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People with meniscus tears who underwent surgery had poorer knee function and worse osteoarthritis after 10 years than those who did not
A common knee surgery for cartilage damage does not benefit patients and may lead to worse outcomes, a 10-year trial suggests.
The study tracked outcomes for patients treated for a meniscus tear, who were given a partial meniscectomy, one of the most common orthopaedic surgeries. Their trajectories were compared with patients who had randomly been assigned to receive “sham surgery”, in which no procedure was carried out.
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© Photograph: Amorn Suriyan/Getty Images
© Photograph: Amorn Suriyan/Getty Images
© Photograph: Amorn Suriyan/Getty Images
© The Guardian, 22 Apr. 2026
In the contiguous United States, crop irrigation, municipal water supplies, and thermoelectric power generation use more than 224 billion gallons of fresh water every day. Conducting water research or making decisions about water use, until now, often required referencing datasets across various agencies. The U.S. Geological Survey (USGS) National Water Availability Assessment Data Companion (NWDC), announced this week, aims to streamline this process. In part, the tool is designed to help decisionmakers better understand the balance between how high demand and limited supply affect water availability in their communities.
“While the United States has abundant water nationally, regional imbalances between supply and demand may create water challenges affecting millions of Americans,” said lead scientist Shirley Leung in a USGS press release. “What once required significant resources and time can now be done in minutes, giving communities of all sizes the same foundation for water planning.”
The lower 48 states are home to about 80,000 sub-watersheds, from those in the arid southwest to the Great Lakes Basin, where about 84% of North America’s surface fresh water is located. According to the USGS, the NWDC is the first tool that integrates information about water availability in individual watersheds at a national scale.
The tool is designed to complement Water Data for the Nation (WDFN), another USGS product that consolidates observational data from the agency’s thousands of local monitoring stations gathering data on streams, lakes, reservoirs, precipitation, water quality, and groundwater. The new tool uses modeling to fill in spatial and temporal gaps between the observations made at these stations.
Water managers, researchers, agricultural experts, and others can use the NWDC to compare watershed conditions, identify seasonal patterns in water use, or to create data visualizations of statewide water use, for example. Though the tool currently covers only the contiguous United States, it will soon be extended to Alaska, Hawaii, and Puerto Rico, according to the USGS.
David Tarboton, a professor of civil engineering at the Utah Water Research Laboratory, said he was “intrigued” by the new tool, and is interested in examining the data its model produces.
While Tarboton was disappointed that the tool’s most recent available data are from 2020, “having a sort of integrated, wall-to-wall dataset that’s consistently produced is very valuable,” he said. He works, in part, in the areas of hydroinformatics and data sharing, and noted that the modern methods the agency is using to share the data could be useful in developing automated tools.
—Emily Gardner (@emfurd.bsky.social), Associate Editor
From rivers and oxbow lakes to crop-field patchworks and mineral sediments, Landsat has seen it all. A program of NASA and USGS, the satellite initiative has documented the Earth’s surface since 1972, making it the longest continuous record of our planet’s ever-evolving landscapes. And to mark Earth Day 2026, the organizations launched a playful way to interact with some of their findings collected over the past five-and-a-half decades—a name generator.
Using the tool is simple: type in your name, or any word, and Landsat returns it in the form of vertical snapshots of a wide range of terrain. Just like we see with composites of Mars, for instance, scientists have digitally enhanced some images to highlight specific features. Those used for “Your Name in Landsat” sport a wide array of hues, textures, and patterns that glimpse the diversity of our planet’s surface.
Landsat is an incredible resource that features time-lapses of changing land use over several decades. Even this playful name generator allows you to hover over individual images and learn the exact locations—down to the coordinates—and all of the program’s data is publicly accessible. For example, the “C” in “Colossal” above is a vertical view of a cloud-speckled Deception Island in Antarctica, and the “A” is the uniquely shaped Lake Mjøsa in Norway.
You might also enjoy Overview, a book that chronicles how the landscape has changed over time. Learn more about Landsat from NASA. (via PetaPixel)
Do stories and artists like this matter to you? Become a Colossal Member today and support independent arts publishing for as little as $7 per month. The article Spell Your Name with NASA’s Earthly Alphabet of Aerial Images appeared first on Colossal.
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The 65th National Bivalent Oral Polio Vaccination Campaign began on Monday in Havana and will extend throughout the country, representing one of the achievements of Cuban public health, as it guarantees that all children will be free of this disease.
© SSPL via Getty Images
The supply of magma from the Earth’s mantle is a primary source of heat to volcanic arc crust, where the heat is then dissipated through various processes. Much of this magmatic heat is dissipated as heated water, or aqueous fluid.
A new article in Reviews of Geophysics compares 11 different volcanic-arc segments where heat discharge via aqueous fluid has been well-inventoried to better understand the factors that influence this process. Here, we asked the authors to give an overview of heat discharge from volcanic arcs, how scientists measure it, and what questions remain.
Why is it important to study how heat is dissipated from volcanic arcs?
The heat from these magmas matters for geothermal energy, patterns of groundwater flow, and the patterns of volcanic activity at the surface.
Volcanic arcs are the chains of volcanoes on top of subduction zones. They can produce some of Earth’s most explosive and hazardous eruptions. But much of the magma beneath the surface never erupts. Nevertheless, the heat from these magmas—and the simple fact of their existence and abundance—still matters for geothermal energy, patterns of groundwater flow, and the patterns of volcanic activity at the surface.
What are the main modes in which heat is discharged from volcanic arcs?
Heat at volcanic arcs can be carried by magmas, transmitted through the crust conductively, and carried by water seeping slowly through the crust. At the base of the crust, magmas are probably most important, with conduction coming in second. But as magmas move upwards through the crust, some of them solidify and impart their heat to their surroundings where it is transferred by conduction. Within a few kilometers of the surface, fluids seeping through the crust begin to take up all that heat, and so if we can quantify the heat carried by those fluids, we can retrace it to its origins in magmas.
How do scientists measure these different forms of heat loss?
Scientists measure the heat carried by erupting magmas using satellites, or by adding up the erupted masses and making an estimate of how much energy was released by cooling from eruption temperatures to solid igneous rocks at Earth’s surface. Conductive heat flow is measured by drilling holes in the Earth’s crust to see how quickly it gets hotter with depth.
Measuring the heat carried by aqueous fluids in the crust is in some ways the trickiest. One approach is to find all the springs where hot or even slightly warm water is trickling out and measure the temperature and discharge to estimate how much extra heat that water is carrying.
What are the challenges and uncertainties in measuring hydrothermal heat discharge?
One challenge is that many springs are only slightly warmer than you’d expect. There is good data for many hot springs, but there are data tracking these ‘slightly warm’ springs for only a subset of arcs. Another challenge is that warm underground fluids can flow laterally, so you have to try to account for that. This is not an uncertainty in hydrothermal discharge, but one additional big uncertainty for our study, where we were trying to quantify the proportion of magmas that freeze underground versus erupting, is in the estimates of how much magma has actually erupted through time.
What are some of the factors that influence hydrothermal heat loss?
A major goal of our paper is to try to quantify these hidden magmas.
A main factor that influences hydrothermal heat loss is the magmas that solidify underground. This link is the key motivation for our study. A major goal of our paper is to try to quantify these hidden magmas—how much magma needs to intrude the crust beneath the surface to supply the hydrothermal heat fluxes that we observe? The composition of magmas influences how much heat they can release. The depth at which magmas are emplaced also matters, because magmas that intrude the shallow crust eventually cool to lower temperatures than magmas emplaced in the lower crust and therefore release more heat.
What are the remaining questions or knowledge gaps where additional research efforts are needed?
A big outstanding challenge is combining estimates from hydrothermal data of how much magma is coming into the crust – like ours – with other approaches to estimating the same thing. The magmatic systems beneath volcanoes span the crust. At the base of the crust, you have magma supply, sort of like the water main feeding your plumbing system. Despite how fundamental magma supply is, we know remarkably little about it. It’s exciting to think about how the rates of magma supply could vary through time and space and why. Applying a range of techniques—including geophysical imaging, hydrothermal budgets, gas and igneous geochemistry, and petrology—to understand these questions could really be a game changer.
—Benjamin A. Black (bblack@eps.rutgers.edu; 0000-0003-4585-6438), Rutgers University, United States; S. E. Ingebritsen (steve.ingebritsen@gmail.com; 0000-0001-6917-9369), Kyoto University, Japan; and Kazuki Sawayama (sawayama@bep.vgs.kyoto-u.ac.jp; 0000-0001-7988-3739), Kyoto University, Japan
Editor’s Note: It is the policy of AGU Publications to invite the authors of articles published in Reviews of Geophysics to write a summary for Eos Editors’ Vox.
Evident Scientific, a scientific solutions and microscopic imaging company, has announced the winners of its sixth annual Image of the Year photo contest. The competition celebrates the world's best scientific microscopic imaging, and the photos are as scientifically valuable as they are beautiful.
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.
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.
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.
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.
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.
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.
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.
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.
Allowing water security to flow through the diverse components of the Genesis Mission would benefit both the policies championed by the mission itself and the hydrology community.
The Genesis Mission gets real-world, noisy test beds where AI proves value beyond benchmarks, a domain to stress test climate and infrastructure investments, and scientists trained in both AI and the stubborn realities of rivers, aquifers, and pipes.
Hydrology gets resources for shared data infrastructure, foundation models and instrumented basins no single lab can support, a seat when rules for AI and national scientific infrastructure are negotiated, and a chance to reset practices around openness, collaboration, and equity.
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.
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.
Carroll, S. R., et al. (2020), The CARE principles for Indigenous data governance, Data Sci. J., 19, 43, https://doi.org/10.5334/dsj-2020-043.
European Commission (2023), Destination Earth: Digital Twins and the Digital Twin Engine, Publ. Off. of the Eur. Union, Luxembourg, destination-earth.eu/destination-earth/destines-components/digital-twins-digital-twin-engine/.
Google Research (2024), Flood forecasting and Flood Hub, Google Research Technical Overview, sites.research.google/gr/floodforecasting/.
International Telecommunication Union (2024), AI for Good: Water and sanitation, aiforgood.itu.int/aifg-course/harnessing-ai-for-sustainable-innovation-sdg6-advancing-clean-water-and-sanitation/.
Kratzert, F., et al. (2019), Toward improved predictions in ungauged basins: Exploiting the power of machine learning, Water Resour. Res., 55, 11,344–11,354, https://doi.org/10.1029/2019WR026065.
Kratzert, F., et al. (2023), Caravan: A global community dataset for large-sample hydrology, Sci. Data, 10, 61, https://doi.org/10.1038/s41597-023-01975-w.
Li, P., et al. (2023), Making AI less “thirsty”: Uncovering and addressing the secret water footprint of AI models, Commun. ACM, 66, 28–31, cacm.acm.org/sustainability-and-computing/making-ai-less-thirsty/.
The White House (2025a), Accelerating Federal Permitting of Data Center Infrastructure, Executive Order 14318, Washington, D.C., www.whitehouse.gov/presidential-actions/2025/07/accelerating-federal-permitting-of-data-center-infrastructure.
The White House (2025b), Launching the Genesis Mission, Executive Order 14363, Washington, D.C., www.whitehouse.gov/presidential-actions/2025/11/launching-the-genesis-mission.
Xiao, T., et al. (2025), Environmental impact and net-zero pathways for sustainable artificial intelligence servers in the USA, Nat. Sustainability, 8, 1,541–1,553, https://doi.org/10.1038/s41893-025-01681-y.
Zhang, L., et al. (2025), Foundation models as assistive tools in hydrometeorology: Opportunities, challenges, and perspectives, Water Resour. Res., 61, e2024WR039553, https://doi.org/10.1029/2024WR039553.
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
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