Seed Funding Program

[SMALL] Geothermal Play Fairway Analysis: Locating Potential Hidden Geothermal Resources in the Lost River Valley 

Lead Investigator: Ryan B. Anderson, Assistant Professor, Idaho State University, Department of Geosciences 

Co-Investigators: H. Carrie Bottenberg, Clinical Associate Professor, Director of Geotechnologies Graduate Program, Idaho State University, Department of Geosciences 

Project Summary:

Energy production in Idaho is reliant on a limited water supply. Electrical power demand in the state reached a record high in 2024 and is projected to increase a further ~50% within the next 20 years. In-state generation has historically been dominated by hydroelectric sources but has diminished significantly since 2009 as reliability and output have been affected in part by drought, changing snowpack conditions, and competing demand for agriculture uses. Identification of new sustainable and reliable energy sources that rely less on Idaho’s limited water supply are required to meet projected electrical demand. Geothermal power is one potential option as it is a renewable energy source that takes advantage of naturally heated subsurface fluid reservoirs rather than consumption of a fuel to produce steam and power a turbine, and conserves water resources by returning produced geothermal fluids back into the subsurface. Much of Idaho is positioned in a geologically favorable setting for geothermal energy production but is woefully underdeveloped compared to neighboring states. The objectives of this project are to 1) create favorability maps that identify promising areas for new geothermal development, with an emphasis on blind or hidden systems in the Lost River Valley, 2) further characterize areas identified by the favorability maps with low-cost temperatures surveys and detailed geologic mapping, 3) Identify potential resources to be further explored with higher cost tools in future studies (e.g., temperature gradient wells, geophysical surveys). 

[SMALL] Physics-Informed Machine Learning for Real-Time Reconstruction of 2D River Hydraulics in Energy-Water Systems

Lead Investigator: Angel Monsalve, Assistant Professor, Civil and Environmental Engineering, Department, University of Idaho 

Co-Investigators: Andrew Tranmer, Associate Research Professor, Center for Ecohydraulics Research, University of Idaho  

Project Summary:
Idaho’s energy-water systems face critical operational challenges where hydropower generation and irrigation energy consumption compete for limited water resources. The Boise River system alone generates 494 GWh annually through three major dams while supporting 250,000 irrigated acres requiring significant pumping energy. Current operational decisions rely on simplified 1D hydraulic models that cannot predict the 2D flow conditions critical for optimizing energy production and minimizing irrigation pumping costs. This creates a fundamental gap: energy operators lack the real-time, spatially explicit flow predictions needed to balance hydropower generation efficiency with downstream irrigation energy demands. Developed through strategic partnerships with Idaho Power Company, which manages the Boise River’s hydroelectric system, and Idaho Department of Water Resources, which regulates waterenergy allocation statewide, this project directly addresses operational needs of key energy-water decision makers. 

This project will develop physics-informed neural networks (PINNs) that transform computationally efficient 1D hydraulic models into high-resolution 2D flow predictions in seconds, enabling real-time optimization of energy-water trade-offs. By accurately predicting upstream approach flows to hydroelectric facilities and optimal irrigation diversion locations, this framework directly addresses competing energy demands within Idaho’s interconnected water systems. The innovation lies in embedding fundamental hydraulic conservation laws within machine learning architectures, maintaining physical accuracy while achieving the computational speed necessary for operational energy management decisions. 

[LARGE] Examining the downstream impact of wildfire-related sediment inputs on the water-energy nexus 

Lead Investigator: Anna Bergstrom, Assistant Professor, Geosciences Department, Boise State University 

Co-Investigators: Elowyn Yager, Professor, Center for Ecohydraulics Research, University of Idaho; Jen Pierce, Professor, Geosciences Department, Boise State University; David Huber, Senior Researcher, Boise State University  

Project Summary:
In the western United States, energy and water systems rely heavily on reservoir storage, but these systems face increasing threats from wildfire-induced sedimentation. Wildfires are increasing in frequency and severity across the region, accelerating sediment inputs to river networks and infilling reservoirs. These rapid changes create uncertainty about dam operating lifetimes and storage capacity. Idaho is particularly vulnerable, experiencing the highest average burned area in the western US with frequent large, high-severity fires. Further these fires occur in the highly erodible Idaho Batholith region, exacerbating post-fire erosional events including debris flows. Post-fire debris flows can deliver massive quantities of poorly sorted sediment directly to streams, which when transported downstream to reservoirs, fundamentally alter how managers approach flood control, hydropower generation, and irrigation water supply. 

This interdisciplinary project addresses the critical knowledge gap in understanding how wildfire and associated debris flows impact sediment supply and transport in rivers upstream of energy and water infrastructure. We will leverage and build on a historical dataset collected on the South Fork Payette River and a recent dataset collected following the 2024 Wapiti Fire in same watershed, which burned over 125,000 acres and generated numerous debris flows. Our research combines field data collection, laboratory analysis, statistical modeling, and stakeholder engagement to test two primary hypotheses: (1) immediate post-disturbance sediment rating curves will show higher concentrations and steeper slopes than historical curves, with effects decreasing over time, and (2) distance from debris flows, stream slope, watershed area, and burn severity will be key predictors of suspended sediment concentration.

[LARGE] AI-Driven State Estimation for Resilient Power and Water Systems  

Lead Investigator: Yacine Chakhchoukh, Associate Professor, Department of Electrical & Computer Engineering, University of Idaho  

Co-Investigators: Mostafa Fouda, Associate Professor, Department of Electrical and Computer Engineering, Idaho State University; Angel Monsalve, Assistant Professor Department of Civil and Environmental Engineering, University of Idaho

Project Summary
Monitoring the grid is becoming more challenging as the integration of intermittent renewable generation increases. State estimation (SE), the essential tool for offering situational awareness in utilities’ control centers, must be adapted. The literature recently proposed implementing the state estimation concept in water distribution systems to improve monitoring and control. An integrated state estimator that accounts for the coupling between water distribution and power transmission systems would improve the integrated operation, resilience, and security of both systems relative to considering them independently. The proposed project will: (1) Integrate water distribution with power transmission systems by modeling the coupling between the two systems in the state estimation formulation. (2) Using testbeds, generate realistic data from power grid and water distribution system simulators. (3) Exploit artificial intelligence techniques to improve SE performance, improving both systems’ real-time monitoring and control in the presence of distributed generation such as modular nuclear and renewable generation. AI augments the integrated SE with water distribution system (WDS) pseudo-measurements, physics-informed pump coupling, and cyber-resilient bad-data/false-data injection (FDI) detection. The result is a multi-rate powerwater estimator robust to missing/non-Gaussian measurements and topology manipulation. (4) Develop and submit proposals to NSF and other agencies such as DOE. 

[LARGE] Coupled Water and Energy Consequences of Agricultural-to-Urban Transitions in the Treasure Valley  

Lead Investigator: Kendra Kaiser, Assistant Research Professor, Soil and Water Systems Department, Director, Idaho Water Resources Research Institute, University of Idaho  

Co-Investigators: Stephanie Lenhart, Associate Professor, School of Public Service, School of Environment, and Energy Policy Institute, Boise State University; Meetpal Kukal, Assistant Professor, Soil and Water Systems Department, University of Idaho  

Project Summary
This project will quantify the coupled water–energy consequences of agricultural-to-urban land transitions in Idaho’s Treasure Valley and generate decision-support information for local and regional planners. It will combine ground-based measurements of evapotranspiration (ET) in turfgrass, satellitebased consumptive use (CU) modeling, and qualitative methods (interviews and surveys) to compare outdoor water use and associated energy demand in locations with irrigated agriculture, rural community water systems, and private domestic wells. The team will (1) develop and validate locally relevant methods to estimate consumptive use across mixed urban–agricultural landscapes, (2) estimate the energy required for water system operations under different land-use and infrastructure configurations, and (3) co-produce case studies with local partners to inform alternative futures modeling and resilience planning for the Treasure Valley. The project will support a postdoctoral researcher and a graduate student who will gain interdisciplinary training in hydrology, remote sensing, and community-engaged qualitative social science.