Every chip fabricated in a semiconductor plant needs ultrapure water. Most nuclear reactors need water as a coolant and neutron moderator. Every artificial intelligence (AI) data center drinks between 1 million and 5 million gallons of water a day, with thirst often peaking during drought.

Water runs through every technology priority the United States has named, yet the word does not appear once in “Launching the Genesis Mission,” an executive order (EO) released in November 2025. As described in the EO, the Genesis Mission is a “dedicated, coordinated national effort to unleash a new age of AI-accelerated innovation and discovery that can solve the most challenging problems of this century.”

Led by the Department of Energy (DOE), the initiative aims to build an integrated AI framework that would harness federal scientific datasets to accelerate breakthroughs in advanced manufacturing, biotechnology, critical materials, nuclear fission and fusion energy, quantum information science, and semiconductor development. The scope of the mission is comparable to that of the Manhattan Project.

Since the announcement, the DOE has listed “Predicting U.S. Water for Energy” among its 26 Genesis Mission Science and Technology Challenges. The project is also soliciting proposals in three water-related focus areas.

This framework provides a foothold for hydrology in the Genesis Mission, but it is scoped narrowly around water as a supply variable for energy production.

In reality, water is a crosscutting constraint that will help determine whether the mission’s priorities translate into deployable outcomes. The hydrology community now has a seat at the table, and if it moves first and positions water security as one of the “most challenging problems of this century,” the Genesis Mission can become the sandbox in which AI reshapes how the country measures, models, and manages water.

Making this happen will require that the DOE and the Office of Science and Technology Policy charter a hydrology workstream inside the Genesis Mission, with interagency delivery involving the U.S. Geological Survey (USGS), NOAA, the Bureau of Reclamation, the EPA, and partners at state, regional, and community levels. Here is what we think that workstream should look like:

While the existing challenges reflect some of these components, others will require coordinated effort from the hydrology community to bring into the Genesis Mission’s scope.

Build the Water Corpus Genesis Will Need

The Genesis Mission EO instructs the DOE to create an American Science and Security Platform to provide the public, scientists, agencies, and policymakers access to crucial scientific datasets.

The good news is that accessible water data systems already exist across several federal agencies and academic research centers. The USGS National Water Information System tracks real-time and historical water quality and use across the country. NASA’s Earth Science Data Systems Program provides open access to Earth science observations. NOAA’s National Water Center, the first federal facility dedicated to national water resource forecasting, operates the National Water Model, which continuously forecasts flows on 2.7 million stream reaches across the continental United States. The Catchment Attributes and Meteorology for Large-Sample Studies (CAMELS) dataset, currently hosted by the National Center for Atmospheric Research, provides data tailored for hydrological research on hundreds of river basins, and the Caravan framework pulls together multiple large-sample meteorological and hydrological datasets at a global scale.

What is missing is a unified, AI-ready repository that brings federal, state, and community data together. Building one is hard. Water data are fragmented, inconsistent, and often entirely absent. Consistent, reliable data for groundwater, withdrawals, reservoir operations, and water quality are especially difficult to obtain.

Local resistance to sharing data is real. In Texas, for example, landowners hold private property rights over groundwater and have opposed metering and reporting requirements imposed by groundwater conservation districts. In California, agricultural well owners fought metering mandates for years before the Sustainable Groundwater Management Act compelled local agencies to begin tracking withdrawals. Tribal nations face a different concern: Water data collected on Indigenous lands has been misrepresented in federal datasets that were modeled without accounting for Indian country, leading many nations to restrict access to their data as an exercise of sovereignty.

Practical steps toward building a unified AI-ready repository include tiered access and licensing for different stakeholders, clear provenance tracking for all data reported, financial and educational incentives for stakeholders for reporting, and targeted gap filling. Where measurements are missing, AI can fuse remote sensing with gauged records and operational logs—but only if the results carry honest uncertainty estimates tied to real decisions.

Get the corpus right, and it will outlive any single program name. It becomes infrastructure the country can lean on.

Develop Shared Hydrologic Foundation Models

The Genesis Mission EO directs the DOE to provide “domain-specific foundation models across the range of scientific domains covered.”

Hydrology has a head start. Long short-term memory (LSTM) networks are a key type of neural network designed to last thousands of time steps. Hydrology LSTMs trained on CAMELS data have already matched traditional conceptual models for daily streamflow discharge prediction. Open-source Neural Hydrology tools serve as baselines for regional runoff prediction. These predictions may serve as precursors to the foundation models the Genesis Mission envisions and building blocks from which they could be developed.

The process of scaling up these tools is not straightforward, however. A hydrologic investigation of snowmelt-driven streams in Colorado will not require the same spatiotemporal data as tile-drained fields in Iowa, for example. A hydrology-specific foundation model must take nuanced requirements into consideration and provide a clear path for managing and exploiting a variety of datasets.

Google’s Flood Hub shows what is possible: Its AI-enabled flood forecasts now cover more than 80 countries. However, Flood Hub’s core model code and trained weights remain proprietary, meaning researchers can use the forecasts but cannot rebuild or adapt the underlying models. Genesis, if well positioned, can fill that accessibility gap by producing foundation models for water that are reusable, reliable, and openly governed.

Build a National Water Digital Twin

The EO prescribes an integrated AI platform combining foundation models with simulation tools to stimulate AI-enabled innovations.

That architecture is exactly what a digital twin requires. Europe’s Destination Earth initiative is already building digital twins for weather extremes and nonstationary conditions on the Large Unified Modern Infrastructure (LUMI) supercomputer. The United Nations–led AI for Good initiative has prioritized water applications, warning that fragmented national efforts risk duplicating work.

If the United States aims for global strategic leadership in AI-accelerated science, water infrastructure cannot be an afterthought.

Rather than starting from scratch, a water-centric Genesis Mission would unite existing federal models—the National Water Model, reservoir simulators, and groundwater codes—in a single digital twin. AI can become the thread that stitches them together, correcting biases and providing numerical solvers to enforce mass and energy balance.

What should this twin actually do? Help a dam operator decide whether to release water ahead of a storm. Tell planners where a new data center can draw cooling water without drying up a stream. Flag which coastal defenses will fail first under rising seas.

A water digital twin earns its keep when it makes the consequences of choices visible, in terms of flows, levels, temperatures, and risks to people and ecosystems.

Turn Basins into AI Test Beds

The Genesis Mission promotes AI-directed experimentation and directs the DOE to keep a record of robotic laboratories and production facilities in which such experimentation could be conducted. Hydrological field sites belong in that inventory. The National Ecological Observatory Network already operates 81 sites with standardized measurements of meteorology, surface water, groundwater, and biodiversity. The Critical Zone Collaborative Network instruments catchments to track water-soil-vegetation interactions over decades.

Formalizing these networks as AI test beds would link field observations back into the water digital twin so that experiments and models continually sharpen each other. Imagine mobile sensors steered by AI agents during a storm or aquifer recharge experiments designed by algorithms and verified in real time. That feedback loop is what separates a useful model from a decorative one.

Expand Water Challenges on the Genesis Mission List

Earlier this year, the DOE released 26 Genesis Mission Science and Technology Challenges, and “Predicting U.S. Water for Energy” was among them. The accompanying funding call (DE-FOA-0003612) solicits proposals on cloud microphysics, coupled surface water–groundwater modeling, and seasonal to multiyear prediction, all framed around energy needs and flood resilience.

These inclusions are a significant win for a hydrology component to Genesis, but several urgent challenges sit outside their scope. Can AI close the gap between a flood forecast issued 12 hours out and the 48 hours emergency managers actually need? Can it map compound extremes, in which drought, heat, and infrastructure failure collide in the same week? Can it redesign monitoring networks so that coverage follows risk rather than where gauges happened to be installed a century ago? Integrating energy and water systems is equally urgent: Floods have caused 80% of major U.S. grid outages since 2000, while drought-driven water stress curtails cooling at thermoelectric plants and reduces hydropower output, exposing how deeply energy infrastructure depends on hydrologic extremes.

The water footprint of new AI infrastructure deserves a place on that list. A separate executive order (14318, “Accelerating Federal Permitting of Data Center Infrastructure”) is already fast-tracking expansion of data center construction, and a single hyperscale facility can consume 1 million to 5 million gallons of water daily. Emerging research shows how withdrawals at that scale can push streams below ecological thresholds during low flows.

Make Hydrology the Conscience of AI Governance

The EO directs the DOE to set data access rules and clarify policies for ownership, licensing, trade secret protections, and commercialization of products and tools associated with it.

Three principles should anchor such policies for AI use in water security.

First, Indigenous and community data rights must be embedded in every major AI water security effort, in line with the collective benefit, authority to control, responsibility, and ethics (CARE) principles for Indigenous data governance.

Second, AI’s own water footprint, through electricity generation and cooling, must be treated as a design constraint. Transparent reporting, stress-based siting, and efficiency targets will prevent hydrology in Genesis from being self-defeating.

Third, the DOE should define what failure looks like. Missing a flood crest portends loss of lives and livelihoods and breaches of treaties. Accountability standards must be measurable, and they must ask not just how accurate the forecast was on average, but who bore the cost when it was wrong.

A single executive order will not solve the country’s water security problems, and a single challenge topic will not either.

But the Genesis Mission has provided a seat at a table that did not exist 6 months ago. Whether the hydrology community treats it as a ceiling or a foundation depends on what happens next. Europe’s Destination Earth and the United Nations’ AI for Good water initiatives are already moving.

American hydrology now has a seat at the table. We should take it.

Author Information

Amobichukwu C. Amanambu (acamanambu@ua.edu), Department of Geography and the Environment, The University of Alabama, Tuscaloosa; and Jonathan Frame (jmframe@ua.edu), Department of Geological Sciences, The University of Alabama, Tuscaloosa

Citation: Amanambu, A. C., and J. Frame (2026), The Genesis Mission needs hydrology: Here’s how to incorporate it, Eos, 107, https://doi.org/10.1029/2026EO260131. Published on 28 April 2026.

This article does not represent the opinion of AGU, Eos, or any of its affiliates. It is solely the opinion of the author(s).

Text © 2026. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Since 1989, Utah’s Great Salt Lake has lost some 70% of its surface area, reducing its ecosystem services and creating stretches of drying lake bed (playa) that send toxic dust into the air.

That drying ground has also provided opportunities for scientists to survey what lies below the lake’s floor. In a study published in Geosciences, researchers revealed glimpses of fresh water and salt water, with some fresh water lurking only a few meters below the surface. The work could provide clues for conserving the lake, a crucial resource for both the ecology and the economy of the region.

Salt Lake, Fresh Water

In 2023, Michael Thorne and colleagues began using a technique known as electrical resistivity tomography (ERT), which can reveal the presence of fresh or salty water, at dozens of spots near the southern and eastern edges of the Great Salt Lake. Thorne is a geophysicist at the University of Utah in Salt Lake City and a coauthor of the new study.

The lake’s desiccation allowed the researchers to access areas where “at previous times, you would never be able to do measurements because [they] would be underwater,” said Thorne.

Establishing a network of ERT sensors requires robust fieldwork. Over the course of long days in the field, Mason Jacketta, lead author of the new study, and others placed electrodes into the ground a few meters apart, making lines that stretched hundreds of meters. Between pairs of electrodes, they measured the resistance to electrical current. Salty water, filled with electricity-conducting ions, has lower resistance than fresh water.

Paired with information on the rock and sediment beneath the surface, as well as with measurements from nearby wells, the ERT data allowed the team to work out a profile of how electrical resistance varied with depth and to figure out what kind of water seeped through pores in the ground below. The team shared the results of their work on the southern part of the lake in Geosciences, while more in-depth findings about the eastern shore will appear in an upcoming publication.

At many of the sites, Jacketta and others found fresh water near the surface.

“What this is really showing is that [fresh water is] prevalent all over the place,” said Elliot Jagniecki, a geologist at the Utah Geological Survey who wasn’t part of the work.

That fresh water was often in close proximity to patches of salty groundwater. At one spot in the southeastern part of the lake, the team found a shallow layer of brine. But right below that, at only 5 meters of depth, they encountered fresh water. At the team’s most northern study site, they found fresh water around 2 meters deep. On the southern shore, they found fresh water in some places as shallow as 2.8 meters.

Mysterious Formations

The team’s results also helped explain curious features around the Great Salt Lake, including mounds made of salt and islands made of reeds.

The lacy-looking layers of the lake’s so-called mirabilite mounds form in the winter, when the cold freezes upwelling salty water, concentrating its salts. With measurements taken next to where some mirabilite mounds form, the researchers could visualize the underground conduits that send salty water to the surface.

While mirabilite mounds form close to shore, mounds made of Phragmites reeds appear in the lake’s interior as well as along its periphery. Thorne and his colleague William Johnson first noticed these mysterious circles popping up in Google Maps more than a decade ago. When they went to investigate, they found Phragmites.

In the new work, the team placed a line for electrical resistivity tomography straight through a Phragmites mound. These reeds wouldn’t be able to survive in the lake’s briny water, Thorne said, but the team’s results showed fresh water rising right to where the invasive reeds grew thick.

“The population of Phragmites around the Great Salt Lake is really not allowing fresh groundwater to go back into the Great Salt Lake,” said study coauthor Tonie van Dam, a geophysicist at the University of Utah. The reeds suck up some 70,000 acre-feet of fresh water that could go back into the lake, she said. In “sucking up [fresh water] for their own existence,” van Dam explained, the reeds crowd out native plant species that provide habitat for native birds.

More Than a Beautiful Landscape

Overall, the study provides a new picture of the fresh and salty groundwater beneath the lake and how these resources feed what people observe at the surface.

It’s also helped to prompt other work, Thorne said, including one recent study in which researchers used a helicopter carrying a wire loop to create and sense electrical currents underground. That study, published in Scientific Reports, suggested there could be a large amount of fresh water under one part of the lake.

But that work is a proof of concept, Jagniecki said, and accessing such potential aquifers might not be sufficient to help address the lake’s current desiccation. Even if they could, refilling them could take thousands of years. “I just don’t think that’s a solution,” he said.

Saline lakes are fragile ecosystems sensitive to climate change, Jagniecki said. The Great Salt Lake harbors plenty of life, such as brine shrimp that become food for a host of migratory birds that use the lake as a stopover. Mineral extraction and the use of brine shrimp for feed in aquaculture are important drivers of Utah’s economy.

Getting a better understanding of how saline lake systems function could be helpful in conserving them and maintaining the resources they provide humans, Jagniecki explained.

“It’s actually more than that. It’s a beautiful landscape,” he said.

—Carolyn Wilke, Science Writer

Citation: Wilke, C. (2026), What’s below the Great Salt Lake? More water, Eos, 107, https://doi.org/10.1029/2026EO260127. Published on 21 April 2026.

Text © 2026. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.