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AI-Enhanced Hydropower Systems: Smart Dams for a Resilient Future

This paper focuses on water-energy, using AI for smart, holistic hydropower operations to enhance water for energy resilience. The existing challenges include (a) increasing demand for water and electricity, requiring a shift to flexible, real-time hydropower operations due to ch...

Varadharajan, C., Ajami, N., Brodie, E., Ciulla, F., Falco, N., Feldman, D., Newcomer, M., Dwivedi, D., Li, Y., Nakata, R., Nakata, N., Nico, P., Williams, K., Mahoney, M., Ramakrishnan, L., and Cholia, S.

A Multi-Scale Integrated Assessment Model for How Emerging Biotechnologies Can Contribute to Decoupling Energy and Water Systems

Energy-water (EW) systems in the U.S. are deeply interconnected through complex networks vulnerable to disruptions, such as when a regional drought simultaneously impacts hydropower generation, thermoelectric production, and water availability across multiple sectors, producing kn...

Patelli, P., Solander, K., Gonzalez-Esquer, R., Xu, C., Bower, C., Davis, R., Carruthers, J., and Thomas, J.

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.

Natural Materials as a Pathway to Affordable and Scalable Membrane Separation Technologies

This white paper focuses on developing affordable and scalable membrane separation technologies derived from natural materials for selective extraction of critical minerals (CMs), such as lithium, manganese, and barium, from geothermal brines. This approach integrates with geother...

Ho, T., and Sharan, P.

Synergistic Pathways for Water Security in Emerging Sectors: Data Centers and Modern Nuclear Facilities

Stand-alone data centers, nuclear-powered data centers, and novel nuclear facilities such as fusion plant and small modular reactors are water intensive sectors poised to rapidly expand, presenting a significant challenge to the water-for-energy nexus. The potential for large-scal...

Catalano, A., Duan, Z., Quimby, C., Wang, T., Chini, C., Premathilake, L., and Pope, J.

Advancing Energy-Water Resilience through Integrated Science and Technology for Natural Infrastructure Management

This white paper focuses on water for energy. The challenge is that ecosystems are under increasing stress from rising temperatures, prolonged drought, and intensifying disturbances like wildfire, insects, and land-use change, which can impact the hydrologic capacity of watershed...

Dickman, L., Atchley, A., Giovando, J., Fluet-Chouinard, E., Xu, C., and McPherson, T.

Unlocking the energy-water interdependency through bio-augmented treatment of produced water for agricultural re-use and aquifer recharge

The supply and demand for energy and water within the US is increasingly intertwined. Several examples of this include the large volumes of water consumed by hydraulic fracking, the reliance of energy-intensive groundwater pumping in agriculture, and the significant water and powe...

Solander, K., Patelli, P., Gonzalez-Esquer, R., Xu, C., Bower, C., and Kisekka, I.

Revolutionizing Water-Energy Infrastructure Resilience through Distributed Sensing and Digital Twins

This white paper addresses the intersection of Water for Energy and Energy for Water, proposing foundational technology to enhance the resilience and operational longevity of critical national assets, including hydropower facilities and marine energy systems. The core challenge i...

Luo, L.

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.