The Monsoon Asia region is home to ten of the world’s biggest rivers, supporting the lives of 1.7 billion people who rely on streamflow for water, energy, and food. Yet, a synthesized understanding of multi-centennial streamflow variability for this region is lacking. To fill this gap, we produce the first large scale streamflow reconstruction over Monsoon Asia (62 stations in 16 countries, 813 years of mean annual flow). In making this reconstruction, we develop a novel, automated, climate-informed, and dynamic reconstruction framework that is skillful over most of the region. We show that streamflow in Monsoon Asia is spatially coherent, owing to common drivers from the Pacific, Indian, and Atlantic Oceans. We also show how these oceanic teleconnections change over space and time. By characterizing past and present hydroclimatic variability, we provide a platform for assessing the impact of future climatic changes and informing water management decisions.

Most hydropower utilities rely on flow forecasts to manage the water resources of their reservoir systems and to help marketers and schedulers make efficient use of power generating resources. Flow forecast providers and dam operators typically assess the value of flow forecasts by assessing the skill of the forecasts in a verification exercise. Although there are many flow forecasting approaches available—from physics-based approaches associated with statistical pre and post processors and data assimilation, to emerging machine-learning based approaches—there is little consensus on how to choose the best forecast product. Nor are there established methods for translating forecast skill—a summary statistic amalgamating multiple types of errors —to forecast value (benefits or avoided cost) as perceived by a marketer or scheduler. In this work we develop such an approach by combining a water resources management model with a power grid model. Flow forecasts are developed at 85 locations for a varying range of skills, from perfect, to persistent and in-between. Using reservoir and power grid simulations over the Western U.S., we propagate flow forecasts through the power grid model, mapping flow forecast skill to regional hydropower revenues, production costs and carbon emissions. We develop a deeper understanding of the influence of regional and seasonal differences in markets and hydrologic dynamics on forecast value. We discuss future research directions to integrate hydrologic forecasts into decision-making at the utility and wider system scale.

A growing literature emphasizes the importance of integrating climate change impacts into electricity system planning. Rising average temperatures can increase and shift electricity demand while reducing generator and transmission efficiency. Changes to water availability and quality can reduce the output of thermally cooled generators and hydropower. Electric power grids across the US and globally are undergoing transformational changes that present new opportunities and challenges to reliability assurance. However, electric utilities and system operators have limited internal capabilities to incorporate these effects into planning practices. This work addresses gaps in utility and system planner practices by integrating climate-water-electricity expertise from universities and U.S. Department of Energy National Laboratories with electricity system planners and stakeholders in the Western Electricity Coordinating Council (WECC). Using a highly collaborative approach, global climate model data, high-resolution hydrology models, and long-term electric sector capacity expansion tools are employed to analyze a range of climate outcomes for future electricity scenarios aligned with recent WECC planning studies. Doing so allows WECC to expand its climate-agnostic planning assessments to consider how future temperature and precipitation patterns could influence generation and transmission planning. We explore how changes to climate-water conditions can affect power plant investment and operation, system economics, and environmental impacts, providing an expanded perspective on interconnection-wide decision making under climate uncertainty.