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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.
Accelerating the Acquisition of Irrigation Modernization Benefits
Accelerating the modernization of off-farm irrigation water delivery infrastructure can hasten the acquisition of many energy-water benefits: water and energy conservation, energy generation and grid reliability, and additional water conservation and increased agricultural yields ...
Jorgensen, J., Saulsbury, B., Cafferty, K., Golden, B., Kueny, K., Hou, H., and Niazi, H.
Monitoring, Planning, and Management of Water Quality and Quantity for Resilient Water and Energy Systems
Both resilient systems where water is used for energy production and energy is used for treatment and operation depend not only on the quantity of water, but also quality and environmental health. Addressing gaps in environmental health and infrastructure condition/capability data...
Hansen, C., DeRolph, C., Allen-Dumas, M., Griffiths, N., Matson, P., Pilla, R., Siddik, A., Stevenson, L., and Johnson, R.
Hybrid Bioenergy and Hydropower Systems: Leveraging Heterogeneous Biomass and Wastes from Watersheds
Sandia National Laboratories and Idaho National Laboratory (INL) explore the water-for-energy nexus in this white paper, focusing on the impact of heterogeneous watershed waste materials on hydropower facilities. They identify key challenges--such as pile burning, wildfires, and d...
Quiroz-Arita, C., and Paudel, R.
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.