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Abstract The state of the art for optimal water reservoir operations is rapidly evolving, driven by emerging societal challenges. Changing values for balancing environmental resources, multisectoral human system pressures, and more frequent climate extremes are increasing the complexity of operational decision making. Today, reservoir operations benefit from technological advances, including improved monitoring and forecasting systems as well as increasing computational power. Past research in this area has largely focused on improving solution algorithms within the limits of the available computational power, using simplified problem formulations that can misrepresent important systemic complexities and intersectoral interactions. In this study, we review the recent literature focusing on how the operation design problem is formulated, rather than solved, to address existing challenges and take advantage of new opportunities. This paper contributes a comprehensive classification of over 300 studies published over the last years into distinctive categories depending on the adopted problem formulation, which clarifies consolidated methodological approaches and emerging trends. Our analysis also suggests that control policy design methods may benefit from broadening the types of information that is used to condition operational decisions, and from using emulation modeling to identify low‐order, computationally efficient surrogate models capturing realistic representations of river basin systems' complexity in order to isolate key decision‐relevant processes. These advances in reservoir operations hold significant promise for better addressing the challenges of conflicting human pressures and a changing world, which is particularly important, given the renewed interest in dam construction globally. 

Abstract Previous studies investigating deep decarbonization of bulk electric power systems and wholesale electricity markets have not sufficiently explored how future grid pathways could affect the grid's vulnerability to hydrometeorological uncertainty on multiple timescales. Here, we employ a grid operations model and a large synthetic weather ensemble to “stress test” a range of future grid pathways for the U.S. West Coast developed by ReEDS, a well‐known capacity planning model. Our results show that gradual changes in the underlying capacity mix from 2020 to 2050 can cause significant “re‐ranking” of weather years in terms of annual wholesale electricity prices (with “good” years becoming bad, and vice versa). Nonetheless, we find the highest and lowest ranking price years in terms of average electricity price remain mostly tied to extremes in hydropower availability (streamflow) and load (summer temperatures), with the strongest sensitivities related to drought. Seasonal dynamics seen today involving spring snowmelt and hot, dry summers remain well‐defined out to 2050. In California, future supply shortfalls in our model are concentrated in the evening and occur mostly during periods of high temperature anomalies in late summer months and in late winter; in the Pacific Northwest, supply shortfalls are much more strongly tied to negative streamflow anomalies. Under our more robust sampling of stationary hydrometeorological uncertainty, we also find that the ratio of dis‐patchable thermal (i.e., natural gas) capacity to wind and solar required to ensure grid reliability can differ significantly from values reported by ReEDS. 

Abstract The challenge of adapting water resources systems to uncertain hydroclimatic and socioeconomic conditions warrants a dynamic planning approach. Recent studies have designed policies with structures linking infrastructure and management actions to threshold values of indicator variables observed over time. Typically, one or more of these components are held fixed while the others are optimized, constraining the flexibility of policy generation. Here we develop a framework to address this challenge by designing and testing dynamic adaptation policies that combine indicators, actions, and thresholds in a flexible structure. The approach is demonstrated for a case study of northern California, where a mix of infrastructure, management, and operational adaptations are considered over time in response to an ensemble of nonstationary hydrology and water demands. We first identify a subset of non‐dominated policies that are robust to held‐out scenarios, and then analyze their most common actions and indicators compared to non‐robust policies. Results show that the robust policies are not differentiated by the actions they select, but show substantial differences in their indicator variables, which can be interpreted in the context of physical hydrologic trends. In particular, the most frequent statistical transformations of indicator variables highlight the balance between adapting quickly versus correctly. Additionally, we determine the indicators most frequently associated with each action, as well as the distribution of action timing across scenarios. This study presents a new and transferable problem framing for adaptation under uncertainty in which indicator variables, actions, and policy structure are identified simultaneously during the optimization. 

Abstract The United States (U.S.) West Coast power system is strongly influenced by variability and extremes in air temperatures (which drive electricity demand) and streamflows (which control hydropower availability). As hydroclimate changes across the West Coast, a combination of forces may work in tandem to make its bulk power system more vulnerable to physical reliability issues and market price shocks. In particular, a warmer climate is expected to increase summer cooling (electricity) demands and shift the average timing of peak streamflow (hydropower production) away from summer to the spring and winter, depriving power systems of hydropower when it is needed the most. Here, we investigate how climate change could alter interregional electricity market dynamics on the West Coast, including the potential for hydroclimatic changes in one region (e.g., Pacific Northwest (PNW)) to “spill over” and cause price and reliability risks in another (e.g., California). We find that the most salient hydroclimatic risks for the PNW power system are changes in streamflow, while risks for the California system are driven primarily by changes in summer air temperatures, especially extreme heat events that increase peak system demand. Altered timing and amounts of hydropower production in the PNW do alter summer power deliveries into California but show relatively modest potential to impact prices and reliability there. Instead, our results suggest future extreme heat in California could exert a stronger influence on prices and reliability in the PNW, especially if California continues to rely on its northern neighbor for imported power to meet higher summer demands. 

Abstract In land surface models (LSMs), the hydraulic properties of the subsurface are commonly estimated according to the texture of soils at the Earth's surface. This approach ignores macropores, fracture flow, heterogeneity, and the effects of variable distribution of water in the subsurface oneffectivewatershed‐scale hydraulic variables. Using hydrograph recession analysis, we empirically constrain estimates of watershed‐scale effective hydraulic conductivities (K) and effective drainable aquifer storages (S) of all reference watersheds in the conterminous United States for which sufficient streamflow data are available (n = 1,561). Then, we use machine learning methods to model these properties across the entire conterminous United States. Model validation results in high confidence for estimates of log(K) (r² > 0.89; 1% < bias < 9%) and reasonable confidence forS(r² > 0.83; −70% < bias < −18%). Our estimates of effectiveKare, on average, two orders of magnitude higher than comparable soil‐texture‐based estimates of averageK, confirming the importance of soil structure and preferential flow pathways at the watershed scale. Our estimates of effectiveScompare favorably with recent global estimates of mobile groundwater and are spatially heterogeneous (5–3,355 mm). Because estimates ofSare much lower than the global maximums generally used in LSMs (e.g., 5,000 mm in Noah‐MP), they may serve both to limit model spin‐up time and to constrain model parameters to more realistic values. These results represent the first attempt to constrain estimates of watershed‐scale effective hydraulic variables that are necessary for the implementation of LSMs for the entire conterminous United States. 

Abstract Hydrologic variability poses an important source of financial risk for hydropower‐reliant electric utilities, particularly in snow‐dominated regions. Drought‐related reductions in hydropower production can lead to decreased electricity sales or increased procurement costs to meet firm contractual obligations. This research contributes a methodology for characterizing the trade‐offs between cash flows and debt burden for alternative financial risk management portfolios, and applies it to a hydropower producer in the Sierra Nevada mountains (San Francisco Public Utilities Commission). A newly designed financial contract, based on a snow water equivalent depth (SWE) index, provides payouts to hydropower producers in dry years in return for the producers making payments in wet years. This contract, called a capped contract for differences (CFD), is found to significantly reduce cash flow volatility and is considered within a broader risk management portfolio that also includes reserve funds and debt issuance. Our results show that solutions relying primarily on a reserve fund can manage risk at low cost but may require a utility to take on significant debt during severe droughts. More risk‐averse utilities with less access to debt should combine a reserve fund with the proposed CFD instrument in order to better manage the financial losses associated with extreme droughts. Our results show that the optimal risk management strategies and resulting outcomes are strongly influenced by the utility's fixed cost burden and by CFD pricing, while interest rates are found to be less important. These results are broadly transferable to hydropower systems in snow‐dominated regions facing significant revenue volatility. 

Abstract California’s Central Valley is one of the world’s most productive agricultural regions. Its high-value fruit, vegetable, and nut crops rely on surface water imports from a vast network of reservoirs and canals as well as groundwater, which has been substantially overdrafted to support irrigation. The region has undergone a shift to perennial (tree and vine) crops in recent decades, which has increased water demand amid a series of severe droughts and emerging regulations on groundwater pumping. This study quantifies the expansion of perennial crops in the Tulare Lake Basin, the southern region of the Central Valley with limited natural water availability. A gridded crop type dataset is compiled on a 1 mi²spatial resolution from a historical database of pesticide permits over the period 1974–2016 and validated against aggregated county-level data. This spatial dataset is then analyzed by irrigation district, the primary spatial scale at which surface water supplies are determined, to identify trends in planting decisions and agricultural water demand over time. Perennial crop acreage has nearly tripled over this period, and currently accounts for roughly 60% of planted area and 80% of annual revenue. These trends show little relationship with water availability and have been driven primarily by market demand. From this data, we focus on the increasing minimum irrigation needs each year to sustain perennial crops. Results indicate that under a range of plausible future regulations on groundwater pumping ranging from 10% to 50%, water supplies may fail to consistently meet demands, increasing losses by up to 30% of annual revenues. More broadly, the datasets developed in this work will support the development of dynamic models of the integrated water-agriculture system under uncertain climate and regulatory changes to understand the combined impacts of water supply shortages and intensifying irrigation demand.