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.

Peter Beard is a unique and often confusing figure in the history of wildlife photography. But without doubt, his book, The End of the Game, remains one of the most important works of early conservation.

[Read More]

Source: Earth’s Future

Greenhouse gas emissions are heating our atmosphere and oceans, and turning seawater more acidic. One of the myriad expected impacts of these conditions is a reduction in farming yields of shellfish, such as oysters and mussels. Coastal communities worldwide rely on these organisms for their economies and as a major food supply. However, exactly how climate change will affect oyster and mussel farming is not yet clear.

Using a novel experimental setup, Pernet et al. report new projected yields of oyster and mussel farming in the Mediterranean Sea for the years 2050, 2075, and 2100. Their results suggest that by 2050, yields of both shellfish will drop dramatically, with mussel production perhaps collapsing altogether.

Most prior studies have assessed shellfish in tank experiments under fairly idealized conditions that do not adequately reflect real-world aquaculture settings. This research team took a different approach. They developed a novel system for exposing oysters and mussels in tanks to realistic conditions using water pumped in from the sea, meaning the animals would experience fluctuations in acidity, temperature, and nutrients similar to those experienced by shellfish on nearby farms.

The researchers set up 12 experimental tanks on the French Mediterranean coast in the Thau lagoon, where shellfish farming is key for the local economy. In three tanks, oysters and mussels were exposed directly to pumped-in seawater under present, ambient conditions. The rest of the tanks received seawater that was first warmed and acidified in accordance with widely accepted climate projections for 2050, 2075, and 2100, with three tanks for each year.

The survival rate of oysters in the tanks with predicted 2100 conditions dropped by 7% compared to present rates, and their growth rate dropped by 40%. These results suggest that yields of farmed oysters in the Mediterranean could drop severely over the next several decades.

The mussels fared even worse. In fact, compared to oysters, mussels have a lower range of water temperatures in which they can survive, and the upper limit is already being exceeded in some summertime Mediterranean waters, leading to mass-mortality events. In the experimental tanks under present conditions, mussel mortality was about 40%, and nearly all mussels died under predicted 2050 conditions.

On the basis of these findings, the researchers call for the urgent development of strategies to protect Mediterranean shellfish farming, such as relocating mussel-farming operations to the cooler waters of open seas or developing cofarming with algae to increase resilience to climate change. (Earth’s Future, https://doi.org/10.1029/2025EF005992, 2025)

—Sarah Stanley, Science Writer

Citation: Stanley, S. (2026), Mediterranean mussel farming could collapse by 2050, Eos, 107, https://doi.org/10.1029/2026EO260121. Published on 17 April 2026.

Text © 2026. AGU. 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.

When I set out for Waterton National Park in Alberta, Canada, I imagined fall forests resplendent in golds, accented by oranges and reds. The smell of leaves composting into the earth and the peace of the earth quieting into winter. What I found was a blackened landscape, still deeply scarred by the 2017 Kenow Fire eight years ago.

When the foliage is gone, the structure lies bare. Undulations ripple along the mountainsides; seeps and drainages stand out. 

The rhythms of the forest are speaking in structure, not color. This gift in this landscape of open vistas is long sightlines – a dream for wildlife spotting.

The Kenow Fire ignited with a lightning strike and burned slowly until September 11, 2017, when it blew up in critically dry conditions, surging from 30,000 to 104,000 acres overnight, overtaking Waterton National Park. The Kenow Wildfire was a fire of exceptional severity exceeding every fire since the Park’s records began in 1700. In the end, half of the vegetated land and 80% of the hiking trails in the Park were burnt. 

In almost all of this burn area, most or all of the organic matter was seared away by the fire. The topsoil burned away to a depth of three feet.

Dense conifer forests are being replaced by young aspens and shrubs such as Saskatoon berry, thimbleberry, and huckleberry. It’s a bear’s delight! The conifers will come back, too. They grow relatively slowly.

Fire is necessary, natural, “normal” for these forests. Our human misunderstanding and resulting meddling have given rise to an increase in these large, catastrophic (by human standards) fires. This was a dramatic fire. The recovery is being documented and studied, providing insights into the land’s history and the resilience of nature.It’s often not what I expected, but it’s always an adventure.

If you’re interested in purchasing or licensing any images you see here, please email me at SNewenham at exploringnaturephotos.com, and I’ll make it happen.

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The post Waterton Park and the 2017 Kenow Fire appeared first on Exploring Nature by Sheila Newenham.