Integrated water-energy management is crucial for balancing socioeconomic and environmental objectives in multi-reservoir systems. Multipurpose reservoirs support clean energy production, recreation, navigation, and flood protection but also disrupt natural water flows and fish migration. As hydropower's role evolves with grid decarbonization, managing these tradeoffs becomes increasingly complex. An integrated model combining economic and environmental factors is essential to inform how to adapt hydropower operations effectively to complement decarbonization of the electric grid. However, existing literature lacks such comprehensive models. This study introduces an integrated water-energy optimization model using the Columbia River Basin (CRB) and Mid-Columbia (Mid-C) energy market as a case study. The model couples a simulation of operations of 48 CRB reservoirs with a unit commitment/economic dispatch model of the California and West Coast Power system (CAPOW). We employ Direct Policy Search (DPS) and a multi-objective evolutionary algorithm (MOEA) to optimize four objectives: maximize economic benefits from energy production, minimize fossil fuel electricity generation, minimize environmental flow violations, and minimize peak flood levels. Our findings reveal that the integrated model discovers superior operational strategies compared to existing rules, with some policies outperforming current operations on all objectives simultaneously. Insights from the optimized policies include strategies for improved coordination of reservoir operations using storage and inflow data, and the strategic timing of water releases to ensure increased hydropower production leads to less fossil fuel dependence and greater revenue. These results highlight the potential of integrated models to enhance the sustainability of hydropower operations amid a transitioning energy landscape.

Climate change is projected to increase the intensity and frequency of extremes in river basins around the world. Water infrastructure such as reservoirs are often used to buffer against these extremes, enabling more reliable water supply for human uses like irrigation. Yet this can have negative impacts on the system's ecological flows. In designing water infrastructure for human adaptation to climate change, it is important to consider whether the infrastructure is mitigating or exacerbating climate change impacts on ecological systems. Prior work has found that dams mitigate long-duration extremes but exacerbate short-duration extremes. In this study, we investigate whether reservoir operations can be designed to also yield beneficial climate adaptation outcomes for short-duration high and low flow extremes while still improving average socioeconomic and ecological objectives compared to uncontrolled conditions. We explore this research question

Water resources planning depends upon hydrologic models to estimate flows and storage under candidate engineering designs. However, such models are calibrated with limited flow data relative to the many model parameters. This may result in different equifinal parameterizations that imply different optimal designs. To assess if and how this uncertainty should be considered, we compare three methods for multi-objective optimization of green infrastructure (GI): one that designs to the most likely parameterization and two robust alternatives that use several likely parameterizations with 1) likelihood-weighted objective functions, and 2) min-max objective functions. To evaluate these methods, we set synthetic true values for model parameters, use them to simulate "observed" streamflow, and then use Bayesian calibration to estimate parametric uncertainty. We compare results from optimization to the synthetic parameterization against the three alternatives. The GI optimizations aim to minimize flooding, low flow intensification, and cost. We find the two robust methods provide objective values and decisions that are closer to those optimized to the synthetic

Simulation models of multi-sector systems are increasingly used to understand societal resilience to climate and economic shocks and change. However, multi-sector systems are also subject to numerous uncertainties that prevent the direct application of simulation models for prediction and planning, particularly when extrapolating past behavior to a nonstationary future. Recent studies have developed a combination of methods to characterize, attribute, and quantify these uncertainties for both single- and multi-sector systems. Here we review challenges and complications to the idealized goal of fully quantifying all uncertainties in a multi-sector model and their interactions with policy design as they emerge at different stages of analysis: (1) inference and model calibration; (2) projecting future outcomes; and (3) scenario discovery and identification of risk regimes. We also identify potential methods and research opportunities to help navigate the tradeoffs inherent in uncertainty analyses for complex systems. During this discussion, we provide a classification of uncertainty types and discuss model coupling frameworks to support interdisciplinary collaboration on multi-sector dynamics (MSD) research. Finally, we conclude with recommendations for best practices to ensure that MSD research can be properly contextualized with respect to the underlying uncertainties.