Objective Direct Policy Search. This enables water managers to evaluate the tradeoffs across more coordinated reservoir operations in SWAT while capitalizing on the model's ability to physically simulate hydrological processes better than traditional reservoir simulation models. Comparing our advanced reservoir operations with SWAT's existing operating options in the Omo River basin of Ethiopia, we find a wider range of policies for managing conflicting stakeholder objectives that better compromise across them and are more robust to climate change. These advances to SWAT's reservoir module show promise for informing integrated water resources management. • We integrate coordinated, closed-loop reservoir operations into the Soil & Water Assessment Tool. • This allows water managers to evaluate tradeoffs of smarter water management options. • It also improves the modeling of hydrologic impacts in traditional reservoir models. [ABSTRACT FROM AUTHOR]

Globally, the pressures of expanding populations, climate change, and increased energy demands are motivating significant investments in re-operationalizing existing reservoirs or designing operating policies for new ones. These challenges require an understanding of the tradeoffs that emerge across the complex suite of multi-sector demands in river basin systems. This study benchmarks our current capabilities to use Evolutionary Multi-Objective Direct Policy Search (EMODPS), a decision analytic framework in which reservoirs’ candidate operating policies are represented using parameterized global approximators (e.g., radial basis functions) then those parameterized functions are optimized using multi-objective evolutionary algorithms to discover the Pareto approximate operating policies. We contribute a comprehensive diagnostic assessment of modern MOEAs’ abilities to support EMODPS using the Conowingo reservoir in the Lower Susquehanna River Basin, Pennsylvania, USA. Our diagnostic results highlight that EMODPS can be very challenging for some modern MOEAs and that epsilon dominance, time-continuation, and auto-adaptive search are helpful for attaining high levels of performance. The ϵ-MOEA, the auto-adaptive Borg MOEA, and ϵ-NSGAII all yielded superior results for the six-objective Lower Susquehanna benchmarking test case. The top algorithms show low sensitivity to different MOEA parameterization choices and high algorithmic reliability in attaining consistent results for different random MOEA trials. Overall, EMODPS poses a promising method for discovering key reservoir management tradeoffs; however algorithmic choice remains a key concern for problems of increasing complexity. [ABSTRACT FROM AUTHOR]