In recent years, several reservoirs in the western U.S. have approached or exceeded critically low storage during drought conditions; such events could be particularly impactful to water and energy availability if they are widespread across the region or exacerbated by climate change. However, projected changes in low storage and near-minimum-power-pool (MPP) risk remain poorly quantified across large spatial domains. Here, we assess simulated low storage frequency (LSF) across 94 western U.S. reservoirs using a calibrated offline hedging release model driven by projected reservoir inflows under future climate scenarios. We evaluate near-MPP risk for 30 reservoirs for which reservoir-specific thresholds are known. Projected low storage responses are spatially and seasonally heterogeneous. Across the ensemble, annual LSF declines in many future simulations, and near-MPP exposure remains limited for most reservoirs. Thus, within the climate-driven inflow projections and hedging framework, future projections do not produce a widespread increase in critically LSF across western U.S. hydropower reservoirs. This broad pattern masks important regional and seasonal differences, with elevated late-season vulnerability emerging in California and parts of the Southwest. LSF is more strongly associated with precipitation than with temperature, with drier years producing elevated risk across nearly every reservoir and warming amplifying risk in California and the Southwest. These results indicate that estimating operational threshold risks from climate-driven inflow projections remain challenging, particularly for low storage conditions that matter most for hydropower vulnerability.

Price dynamics in wholesale electricity markets are driven by supply and demand. In markets with hydroelectric dams, the timing and amount of hydropower offered can influence prices in similar ways to wind and solar power. Unlike variable renewable energy, however, the supply of hydropower in wholesale markets is a function of both water availability and operational decisions at dams. Dam operators maximize revenues in wholesale markets by aligning generation with the periods of highest expected prices, and these scheduling decisions may in turn influence prices. Here, we examine the relative importance of two types of information in predicting forward electricity prices: a) water availability at dams, in the form of short-to-medium-range hydrological forecasts; and b) hourly scheduling decisions at dams. Using softly coupled hydrologic, hydropower scheduling, and power systems models spanning the U.S. Western Interconnection, we quantify the importance of hydrologic forecast accuracy in correctly predicting wholesale electricity prices and compare this with the influence of dam operators’ own hourly scheduling decisions on realized market prices. We find that aligning hydropower generation schedules with the periods of high forecasted prices causes larger, inadvertent price forecast errors than imperfect hydrologic forecasts. This suggests that knowledge of how water is managed by dam operators within the week is more important than weekly inflow forecast errors when predicting forward electricity prices. Our findings have implications for optimal hydropower scheduling by region. Specifically, accounting for price effects is critical in markets dominated by hydropower capacity.

Hydropower facilities in the United States (US) most often have non-powered objectives, for example storage and release of water for water supply or environmental benefit, or flood control. These objectives can limit the flexibility available to hydropower operations to generate power to provide maximum benefit to the power grid. There does exist however flexibility within a week to optimize hydropower generation while still ensuring non-powered objectives are met. We examine the flexibility available to optimize generation and the value of medium-range inflow forecasts using a dynamic programing reservoir optimization model applied at ~250 hydropower facilities over the US Western Interconnection. Optimization is performed using day-ahead hourly scheduling to reflect existing electricity markets, using Locational Marginal Prices (LMPs) provided by a production cost model, and using three flavors of medium-range inflow forecasts – perfect forecasts representing an upper limit on performance, persistence forecasts representing a lower benchmark, and synthetic forecasts as a surrogate for operational streamflow forecast products. Measures of direct performance and flexibility are examined at the grid-scale for Balancing Authorities within the Western Interconnection. This study highlights where and under what conditions medium-range forecasts influence flexibility in hydropower operations which will be increasingly valuable under an evolving grid with increased renewable penetration.

Hydroelectric power generation in Western Canada significantly contributes to power grid operations of the North American Western Interconnection through substantial generation, some of which is exported to the United States (U.S.). However, the lack of publicly available hydropower generation datasets poses challenges for future market projections and resource adequacy evaluations. We present a simulation-based monthly power system model-ready hydropower generation dataset for 110 facilities in British Columbia and Alberta from 1981 to 2019. These monthly hydropower generation estimates are developed from integrated hydrologic model simulations of runoff and reservoir-operated streamflow, followed by scaling that considers diversion inflow constraints based on hydropower water license information. To address the lack of comparable hydropower generation records, we conduct step-by-step evaluations for simulated runoff, regulated streamflow, and hydropower generation using available observations or estimates. The presented hydropower dataset aims to enhance the representation of hydropower resources in Western Canada, supporting power grid system studies for the Western Interconnection of the U.S. and Canada.

Hydropower is a critical electricity resource in the United States which, in addition to low-cost electricity generation, provides valuable ancillary grid services, and supports the integration of nondispatchable weather-dependent resources (e.g., wind and solar). Despite its value to the grid, there are very few comprehensive datasets available from which to study both historical and future impacts of climate, weather driven energy droughts, and integration of other weather driven generation. In this paper, we present a hydropower generation dataset covering 1,452 hydroelectric plants in the contiguous U.S. The dataset contains monthly and weekly hydropower generation estimates for both historical (1982-2019) and future (2020-2099) periods which includes 4 future climate scenarios. In addition, this dataset provides weekly and monthly constraints such as minimum and maximum power which are particularly useful in power system models which are used to study grid reliability, transmission planning and capacity expansion.

Climate change impacts on watersheds can potentially exacerbate water scarcity issues where water serves multiple purposes including hydropower. The long-term management of water and energy resources is still mostly approached in a siloed manner at different basins or watersheds, failing to consider the potential impacts that may concurrently affect many regions at once. There is a need for a large-scale hydropower modeling framework that can examine climate impacts across adjoining river basins and balancing authorities (BAs) and provide a periodic assessment at regional to national scales. Expanding from our prior assessment only for the United States (US) federal hydropower plants, we enhance and extend two regional hydropower models to cover over 85% of the total hydropower nameplate capacity and present the first contiguous US-wide assessment of future hydropower production under Coupled Model Intercomparison Project phase 6’s high-end Shared Socioeconomic Pathway 5-8.5 emission scenario using an uncertainty-aware multi-model ensemble approach. We present regional hydropower projections, using both BA regions and US Hydrologic Subregions (HUC4s), to consistently inform the energy and water communities for two future periods—the near-term (2020–2039) and the mid-term (2040–2059) relative to a historical baseline period (1980–2019). We find that the median projected changes in annual hydropower generation are typically positive—approximately 5% in the near-term, and 10% in the mid-term. However, since the risk of regional droughts is also projected to increase, future planning cannot overly rely on the ensemble median, as the potential of severe hydropower reductions could be overlooked. The assessment offers an ensemble of future hydropower generation projections, providing regional utilities and power system operators with consistent data to develop drought scenarios, design long duration storage and evaluate energy infrastructure reliability under intensified inter-annual and seasonal variability.

As we move towards a decarbonized grid, reliance on weather-dependent energy increases as does exposure to prolonged natural resource shortages known as energy droughts. Compound energy droughts occur when two or more predominant renewable energy sources simultaneously are in drought conditions. In this study we present a methodology and dataset for examining compound wind and solar energy droughts as well as the first standardized benchmark of energy droughts across the Continental United States (CONUS) for a 2020 infrastructure. Using a recently developed dataset of simulated hourly plant level generation which includes thousands of wind and solar plants, we examine the frequency, duration, magnitude, and seasonality of energy droughts at a variety of temporal and spatial scales. Results are presented for 15 Balancing Authorities (BAs), regions of the U.S. power grid where wind and solar are must-take resources by the power grid and must be balanced. Compound wind and solar droughts are shown to have distinct spatial and temporal patterns across the CONUS. BA-level load is also included in the drought analysis to quantify events where high load is coincident with wind and solar droughts. We find that energy drought characteristics are regional and the longest droughts can last from 16 to 37 continuous hours, and up to 6 days. The longest hourly energy droughts occur in Texas while the longest daily droughts occur in California. Compound energy drought events that include load are more severe on average compared to events that involve only wind and solar. In addition, we find that compound high load events occur more often during compound wind and solar droughts that would be expected due to chance. The insights obtained from these findings and the summarized characteristics of energy drought provide valuable guidance on grid planning and storage sizing at the regional scale.

With its mountain-to-coast hydroclimate, strong influence of Pacific Ocean weather systems and climate patterns, and unique land use history with strong rural-to-urban gradients, the Puget Sound region is a natural laboratory for studying a number of complex processes in, and interactions among, different Earth and human systems. A 1-year scoping study was initiated by the Earth and Environmental Systems Modeling program of the Department of Energy’s Office of Science Biological and Environmental Research. It was intended to elucidate and highlight the rich opportunities Puget Sound offers to advance our understanding of and ability to simulate Earth system changes and human-Earth system interactions. A literature review, multi-day community workshop, and external input were used to develop this scoping study report. The report first summarizes scientific understanding and knowledge gaps associated with major regional systems, including atmosphere and climate, the land surface, coastal and marine processes, and human systems, as well as how these systems are changing over time. It then highlights some of the most notable extreme events in the region, including heat waves, atmospheric rivers, droughts, and wildfires. Finally, key research opportunities for Earth and environmental systems modeling in, above, and around Puget Sound are highlighted.

Precipitation during the Ethiopian Kiremt (June–September) season has exhibited significant interannual and multi‐decadal variability over the 20th and early 21st century. We investigate the temporal variability in the strength of the teleconnections between sea‐surface temperatures in key global oceanic regions, including the Tropical Pacific, Indian Ocean, and Tropical Atlantic, and Kiremt season precipitation at sub‐seasonal, interannual and multi‐decadal time scales. We also investigate the influence of the Madden‐Julian Oscillation (MJO). We perform a systematic analysis of 112‐year long (1901–2012) precipitation in the northern region of East Africa including the southern and central regions of Ethiopia and uncover interesting spatial, temporal and sub‐seasonal variability, and teleconnection patterns. Precipitation anomalies during wet and dry years extend throughout Northern Africa and also, during September extends over Indian subcontinent, suggesting large‐scale variability of wet/dry patterns. Wet (dry) years are accompanied by La Nina like (El Nino like) conditions in the tropical Pacific and extending into Atlantic and Indian Oceans. Through Bayesian dynamical linear modelling we find that temporal changes in seasonal precipitation correspond to changes in the strengths of sea surface temperature teleconnections, and that the relative strengths of these teleconnections rather than one dominant teleconnection influences precipitation variability. During three precipitation epochs in this region, the mid‐century pluvial, the late‐century drought, and the early 21st century, we find that changes in precipitation are related to changes in the main dynamical features of precipitation. These findings suggest that Kiremt season precipitation is in a new regime, and is of key interest to the agricultural and water resources communities who rely on accurate forecasts of precipitation to make operational decisions.