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Hardening Forested Watersheds to Wildfire to Enhance Downstream Water Quality for Critical Energy Infrastructure

This white paper focuses on water-for-energy stability, emphasizing the risks to energy infrastructure from water quality impacts due to severely burned forested watersheds. The primary challenge is that energy production in the U.S. is vulnerable to surface water quality, with hi...

Crockett, J., and Krofcheck, D.

Advanced Manufacturing of Polymer Composites for Energy-Water Resilience and Infrastructure Modernization

This white paper addresses the intersection of energy and water systems through innovations in advanced polymer composites and convergent manufacturing to enhance national waterpower infrastructure resilience, reduce lifecycle energy and water intensity, and strengthen domestic ma...

Hassen, A.

Integrated Water-Energy-Economics Framework for Public Water System Resilience

This paper highlights key energy-for-water challenges in U.S. public water systems (PWS): aging infrastructure, cost recovery gaps, hydrologic vulnerabilities, and high-demand users like data centers--which concentrate near population centers and existing PWS. It underscores the n...

Siddik, M., Ahmad, N., Guaita, S., and Chinthavali, S.

A Hybrid AI-Optimization Framework for Resilient Hydropower Operations to Support Grid Stability, Extreme Weather Management, and Large-Scale Industrial Loads

This white paper supports a two-stage decision-support framework that links fast, probabilistic inflow forecasts from modern machine learning and generative AI with multistage stochastic optimization for hydropower scheduling. By propagating uncertainty from prediction into operat...

Ploussard, Q., and Feinstein, J.

Gaps Between Western U.S. Reservoir Inflows and Headwater Precipitation Timing, Amount, and Phase

Snowmelt from mountain headwater basins supplies approximately 80% of the runoff to reservoirs in the western United States (WUS), and is therefore a key component of western hydropower. Yet predicting runoff from hydrometeorological data in complex terrain (and its sensitivity to...

Rudisill, W., and Feldman, D.

Enabling Energy-Water Resilience in U.S. Data Centers: Water-Aware Grid Planning and Operations with Infrastructure-Compatible Cooling

Rapid, concentrated expansion of AI data centers is reshaping electricity demand profiles, local infrastructure requirements, and regional resource constraints. These facilities are often sized from tens to hundreds of megawatts with multi-gigawatt campus clusters. They are relati...

Zhou, Z., Wu, M., Qiu, F., Muehleisen, R., Zhao, D., Yan, E., and Worek, W.

Field-Validated, Grid-Interactive Irrigation for Energy-Water Resilience in Puerto Rico's Agricultural Experimental Stations

Puerto Rico's irrigation infrastructure depends primarily on electrically powered pumping and distribution systems, making agricultural water delivery highly vulnerable to grid instability and frequent outages. This white paper proposes the development and validation of grid-inter...

Plaza, J., and Andrade, F.

Employing AI-Enabled Prognostics for Energy-Water Resilience

This white paper focuses on strengthening the resilience and efficiency of hydropower systems at the energy-water nexus through AI-enabled prognostics and hardware-in-the-loop (HIL) simulation. It emphasizes integrating digital intelligence with physical infrastructure to optimize...

Zhao, S., Qiu, F., and Agalgaonkar, Y.

Mitigating Wildfire-Driven Hydrological Impacts on Energy Generation

Following a wildfire, increased sedimentation and altered flow regimes can disrupt water quantity and quality for years, impacting water users (e.g., municipal, industrial, agricultural) and undermining energy reliability, efficiency, and infrastructure longevity. Upstream restora...

Catalano, A., Ferencz, S., Michaels, R., and Hester, E.

Agentic Optimization for Resilience in Hydropower Reservoir Systems

The growing electricity demand in the U.S. requires a resilient energy strategy. Hydropower is a major component of that strategy but is subject to environmental and human stressors that impact its dependability. AI-driven reinforcement learning, called "Agentic" learning, presen...

Schwenk, J., Garcia-Cardona, C., Bennett, K., Singh, S., and Brelsford, C.

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