EOS

Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: Journal of Geophysical Research: Solid Earth

Earthquakes release energy and result in source properties defined across a wide range of scales that are not represented in conventional frictional laws. Norisugi and Noda [2026] introduce a new rate- and roughness-dependent friction (RRF) law which incorporates both effects from fault slip rate and multi-scale variation in fault topography. By limiting the number of state variables in the RRF formulation, the authors show with efficient earthquake cycle simulation that this multi-scale approach can reproduce a key observed relationship between fracture energy and fault slip.

Although further refinement is needed to better represent roughness evolution, this study marks a major advance in earthquake modeling by demonstrating the necessity and feasibility of incorporating multi-scale fault topography in the characterization of earthquake source process.  

Citation: Norisugi, R., & Noda, H. (2026). Multi-scale rate- and roughness-dependent frictional constitutive law and dynamic earthquake sequence simulation. Journal of Geophysical Research: Solid Earth, 131, e2025JB033580. https://doi.org/10.1029/2025JB033580

—Yajing Liu, Associate Editor, JGR: Solid Earth

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.

Solar activity affecting Earth and its planetary neighbors encompasses a wide range of phenomena, from the steady solar wind and the interplanetary magnetic field to extreme events like solar flares, coronal mass ejections (CMEs), and solar energetic particle (SEP) events. These space weather phenomena interact in complex ways with planetary magnetospheres and atmospheres. On Earth, we see the results in the dancing lights of stunning auroras and in less frequent but sometimes severe disruptions to telecommunications, navigation, and energy infrastructure.

Forecasting conditions throughout the heliosphere (the region influenced by the solar wind), understanding the variety of Sun-Earth interactions, and predicting arrivals of space weather events—both benign and potentially hazardous—are a grand challenge.

The Sun-Earth challenge requires tracking and predicting conditions—from routine and quiet to rare and extreme—across tens of millions of kilometers of interplanetary space.

Solar flares emit electromagnetic radiation that spreads in all directions. In contrast, the propagation of CMEs and SEP events depends on their source location on the Sun and on the heliospheric magnetic field, which is carried outward by the solar wind. The impacts these events have on magnetosphere systems further vary depending on particle energies and intensities in SEPs and on particle speeds and the magnetic field orientation in CMEs. The Sun-Earth challenge thus requires tracking and predicting conditions—from routine and quiet to rare and extreme—across tens of millions of kilometers of interplanetary space.

This tracking and prediction is powered by petabyte-scale datasets from solar observatories and spacecraft measurements that provide rich observational archives. Researchers use these data to deduce physically meaningful quantities describing the heliosphere and to identify patterns to distinguish quiet from active conditions. The resulting insights not only answer fundamental science questions but also provide critical prediction time frames needed by space weather forecasters.

Even with all these data, the enormity of space between the Sun and Earth presents a major obstacle to our predictive capabilities. Another obstacle is that the data are obtained by different instruments operating at different locations and times. These factors combine to create a unique data sparsity challenge that complicates large-scale analysis.

These fundamental issues—the massive yet still insufficient supply of data available, the extreme differences in the scales of the processes we must illuminate, and the need for actionable predictions—suggest opportunities for artificial intelligence (AI) and machine learning (ML) to complement traditional physics-based analytical approaches [Camporeale, 2019]. In a series of workshops—insights from which inform the discussion below—scientists explored such opportunities and how they can advance heliophysics research and operational space weather forecasting.

The Need for Space Weather Forecasting

Space weather events can have significant impacts on infrastructure and humans. They can disrupt satellite operations (e.g., by enhancing atmospheric drag on satellites), damage electronics in space, interfere with radio communications and GPS, and even affect power grids (e.g., through geomagnetically induced currents) during the most severe events. They can also pose risks to people, especially astronauts beyond the protection of Earth’s atmosphere and airline crews and passengers on long-distance polar flights, during which exposure to energetic particles is elevated. Forecasting offers a first line of defense in preparing for or preventing damaging and hazardous effects of space weather.

In assessing major CMEs, forecasters consider whether and when events will reach Earth and whether they will trigger geomagnetic storms and substorms. For SEP events, predictions must include arrival times, peak intensities, durations, and energy characteristics.

Predicting extreme space weather phenomena is vital, but equally important is forecasting periods when no significant activity is expected, which is critical information for satellite operators and other stakeholders. Making such predictions requires understanding physics spanning 8 orders of magnitude in space and time, from subsecond processes in Earth’s magnetic environment to multiday solar eruptions propagating across the 150 million kilometers between the Sun and Earth (Figure 1) and long-term interactions at scales associated with the 11-year solar cycle.

Fig 1. Length scales and Sun-to-Earth transit times vary greatly for different types of space weather (SW), including solar flares, solar energetic particle (SEP) events, coronal mass ejections (CMEs), and interplanetary coronal mass ejections (ICMEs). High-speed particles are the first to arrive, usually within minutes of a flare, whereas CMEs arrive in 2–4 days. Credit: Georgoulis et al. [2026], CC BY-NC-ND 4.0

In addition to operational forecasting, these challenges are fundamental in heliophysics research. Such research includes work to reveal how the Sun generates its magnetic field, how solar wind accelerates and evolves, how planetary magnetospheres respond to external forcing, how particles are accelerated, and how energy transfers across multiple scales and regimes.

Unique Challenges in Heliophysics

Modern AI and ML algorithms excel at analyzing well-curated, extensive datasets that include millions of training examples. For example, AI-aided terrestrial weather forecasting relying on continuous, high-resolution coverage from thousands of ground stations, weather balloons, and satellites has advanced dramatically in recent years.

Fewer than a dozen spacecraft monitor Earth’s magnetosphere, a region spanning tens of Earth radii. Solar wind observations are even sparser.

Heliophysics, however, presents a unique and somewhat opposite scenario. Fewer than a dozen spacecraft monitor Earth’s magnetosphere, a region spanning tens of Earth radii (about 6,371 kilometers). Solar wind observations are even sparser, with just a handful of monitors scattered across the space between the Sun and Earth. This fundamental scarcity poses a challenge for data-driven approaches, which typically depend on abundant observations that are well distributed in space and time to produce trustworthy (i.e., generalizable and reproducible) models.

Data sparsity is further compounded by the relative rarity of intense space weather phenomena such as CMEs, major geomagnetic storms, and extreme substorms, which occur only a few times per solar cycle. Most heliophysical observations capture quiet, low-activity conditions when the solar wind is steady and magnetospheres are calm. Standard ML approaches trained on such imbalanced datasets may achieve high statistical accuracy by simply predicting a “nothing-will-happen” outcome but completely fail when extreme events occur.

Although solar eruptions and geomagnetic storms are relatively rare, they exhibit recurring patterns and consistency in their physical drivers. This regularity suggests that historical observations, when properly clustered and analyzed, can be used to enhance prediction capabilities. The challenge therefore lies in extracting meaningful patterns from sparse measurements of rare events while avoiding models that work well for average conditions but fail when they matter most [Chu et al., 2025].

AI Solutions for Data Sparsity

Heliophysics research employs clever approaches to extract maximum information from the limited available observations. One strategy is to mine multidecade observational records from various satellites and to match and group together measurements collected at times with similar solar wind and geomagnetic activity conditions.

This process clusters tens of thousands of data points from similar magnetospheric states. Such clustering enables reconstruction of dynamic features like nightside magnetic field changes during substorms [Stephens et al., 2019] and the presence of near-Earth magnetotail reconnections [Angelopoulos et al., 2020].

Another, more universal approach is to embed fundamental physical laws directly into ML models through physics-informed neural networks [Raissi et al., 2019], ensuring that predictions respect physical reality even when training data are limited. Data assimilation techniques used in weather forecasting similarly blend sparse observations with physics-based simulations and update models as new measurements arrive.

This animated model shows Earth’s magnetosphere during a powerful May 2024 geomagnetic storm that involved strong solar flares and multiple CMEs. The visualization uses the Multiscale Atmosphere-Geospace Environment (MAGE) model from the Johns Hopkins Applied Physics Laboratory to depict wind rushing toward Earth and disturbing its magnetic field (orange and purple lines). The green cloud represents electric field current intensity; the blue squiggles are tracers of solar wind velocities. Credit: NASA Scientific Visualization Studio and NASA DRIVE Science Center for Geospace Storms

These methods converge on a common theme: building gray box models (so named because they’re less opaque than black box models) that are data driven but grounded in physically real constraints. For data-starved applications, hybrid approaches can outperform purely data-driven or purely physics-based methods [Liu et al., 2025].

Satellite instruments are generating increasingly large solar wind datasets. However, the variables obtained (e.g., solar wind speed and pressure) are highly intercorrelated [Borovsky, 2018], making it difficult to identify which ones truly drive magnetospheric responses. New algorithms are helping to distill datasets without losing critical scientific information [e.g., Camporeale, 2025]. Meanwhile, advanced statistical and ML methods can cut through dataset complexity by reducing dimensionality, identifying causal relationships among variables, and providing clues about dominant drivers.

For instance, information theory provides tools to detect dependencies in complex systems, establish causality, and rank variables that most effectively predict space weather outcomes [Wing et al., 2022]. Such techniques can be paired with other “explainable” tools, such as SHAP (SHapley Additive exPlanations) values, a method inspired by game theory, to pinpoint physical variables (e.g., solar wind speed or magnetic orientation) that drive a prediction [Ma et al., 2023].

Distilling datasets and improving model interpretability help make ML more practical and more scientifically trustworthy and its predictions more robust. But fully trusting ML models in operational environments requires rigorous validation and uncertainty quantification. These models must not only make predictions but also indicate their confidence levels for operational decisionmaking.

When a model forecasts a major geomagnetic storm, operators need to know whether that prediction carries 60% or 95% confidence, for example.

When a model forecasts a major geomagnetic storm, operators need to know whether that prediction carries 60% or 95% confidence, for example. Ensemble approaches, in which multiple models provide a range of outcomes, help quantify this uncertainty, while using standardized, well-documented datasets enables fair model intercomparisons.

The research community is developing ML-ready benchmark datasets with consistent formatting and clear metadata to establish such validation procedures [e.g., Angryk et al., 2020]. These resources allow researchers to test new algorithms against common baselines, accelerating progress while ensuring that advances are robust and reproducible rather than artifacts of specific data processing choices.

Notably, one domain in heliophysics that is not affected by severe data sparsity is solar imaging. Decades of continuous, high-resolution observations from the Solar Dynamics Observatory (SDO), which delivers 1.5 terabytes of data every day, have created enormous data archives. Because the Sun drives space weather throughout the heliosphere, these datasets offer an ideal opportunity for use in foundation models, large-scale ML systems trained to learn comprehensive internal representations that can then be easily adapted to specific scientific tasks with minimal additional training.

Surya, a foundation model designed to construct a digital representation of the Sun, represents one such effort. It is still in early development and has yet to be validated, but this approach illustrates how data-rich domains can be leveraged with modern AI techniques to create tools that broadly benefit heliophysics research and space weather forecasting.

Advancing Research and Operational Forecasting Together

In addition to the needs for data and model development and validation, applying AI to address the challenges of heliophysics requires sustained, multidisciplinary collaborations. Fostering those collaborations has been the focus of a series of workshops, with the most recent being 2025’s Machine Learning, Data Mining and Data Assimilation in Geospace (LMAG25) meeting at the Johns Hopkins University Applied Physics Laboratory. The workshops have brought together heliophysicists, machine learning experts, data scientists, and specialists from weather forecasting and applied mathematics to exchange knowledge and establish community standards.

Space weather forecasters need models that are accurate and interpretable and that provide not just statistical metrics but also actionable predictions.

The LMAG forums also serve as gathering spaces for scientists to validate models against diverse datasets, compare physics-based and data-driven approaches, develop performance benchmarks, and discuss how to bridge research and operational requirements. Space weather forecasters need models that are accurate and interpretable and that provide not just statistical metrics but also actionable predictions with known limitations and reliability. Of course, researchers also benefit. These conversations allow them to gain insight into operational constraints that shape how modeling approaches become practical in real-world settings.

LMAG and similar initiatives facilitate direct exchanges among adjacent communities, including by making meeting presentations openly available. These efforts are helping translate cutting-edge AI and ML techniques into practical tools that help protect critical infrastructure and human well-being. They are also deepening our understanding of how the Sun shapes space weather throughout the solar system and its effects—both mundane and major—on Earth.

References

Angelopoulos, V., et al. (2020), Near-Earth magnetotail reconnection powers space storms, Nat. Phys., 16(3), 317–321, https://doi.org/10.1038/s41567-019-0749-4.

Angryk, R. A., et al. (2020), Multivariate time series dataset for space weather data analytics, Sci. Data, 7(1), 227, https://doi.org/10.1038/s41597-020-0548-x.

Borovsky, J. E. (2018), The spatial structure of the oncoming solar wind at Earth and the shortcomings of a solar-wind monitor at L1, J. Atmos. Sol. Terr. Phys., 177, 2–11, https://doi.org/10.1016/j.jastp.2017.03.014.

Camporeale, E. (2019), The challenge of machine learning in space weather: Nowcasting and forecasting, Space Weather, 17(8), 1,166–1,207, https://doi.org/10.1029/2018SW002061.

Camporeale, E. (2025), PARIS: Pruning Algorithm via the Representer theorem for Imbalanced Scenarios, arXiv:2512.06950, https://doi.org/10.48550/arXiv.2512.06950.

Chu, X., et al. (2025), Imbalanced Regression Artificial Neural Network Model for Auroral Electrojet Indices (IRANNA): Can we predict strong events?, Space Weather, 23(5), e2024SW004236, https://doi.org/10.1029/2024SW004236.

Georgoulis, M. K., et al. (2026), Prediction of solar energetic events impacting space weather conditions, Adv. Space Res., in press, https://doi.org/10.1016/j.asr.2024.02.030.

Liu, Y., et al. (2025), Data-driven modeling of electrostatic turbulence by physics-informed Fourier neural operator, Mach. Learn. Sci. Technol., 6(4), 045050, https://doi.org/10.1088/2632-2153/ae19cd.

Ma, D., et al. (2023), Opening the black box of the radiation belt machine learning model, Space Weather, 21(4), e2022SW003339, https://doi.org/10.1029/2022SW003339.

Raissi, M., P. Perdikaris, and G. E. Karniadakis (2019), Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, J. Comput. Phys., 378, 686–707, https://doi.org/10.1016/j.jcp.2018.10.045.

Stephens, G. K., et al. (2019), Global empirical picture of magnetospheric substorms inferred from multimission magnetometer data, J. Geophys. Res. Space Phys., 124(2), 1,085–1,110, https://doi.org/10.1029/2018JA025843.

Wing, S., et al. (2022), Modeling radiation belt electrons with information theory informed neural networks, Space Weather, 20(8), e2022SW003090, https://doi.org/10.1029/2022SW003090.

Author Information

Savvas Raptis (savvas.raptis@jhuapl.edu), Manolis K. Georgoulis, Mikhail Sitnov, Anthony Sciola, and Simon Wing, Johns Hopkins University Applied Physics Laboratory, Laurel, Md.

Citation: Raptis, S., M. K. Georgoulis, M. Sitnov, A. Sciola, and S. Wing (2026), Vast space, sparse data: An AI answer to twin space weather challenges, Eos, 107, https://doi.org/10.1029/2026EO260188. Published on 11 June 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.

In November 2025, Cyclone Senyar generated extreme rainfall in parts of Sumatra, Indonesia, triggering thousands of landslides. Our new paper in the journal Current Biology demonstrates that these landslides might have a devastating impact on a critically endangered population of Tapanuli orangutan.

In November 2025, Cyclone Senyar brought extreme rainfall to large parts of Sumatra in Indonesia. I have written about this on previous occasions – the rainfall triggered vast numbers of landslides.

In my line of work, we often focus on the landslide impacts on the landscape, on human lives and on infrastructure. We rarely consider the impacts on th eanimal population. This is certainly a weakness that the Cyclone Senyar event brings to focus.

Part of the area devastated by the landslides is that slopes around the Batang Toru rover, an area of forest that is home to a rare species of orangutang. These great apes, Pongo tapanuliensis, live in a habitat known as the West Block of Tapanuli. There are only 800 individuals left in the wild, a situation that is highly precarious. The loss of even a small number of adults could tip the species towards extinction.

I was a part of a consortium of scientists that considered the landslide impacts of Cyclone Senyar on the habitat of these orangutangs. The results have just been published in the journal Current Biology (Meijaard et al. 2026) – the paper is open access and published under a creative commons license.

This image, from the paper, shows the landslide impacts of Cyclone Senyar:-

Before and after satellite imagery of the impacts of Cyclone Senyar. From: Meijaard et al. (2026).

In the study area of 71,161 hectares, the mapping indicates that there were 50, 185 individual landslides, covering a surface area of 8,303 hectares. This is about 11% of the forested area. We then estimate the likely loss of the orangutang population, which is likely to be in the range of 18-120 individuals, with a central estimate of 58 individuals. This is likely to have been a devastating loss for this highly endangered population.

This level of habitat loss might also be placing a severe pressure on the remaining population, so further fatalities are very possible through, for example, reduced food availability.

The intensity of the rainfall was almost certainly supercharged by climate change. The impacts of Cyclone Senyar are being replicated widely – and of course we are now in the northern hemisphere tropical cyclone season again.

Our paper makes some policy recommendations for this population of orangutans. First, the government of Indonesia needs to permanently protect this area of forest against mining , palm oil and hydropower developments. Ideally, the protected area should be expanded. Second, Indonesia needs support for biodiversity-recovery, hazard forecasting and ecological restoration planning.

Reference

Meijaard, E. … Petley. D. … et al. 2026, Extreme rainfall further endangers the world’s rarest great ape. Current Biology. https://doi.org/10.1016/j.cub.2026.05.029

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Source: Earth’s Future

Hotter, drier conditions in the western United States have led to a rise in wildfire activity that has damaged or destroyed infrastructure, natural ecosystems, and entire towns across the region. As fires grow larger and more destructive, the cost of managing them rises as well.

Fire management agencies in the United States have been feeling the pressure. Between 2014 and 2023, fire management agencies across all levels of government experienced a 131% increase in total area burned and a 268% increase in total fire spending adjusted for inflation compared to the period between 1985 and 1994.

Today, federal agencies like the Department of the Interior (DOI) and the U.S. Department of Agriculture Forest Service (USFS) continue to invest in aiding states and managing hazardous fuel growth on public land, as well as suppressing active fires. Policymakers and federal agencies alike must decide how to manage limited budgets while protecting people, property, and natural resources.

Prestemon et al. built statistical models based on historical data to examine the potential increase in spending by the DOI and the USFS between now and 2100. Their models link wildfire activity to climate variables such as temperature and water vapor deficit and then connect fire activity to suppression costs. To capture a range of possible future conditions on federal lands, the study predicts 10 fire and suppression spending scenarios by applying five different climate models to two different warming pathways (the moderate Representative Concentration Pathways (RCP) 4.5 scenario and the high-emissions RCP 8.5 scenario).

The results varied by region and scenario, but each of the 10 scenarios suggested a rise in area burned as well as inflation-adjusted fire suppression spending, with higher fire activity translating to higher costs. Projected changes in DOI and USFS land burned increased 80% by mid-century and 208% by late century.

By the middle of the century, both agencies are projected to see spending increases: about 0.65% per year for DOI spending and about 0.87% per year for USFS spending from 2020 to 2100. Although uncertainty increased with time and outcomes varied across climate models and warming pathways, the largest increases in both cost and wildfire activity were consistently projected for the northwestern United States. (Earth’s Future, https://doi.org/10.1029/2025EF007985, 2026).

—Rebecca Owen (@beccapox.bsky.social), Science Writer

Citation: Owen, R. (2026), As wildfires increase in the West, so does suppression spending, Eos, 107, https://doi.org/10.1029/2026EO260187. Published on 10 June 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.

“Future winters promise less snow, more rain. Nobody’s prepared.” So proclaims the title of a recent article in the Proceedings of the National Academy of Sciences of the United States of America that frames adaptation to snow loss as the “million-dollar question” facing the western United States.

As the largest sectoral consumer of fresh water globally, agriculture is particularly vulnerable to snow loss.

Declining snowfall—and snowmelt—affects ecosystems, urban and rural water supplies, hydropower, recreation, tourism, and agriculture. As the largest sectoral consumer of fresh water globally, agriculture is particularly vulnerable to snow loss.

Much of the U.S. West faces one of the worst snow years on record, and the statistics on future conditions feel dire. Up to 40% of the water demand for agriculture in the region is likely to go unmet as it gets warmer and less snowy. Similar scenarios are shaping up elsewhere around the world, including southern Europe, high-mountain and Central Asia, western Russia, and the southern Andes [Qin et al., 2020].

In response, water managers have developed a range of approaches for adapting to snow loss: infrastructure-based approaches like managed aquifer recharge, nature-based solutions such as forest management and beaver dam analogues, demand-side approaches like multibenefit land repurposing, and polarizing supply-side approaches like reservoir expansion and cloud seeding (Figure 1).

Fig. 1. Potential approaches to reduce negative impacts to agriculture from snow loss include a variety of adaptive strategies that address either water supply or demand. Click image for larger version.

However, efforts to identify which of these strategies to implement for different drainage basins, or watersheds, using the variety of available approaches seem to fall into one of two traps: either searching for unrealistic one-size-fits-all panaceas [Ostrom, 2007] or treating every basin as unique, which is costly and inefficient.

The “trillion-dollar question” isn’t how to adapt but, rather, where existing strategies may make the most—and fastest—difference.

Importantly, continuing along this trajectory means that we’re on track to offset only about a third of global climate-induced crop yield losses by 2100. For the western United States, previous work has estimated cumulative economic losses from declining snowfall of hundreds of billions to trillions of dollars while noting that rational adaptation decisions are hampered by the lack of financial analyses of the importance of snow [Sturm et al., 2017].

We thus suggest that the “trillion-dollar question” isn’t how to adapt but, rather, where existing strategies may make the most—and fastest—difference to offset projected losses. Answering this question requires an approach that matches strategies to the contexts where they are more likely to succeed—one that treats basins as neither uniform nor unique.

A Mismatch in Research and Operational Scales

Physical scientists tend to look at snow loss as a basin-scale problem, in part because this view aligns with hydrologic boundaries. However, as our colleague, applied economist Joey Blumberg, explains, “county lines were not drawn to follow watersheds, and rivers do not conform to political borders, creating a patchwork of mismatched boundaries.”

Scientists have long emphasized that mitigating climate change requires us to “think globally, assess regionally, act locally.” And in 1992, the authors of the Dublin Principles reasoned that moving the needle on “wicked water problems” requires targeting decisionmaking at the “lowest appropriate level,” where stakeholders can collaborate most effectively.

Lake Tahoe, pictured here, contains 37 trillion gallons of water, roughly half of which is supplied by snowmelt in the Sierra Nevada Mountains. Credit: Beatrice L. Gordon

The challenge is that “local” isn’t a single, consistent unit. We recently explored the lowest appropriate level concept for agricultural water management in the U.S. West by defining local operational contexts on the basis of intermediaries such as irrigation districts, water conservancies, and mutual water companies that connect individual farmers to their hydrology [Gordon et al., 2024].

Working at this scale, we found one-size-fits-all strategies often don’t hold up, even within the same hydrologic basin [Gordon et al., 2024; Boisramé et al., 2026]. In the Upper Colorado River Basin, for example, expanding reservoir storage could buffer agriculture in northeastern Utah against declining snowpack, but the same strategy may fail miles away in southwestern Wyoming, where a thirstier atmosphere may make it harder to refill existing reservoirs.

However, collecting detailed local-scale information for just 13 of the roughly 2,600 operational contexts nationwide took almost 3 years of searching websites, reading working papers, and calling water managers.

Scaling this approach across the entire western United States is understandably overwhelming. We need a more systematic approach to help managers identify which strategies could work most effectively, and where.

A Diagnostic for Agriculture and Snow Loss

Ostrom [2007] argued that complex systems, such as Western agriculture, “are partially decomposable in their structure.” This insight is woven into archetype analysis, an approach for identifying recurrent patterns across otherwise diverse systems.

Like workplace assessments—which are genuinely useful, albeit imperfect, tools for understanding successful management styles—archetypes draw on qualitative, quantitative, or hybrid approaches to group diverse operational contexts on the basis of shared characteristics [Sietz et al., 2019]. These groupings enable systematic knowledge transfer about, for example, how management strategies that work in one context can also guide adaptation elsewhere.

Three main characteristics interact to define operational contexts in snow-dependent agriculture in the western United States: physical constraints, governance systems, and human behavior.

“Researchers can empirically derive building blocks or components that comprise archetypes to represent key features of a system,” explains Elizabeth Koebele, who studies urban water sustainability [Garcia et al., 2019] and has begun applying archetypes in that context. However, she notes, these building blocks “vary based on the system context, available data, and study goal.”

We propose three main characteristics that interact to define operational contexts in snow-dependent agriculture in the western United States: physical constraints, governance systems, and human behavior. Physical constraints, including biophysical setting, infrastructure, and climate, determine available water supplies. Governance capacity relative to governance complexity shapes how those supplies are allocated across competing uses. Human behaviors influence both water demand and how users respond to supply conditions and governance rules.

Using these characteristics to establish archetypes of water management contexts could define a path forward for operationalizing an approach to accelerate successful adaptations to declining snowpacks in the West.

Constraining How Snowmelt Becomes Water Supply

Physical constraints stem from biophysical processes that influence how, when, and how much snow becomes streamflow; infrastructure that stores and conveys water; and hydrologic and climatic uncertainties about future supplies. These constraints can vary substantially from basin to basin.

Consider the Walker River Basin and California’s San Joaquin Valley, both of which rely on Sierra Nevada snowpack but have different biophysical settings. In some parts of the central Sierra, forest management can reduce wildfire risk and increase streamflow by up to 14% during low-snow years. Elsewhere, however, water made available by forest management may be consumed by remaining vegetation, limiting downstream gains.

These biophysical differences interact with uses of built infrastructure, including irrigation systems, reservoir outlets, and canals, to determine how and when water is stored and released. As temperatures warm and snowmelt declines, officials in both the Walker River and San Joaquin Valley basins must increasingly manage for a wider range of extremes, including “cold-water droughts.” However, the infrastructure to manage these trade-offs through reservoir storage and operations that balance agricultural deliveries with aquatic habitat needs is more developed in the highly managed San Joaquin than in the Walker.

Thankfully, measuring physical constraints on snowmelt at basin scales is becoming more feasible today with newly developed tools.

Layered on top of biophysical and infrastructural constraints are climatic and hydrological uncertainties, such as whether snow loss will lead to more evapotranspiration and less streamflow. These uncertainties complicate management decisions based on cost-benefit modeling of individual strategies: Should districts expand reservoir storage if precipitation is predicted to increase or decrease depending on the model? Frameworks like Decision Making Under Deep Uncertainty emphasize the need to select strategies that are robust across many possible futures.

Thankfully, measuring physical constraints on snowmelt at basin scales—a means, along with improved modeling, to reduce hydroclimatic uncertainties—is becoming more feasible today with newly developed tools. Water managers can turn, for example, to databases like the U.S. Geological Survey’s ResOpsUS [Steyaert et al., 2022], which catalogs historical reservoir operations across the contiguous United States, and to publicly available hydrologic projections such as those from Oak Ridge National Laboratory’s Coupled Models Intercomparison Project phase 6 (CMIP6) ensemble.

Governance Controls Supply Allocations

We frame governance around capacity and complexity. Capacity in this context is the ability of stakeholders “to mobilize resources in order to make equitable and fair decisions around shared challenges,” according to governance scholar Gina Gilson. Complexity refers to the number and intricacy of jurisdictions, authorities, regulations, and stakeholders involved. As governance complexity increases, the effectiveness of adaptation strategies becomes more sensitive to capacity constraints, particularly regarding timescales and funding.

For example, infrastructure in the Walker is controlled locally by a single water district, and jurisdictional coordination involves two states and the Walker River Paiute Tribe. Coordination on water management is never simple, but fewer jurisdictions generally means faster decisionmaking and clearer authority, allowing the single water district to implement strategies like multibenefit land repurposing more readily. Such implementations, in turn, enable reduced agricultural water use, directly supporting restoration of Walker Lake and recovery of endangered species.

Walker Lake in Nevada is part of the Walker River Basin. Credit: Alan Levine/Flickr, CC BY 2.0

The San Joaquin Valley is vastly different in scale and complexity, covering eight California counties, one of which alone has 22 water districts and seven cities. Following the passage of the state’s Sustainable Groundwater Management Act, water users in the basin formed more than 120 groundwater sustainability agencies. Agricultural water management thus involves overlapping federal and state systems that operate under different rules, contracts, and regulatory requirements. While land repurposing programs can be implemented, more substantial capacity, time, and resources are typically needed to do so.

Emerging efforts like the Western States Water Data Access and Analysis Tool (WestDAAT) and the Harmonized Database of Western U.S. Water Rights make it easier to assess governance in a basin by standardizing data about rules, regulations, and water rights across states. Combined with mapping of irrigation service areas and water transfers [Siddik et al., 2023], these resources help stakeholders identify the jurisdictions involved, how authority is distributed, and what coordination mechanisms exist for agricultural water management.

Human Behavior Shapes Demand Responses

Once snowmelt reaches water users, behavioral dynamics—how people respond to crises, policies, and changing conditions—determine how effectively management strategies achieve desired results.

Water demand is influenced by consumption choices and by economic, political, and cultural factors.

Water demand is influenced by consumption choices and by economic, political, and cultural factors. It is also influenced by factors that typical hydrologic models rarely account for, including social structure, social memory, and affluence. More affluent users are less likely to modify their behavior to reduce water use under conditions of scarcity.

The dynamics of water demand in the South Platte River Basin, for example, are especially complex, as they are balanced across cities, agriculture, and ecosystems across parts of Colorado, Nebraska, and Wyoming. Water prices in the basin’s Big Thompson project, a federal water diversion system in northern Colorado, jumped from $1,500 per acre-foot in 1990 to more than $30,000 in 2018, driven by economic factors that resulted in cities owning 70% of water originally intended for agriculture.

Even with reliable projections of future climate and water supply, carefully planned strategies can be overwhelmed by economic and behavioral factors, resulting in transfers and reallocations of water. What’s more, behavioral responses to adaptation strategies can paradoxically increase demand when users perceive that scarcity problems are solved.

The “reservoir effect” occurs when water security perceptions encourage expansion of water-intensive activities [Di Baldassarre et al., 2018]. Similarly, the irrigation efficiency paradox shows how efficiency gains can lead to expanded production and reduced return flows (how much irrigation water returns to streams and aquifers) downstream [Grafton et al., 2018].

Conceptual frameworks, models, and global case studies have all been used as approaches to study the effects of human behavior on hydrology. With sufficient training data, we believe tools like machine learning could be used to further explore how behaviors influence adaptation and to anticipate shifts as snow loss continues.

Archetypes in Practice

By evaluating how physical factors, governance systems, and human behavior shape outcomes across places like the Walker, South Platte, and San Joaquin basins, researchers and practitioners can establish archetypes to help identify patterns in what strategies are most effective in different places and assess how to transfer lessons from one setting to another (Figure 2).

Fig. 2. An archetype-based diagnostic grounded in evaluating the physical constraints, governance, and human behavioral dynamics affecting hydrologic basins could facilitate more rapid transfer of learning about successful adaptation approaches across snowmelt-dependent agriculture in the western United States.

The Walker River Basin exemplifies an archetype common to agriculturally dominated headwaters in the western United States with low governance complexity (few jurisdictions), adequate capacity (resources), low behavioral complexity (more predictable and unified user groups), and substantial physical constraints (significant future snow loss and limited infrastructure for water storage and supplementation).

With this profile, the Walker is an ideal testing ground for evaluating how effectively different strategies offset changes in snowmelt. Does cloud seeding increase snowpack? Could beaver dam analogues—a nature-based solution reminiscent of Idaho Fish and Game’s mid-20th century effort to parachute beavers into the wilderness—meaningfully increase water retention? Could multibenefit land repurposing buffer people and ecosystems against supply volatility while restoring ecosystem functionality?

The value of organizing operational contexts by archetypes is that each context need not be treated as unique.

The value of organizing operational contexts by archetypes is that each context need not be treated as unique. Lessons learned from the Walker could be systematically transferred to other areas with similar characteristics and could be incrementally tested in others.

The South Platte has physical constraints similar to Walker’s but features greater governance complexity because of multiple interstate compacts, as well as greater behavioral complexity. Modeling analyses indicate that demand-side strategies could adapt to more volatile water supply in the South Platte [Gharib et al., 2023]. But implementing them requires balancing perspectives from both agricultural and urban water users—a behavioral dynamic absent in Walker.

Crop switching to cultivate higher-value crops on less acreage could reduce water use. However, options for what crops can be grown where are constrained by factors like elevation and climate. Even where feasible, new crops would require investments in education, new infrastructure, risk management, and agronomic knowledge.

Through iterative expansion and testing, broad archetypes like “high behavioral complexity” could be specified to reflect dynamics like rural-urban competition or concerns around buy-and-dry economics. Archetypes may also point to contexts where governance complexity signals that decisionmaking is occurring above the lowest appropriate level.

Agricultural fields line a canal in California’s San Joaquin Valley. Credit: Don Graham/Flickr, CC BY-SA 2.0

The San Joaquin, with its extremely complex governance involving numerous local, state, and federal agencies managing surface and groundwater, is one potential example. Recognizing this pattern can help identify where substantial resources and long timelines may be required to implement programs (e.g., LandFlex) requiring legislative authorization, multiagency coordination, and stakeholder engagement. It may also signal the need to identify smaller operational contexts within larger settings so implementations proceed more rapidly.

Operationalizing Archetypes from Diagnosis to Action

Developing a systematic approach to match adaptation strategies with areas where they are most likely to succeed in operation is only a first step. Applying diagnostics without mechanisms to implement new strategies is often insufficient to drive timely action.

An instructive precedent of success in water quality management comes from the 1970s. By then, pollution controls on factories had improved compared with the early 20th century, yet water quality in surface waters across the country still declined because of pollution in agricultural runoff. The breakthrough came with the EPA’s total maximum daily load (TMDL) program, which created a structured process that set measurable goals for reducing pollution and assigned responsibility for meeting those goals to the sources of the pollution, allowing for local control over adaptation.

Archetypes could play a similar role in facilitating beneficial snow-loss adaptations, and a structure like the TMDL program could start by assessing supply-demand risks across operational areas, setting performance targets such as reservoir reliability and shortage frequency, and then using the diagnostic to identify which strategies fit each archetype. Results and lessons could be shared region-wide, while implementation would remain locally driven.

This suggestion is, emphatically, not a prescription for specific policy mechanisms. But it serves as a reminder that—just as few of us engage with workplace assessments or change behavior on the basis of their results without organizational support—archetypes will need to be paired with implementation structures to translate diagnosis into action.

Beyond Silver Bullets

There is no single answer to our trillion-dollar question, but one path forward for sustaining complex Western ecosystems lies in developing archetypes of different types of basins.

Nearly 20 years ago, Ostrom [2007] warned against seeking panaceas for complex environmental problems. There is no silver bullet for snow loss or single answer to our trillion-dollar question, but one path forward for sustaining complex Western ecosystems lies in developing archetypes of different types of basins.

A small irrigation district, for example, wouldn’t need to independently test every strategy in Figure 1 or develop complex decision support tools when a similar archetype already evaluated which strategies work under comparable governance, behavioral, and physical conditions.

Critically, these archetypes can be developed and refined by managers and scientists to capture more nuanced realities. Physically constrained systems, for example, could include areas facing high future uncertainty or limited reservoir flexibility. Governance and behavioral dimensions could likewise evolve to represent contexts where subsidies lead to incoherent incentives or where cultural norms link water use to local identities and traditions.

Like workplace assessments, the goal isn’t to diminish unique personalities but to work with them more strategically. Archetypes can show where we don’t need to reinvent the wheel to adapt and where the wheel might need to be tweaked. By leveraging collective knowledge and learning across regions facing similar challenges, rather than crafting new solutions basin by basin, we can reduce the time and resources needed to implement equitable and sustainable adaptation solutions.

Acknowledgments

This work is supported by the National Science Foundation (NSF) under grants 1828902 and OIA-2148788. Where We Live is funded by a grant from NSF’s Established Program to Stimulate Competitive Research (EPSCoR) RII Track-2 program and features partnerships across the University of Idaho (award 2316126); the University of Nevada, Reno (award 2316127); and the University of South Carolina (award 2316128). Work was also supported by internal funds from the Division of Hydrologic Resources at the Desert Research Institute.

References

Boisramé, G. F., et al. (2026), Think globally, model locally: Complex responses of agricultural water supplies to different climate projections, J. Am. Water Resour. Assoc., 62(3), e70117, https://doi.org/10.1111/1752-1688.70117.

Di Baldassarre, G., et al. (2018), Water shortages worsened by reservoir effects, Nat. Sustainability, 1(11), 617–622, https://doi.org/10.1038/s41893-018-0159-0.

Garcia, M., et al. (2019), Towards urban water sustainability: Analyzing management transitions in Miami, Las Vegas, and Los Angeles, Global Environ. Change, 58, 101967, https://doi.org/10.1016/j.gloenvcha.2019.101967.

Gharib, A. A., et al. (2023), Assessment of vulnerability to water shortage in semi-arid river basins: The value of demand reduction and storage capacity, Sci. Total Environ., 871, 161964, https://doi.org/10.1016/j.scitotenv.2023.161964.

Gordon, B. L., et al. (2024), The essential role of local context in shaping risk and risk reduction strategies for snowmelt‐dependent irrigated agriculture, Earth’s Future, 12(6), e2024EF004577, https://doi.org/10.1029/2024EF004577.

Grafton, R. Q., et al. (2018), The paradox of irrigation efficiency, Science, 361(6404), 748–750, https://doi.org/10.1126/science.aat9314.

Ostrom, E. (2007), A diagnostic approach for going beyond panaceas, Proc. Natl. Acad. Sci. U. S. A., 104(39), 15,181–15,187, https://doi.org/10.1073/pnas.0702288104.

Qin, Y., et al. (2020), Agricultural risks from changing snowmelt, Nat. Clim. Change, 10, 459–465, https://doi.org/10.1038/s41558-020-0746-8.

Siddik, M. A. B., et al. (2023), Interbasin water transfers in the United States and Canada, Sci. Data, 10, 27, https://doi.org/10.1038/s41597-023-01935-4.

Sietz, D., et al. (2019), Archetype analysis in sustainability research: Methodological portfolio and analytical frontiers, Ecol. Soc., 24(3), 34, www.jstor.org/stable/26796999.

Steyaert, J. C., et al. (2022), ResOpsUS, a dataset of historical reservoir operations in the contiguous United States, Sci. Data, 9, 34, https://doi.org/10.1038/s41597-022-01134-7.

Sturm, M., et al. (2017), Water and life from snow: A trillion dollar science question, Water Resour. Res., 53(5), 3,534–3,544, https://doi.org/10.1002/2017WR020840.

Author Information

Beatrice L. Gordon (beatrice.gordon@dri.edu), Gabrielle F. S. Boisrame, Christine M. Albano, and Rosemary W. H. Carroll, Desert Research Institute, Reno, Nev.; and Adrian A. Harpold, University of Nevada, Reno

Citation: Gordon, B. L., G. F. S. Boisrame, C. M. Albano, R. W. H. Carroll, and A. A. Harpold (2026), Archetypes could accelerate agricultural adaptation to less snowpack, Eos, 107, https://doi.org/10.1029/2026EO260184. Published on 9 June 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.

To date news reports suggest two fatal landslides with a combined toll of 17 people.

There are various news reports trickling in about the landslides triggered by the 8 June 2026 M=7.8 earthquake offshore Mindanao in the Philippines. As usual, the remote locations of many of the landslides means that the information is a bit hit and miss at this point.

To date, the most serious event appears to have occurred at a community called New Aklan, located in Glan, Sarangani. It appears that New Aklan is at: [5.7705 N, 125.3356]. News reports indicate that 13 people were killed, although there are also indications of additional fatalities in this area.

A further four people are missing under a landslide at Sitio Buhangin, Barangay Patuco, Sarangani. Patuco is in the area of [5.4770, 125.4859]. This appears to have been a failure on a coastal cliff:-

A failure in a coastal cliff at Sitio Buhangin, Barangay Patuco, Sarangani following the 8 June 2026 earthquake near Mindanao. Image tweeted by Radyo Pilipinas.

Over the next few days, satellite imagery should become available that will help identify the landslide impacts, but in the meantime Dan Shugar has identified some (using Planet imagery, I’d imagine):-

Return to The Landslide Blog homepage Text © 2026. The authors. CC BY-NC-ND 3.0
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Source: Earth’s Future

The space industry is surging. In coming years, nearly 10,000 spacecraft are slated to launch into low-Earth orbit for a variety of purposes, such as global surveillance, space tourism, and satellite “megaconstellations” providing internet service.

Rocket engine exhaust, as well as the burnup of inactive satellites and rocket parts reentering Earth’s atmosphere, releases a suite of pollutants. These chemicals have long been considered to pose little threat to our climate, given the historically small size of the space industry. Now, the sector’s rapid growth will send its emissions skyrocketing—but scientists don’t yet have a clear picture of the environmental ramifications.

An analysis by Vliex et al. of rockets launched in 2022 revealed that spaceflight depletes the ozone layer and contributes to global warming, with a significant portion of this ozone loss attributable to nitrogen oxide emissions released by objects reentering Earth’s atmosphere.

The researchers calculated emissions from all 186 rockets launched in 2022, as well as all 472 objects—with a combined total mass of nearly 5,000 tons—that reentered the atmosphere that year. They conducted computational simulations of each launch’s trajectory and emissions at various altitudes up to 100 kilometers, and they calculated emissions released by object reentry. They also accounted for the effects of chemical reactions that occur in rocket exhaust plumes, which alter emissions’ chemical composition.

Incorporation of the calculated emissions into GEOS-Chem, a computational model of atmospheric chemistry, revealed their ozone-depleting and Earth-warming effects, with reentry emissions identified as playing a key role in ozone depletion. The researchers found that accounting for plume reactions reduced the estimated effects of spaceflight emissions, highlighting the value of considering plume chemistry in future assessments.

The analysis also underscored the varying effects of different rocket fuel types. Solid-state fuels, used recently in rocket boosters for NASA’s Artemis II mission to return astronauts to the Moon, appeared to cause the greatest amount of ozone depletion relative to propellant mass, while rocket-grade kerosene caused the greatest amount of warming.

On the basis of their findings, the researchers call for further research into reentry emissions and rocket plume chemistry as the space industry continues to expand and evolve. (Earth’s Future, https://doi.org/10.1029/2025EF007795, 2026)

—Sarah Stanley, Science Writer

Citation: Stanley, S. (2026), Rocket launches and reentries harm Earth’s ozone layer, Eos, 107, https://doi.org/10.1029/2026EO260183. Published on 8 June 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.

Initial analyses suggest that the earthquake this morning has the potential to have triggered significant numbers of landslides and areas of liquefaction.

At the time of writing, the impacts of the M=7.8 earthquake that occurred offshore the south coast of Mindanao in the Philippines remain unclear. Initial reports in the local press suggest 15 fatalities so far, but as always it could be the case that there is no information from those areas most seriously impacted.

The USGS Pager site is the best source of information about potential landslide impacts, bearing in mind there is a high level of uncertainty. This estimates that the area exposed to landslides is at the high end of the “significant” scale and that the population exposed to landslides lies in the 1,000 to 10,000 people range. This is the Pager landslide hazard map:-

Initial Pager map of landslide hazard from the 8 June 2026 earthquake offshore Mindanao in the Philippines. Source: USGS.

The area with the highest level of landslide hazard is remote and rural, so we may not get good information from this area for a while.

The potential for liquefaction may be even more serious, with a broad swathe having a high level of hazard:-

Initial Pager map of liquefaction hazard from the 8 June 2026 earthquake offshore Mindanao in the Philippines. Source: USGS.

Past earthquakes have generated large liquefaction-related landslides on low angle slopes, with devastating effects. Hopefully, there won’t have been an event on this scale in Mindanao.

One final point to note is that the Philippines is just entering the typhoon season. Fortunately, Mindanao is sufficiently far south to be away from the main typhoon zone. However, these storms are so large that they can bring very heavy rainfall – see for example Typhoon Bopha in 2012. A similar event this year could have very significant consequences.

Return to The Landslide Blog homepage 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.

Source: Earth’s Future

Mangrove forests straddle the edge of land and sea along some tropical and subtropical coastlines. These trees and shrubs have distinctive tangles of roots that trap sediment and produce organic matter, forming dense soils and efficiently storing carbon. Though mangroves cover only 1% of Earth’s surface, they store a whopping 15% of global ocean carbon in their trapped soils.

Their location along coastlines means mangroves are at the mercy of changing sea levels and sediment availability. Rising sea levels can drown mangroves or push them landward. At the same time, sediment supplies, belowground root growth, and organic matter accumulation can help build up mangrove soils, allowing forests to keep pace with sea level rise. So over time, will mangroves keep locking carbon into their soils, or will they start losing it?

Iwantoro et al. created a new model that examines the links between coastal processes to investigate vegetation growth and carbon accumulation in mangrove forests.

The researchers modeled a simplified tidal embayment to explore how different rates of sea level rise and sediment supplies would affect the mangroves. In these experiments, they found that carbon accumulation can increase at specific locations as waters rise because the increased water can lead to more mangrove growth—a result that matches existing data. However, when looking at landscape scales, they found sea level rise generally reduces total carbon sequestration through mangrove loss and soil erosion. The results showed that rising sea levels can alter mangroves from carbon storage sinks to carbon emitters.

The findings demonstrate that local trends in carbon sequestration may not be representative of larger-scale outcomes in mangrove forests. The study shows that understanding coastal landscapes as an interconnected system is crucial to understanding how mangroves can respond to climate and human-induced pressures, the researchers say. However, new assessments and approaches are needed to better understand future mangrove vulnerabilities. (Earth’s Future, https://doi.org/10.1029/2025EF006984, 2026)

—Sarah Derouin (@sarahderouin.com), Science Writer

Citation: Derouin, S. (2026), Mangroves may be losing their grip on carbon storage as sea levels rise, Eos, 107, https://doi.org/10.1029/2026EO260144. Published on 5 June 2026. Text © 2026. AGU. CC BY-NC-ND 3.0
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Source: AGU Advances

Asteroids and planetesimals regularly bombarded Earth between about 4.6 billion and 3.5 billion years ago, in the Hadean and Archean eons. Because few rocks today are more than 4 billion years old, our understanding of the planet’s environment during that time is limited. However, samples from the Moon and its cratered surface hint at the period’s rate of cosmic impacts.

Early asteroid strikes were responsible for significant changes in Earth’s crust, which was primarily basalt-like at the time. The shock waves from collisions fractured the crust and increased porosity, allowing fluids and gases to move through the rocks. Prior research suggests that the resulting hydrothermal systems—such as the network of geysers around Yellowstone National Park—provided the environment for the origin and evolution of early life on Earth.

Alexander et al. explored how surface impacts during the Hadean and Archean allowed fluids and gases to maneuver through crustal environments. The authors built a large suite of impact simulations with the iSALE shock physics code, toggling parameters such as basalt crust thickness, geothermal gradients, and the presence or absence of a 5-kilometer-deep ocean. The simulations detailed how collisions on the surface shaped permeability in the crust. They then integrated a model for ancient bombardment data to understand the cumulative effects of repeated strikes over time.

The results indicate that prior to 4.3 billion years ago, impacts may have made the crust far more permeable, particularly in its top 8 kilometers. From the simulations, the authors inferred that the size of permeable regions was dependent on impact energy, and that geothermal gradients and rock composition in the crust affected the degree of fragmentation after impact. These porous domains formed potential settings for prebiotic chemistry within the early crust.

The research is the first comprehensive study of impact-generated permeability in early Earth’s outermost layer. The results provide a novel framework for evaluating how bombardment influenced hydrothermal circulation and geochemical alteration during the Hadean and Archean eons, with implications for our understanding of life’s origin and evolution in Earth’s earliest days. (AGU Advances, https://doi.org/10.1029/2025AV002097, 2026)

—Aaron Sidder, Science Writer

Citation: Sidder, A. (2026), Cosmic bombardment created potential for prebiotic chemistry, Eos, 107, https://doi.org/10.1029/2026EO260180. Published on 5 June 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.

After centuries of overharvesting and environmental degradation reduced the world’s oyster reefs by 85%, restoration is bringing the conglomerations of thick-shelled mollusks back to coastal waters. And their return may have more benefits than scientists realized, new research suggests.

“Oysters build the foundation of an entire ecosystem.”

Oysters were initially restored to boost depleted fisheries, according to Rachel Smith, a marine ecologist at the University of California, Santa Barbara. As oysters cement their shells together into reefs, they create habitats for myriad species, including fish. “Oysters build the foundation of an entire ecosystem,” Smith said.

These days, oyster reefs are restored for reasons extending beyond ecology, including to rid coastal water of excess nutrients such as nitrogen. This pollutant enters coastal waters when wastewater, sewage, and fertilizer wash into the sea.

Past studies of nitrogen removed by oyster reefs largely looked at denitrification, a process in which microbes transform organic nitrogen in dead oysters and their excrement into inert gas. If organic nitrogen evades these microbes, it can be buried in reefs, but measurements of this mechanism are few.

Researchers collected cores from 20 oyster reefs in coastal North Carolina. Credit: Antonio Rodriguez/Institute of Marine Sciences, UNC-Chapel Hill

“[Burial] is definitely much less explored,” said Smith.

A study published in PLoS One looked beyond denitrification and found significant amounts of nitrogen become sequestered within oyster reefs as they grow, offering evidence that restored oyster reefs actually remove far more nitrogen than we thought.

Before she started this research, Anne Margaret Smiley, lead author of the new paper and a biogeochemist at the University of North Carolina (UNC) at Chapel Hill, suspected that the amount of nitrogen buried in oyster reefs would be small because organisms at the surface transform so much of it, leaving little left to bury. She was pleasantly surprised by the results.

“We’ve been looking at denitrification all this time, and now we found out that [oysters themselves] are really good at doing this too,” she said. “What an amazing thing to know.”

In Search of Buried Nitrogen

To explore how nitrogen is buried over time, scientists turned to 20 oyster reefs in the Rachel Carson National Estuarine Research Reserve near Beaufort, N.C., that were restored nearly 3 decades ago by UNC scientists.

Using a jackhammer and metal pipe, they extracted cores from the oyster reefs in 2011. About 10 centimeters in diameter, the cores sampled the full thickness of each reef, which ranged from 10 to 55 centimeters. Shortly after they were collected, the cores were sectioned off into 5-centimeter increments, sealed, and stored in a walk-in freezer. In the years since, the samples have proved useful for studying oyster reef growth during sea level rise and how much carbon the reefs sequester and in other areas of research. Recently, Smiley measured the nitrogen levels in each of these 5-centimeter sections.

Below the top 10 centimeters or so, where microbes break down organic matter, nitrogen levels increased. Looking at all samples, Smiley found that on average, a square meter of reef buried more than 6 grams of nitrogen each year, which is similar to the rate of nitrogen transformed by denitrification at oyster reefs.

“The more they can build up and out, the more [nitrogen] they can bury underneath.”

However, there was a large range in the amount of nitrogen buried, between 1 and 15 grams of nitrogen per square meter. The variability, the researchers found, was related to where the different oyster reefs grew.

For oyster reefs in sand flats, those in intertidal areas (between high and low tide on a shore) buried more than twice as much nitrogen as subtidal reefs, on average. Intertidal reefs grow faster and so bury more nitrogen. “The more they can build up and out, the more [nitrogen] they can bury underneath,” said Smiley.

But intertidal reefs that fringed the edge of salt marshes buried less nitrogen than other intertidal reefs. “They’re not able to grow as quickly,” she said, speculating that sediment from the neighboring marshes may slow reef growth.

Put Your Money Where Your Mollusk Is Intertidal oyster reefs, like this one in coastal North Carolina, are exposed above water at low tide. Credit: Johanna Rosman/Institute of Marine Sciences, UNC-Chapel Hill

North Carolina’s Department of Environmental Quality places the economic value of each kilogram of nitrogen removed from the environment at $26.39 (in 2024 dollars, which is about $28.50 in 2026). Using this figure, Smiley and her colleagues calculated that nitrogen removed from coastal waters and buried each year by a hectare of oyster reef has a value of $1,700 on average. This finding increases previous estimates of the value of oysters’ nitrogen removal services by 25% to 42%.

“A really valuable part of the study is not just taking those measurements, but then also translating that into valuation,” said Smith, who was not involved with the new study. The value of nitrogen burial can be added to oyster reef ecosystem services—the monetary value of benefits that humans gain from oyster reefs, such as clean water, food, and flood control. “[Buried nitrogen] is definitely an ecosystem service that I think is underappreciated,” she said.

Looking more broadly at the county that is home to the Rachel Carson Reserve, Smiley and her colleagues found that all the oyster reefs countywide bury about 120,000 kilograms of nitrogen each year—more than $3 million of value in the county’s shallow sounds and bays.

—Lisa S. Gardiner (@lisasgardiner.bsky.social), Science Writer

Citation: Gardiner, L. S. (2026), Oysters clean up more nitrogen pollution than we thought, Eos, 107, https://doi.org/10.1029/2026EO260182. Published on 4 June 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.

Editors’ Vox is a blog from AGU’s Publications Department.

Mesoscale and submesoscale ocean processes influence ocean circulation, air-sea fluxes, ecosystem variability, and transport of materials. A new article in Reviews of Geophysics examines how these fine-scale processes shape the Indian Ocean, an ocean basin with unique monsoon behavior and a disproportionate impact on global climate. Here, we asked the authors to explain what mesoscale and submesoscale processes are, the techniques and challenges of observing and modeling fine-scale processes, and how biogeochemical cycles and climate change interact with these processes.

In simple terms, what are mesoscale and submesoscale processes?

Mesoscale processes pertain to oceanic features such as eddies and fronts, which
typically span a range of approximately 10 to 100 kilometers and can persist from
weeks to months. Submesoscale processes are of an even smaller scale, ranging
between approximately 100 meters and 10 kilometers, and evolve rapidly within a time frame of hours to days. These encompass sharp fronts, filaments, and small vortices.

Mesoscale processes account for more than 80% of the total kinetic energy. Submesoscale motions are of particular significance as they generate robust vertical movements that establish a connection between the surface ocean and deeper layers. As elaborated in our review, mesoscale and submesoscale processes function as a crucial link between large-scale ocean circulation and small-scale turbulence, facilitating the transfer of energy across different scales and regulating the distribution of heat, salt, and nutrients throughout the ocean.

Why is it important to understand how fine-scale processes operate in the Indian Ocean?

The Indian Ocean has a disproportionate influence on global climate.

The Indian Ocean has a disproportionate influence on global climate. It absorbs over a quarter of the ocean’s net heat gain and directly affects the environment and food security of nearly one-third of the world’s population. Unlike other ocean basins, the Indian Ocean is uniquely shaped by seasonally reversing monsoon winds and is strongly coupled with climate modes like the Indian Ocean Dipole and the Madden- Julian Oscillation. Mesoscale and submesoscale variability in this region modulates biogeochemical cycles, air-sea fluxes, and even large-scale energy balance. As our review highlights, understanding these fine-scale dynamics is essential for improving predictions of monsoon rainfall, tropical cyclone behavior, and long-term climate change.

How do scientists study mesoscale and submesoscale ocean processes?

Scientists employ a combination of field measurements, satellite observations, and numerical models, all of which were summarized in our review. In-situ observations serve as the foundation for mesoscale and submesoscale processes in the ocean. They encompass research cruises, moored arrays such as the RAMA network, Argo profiling floats, and autonomous platforms. The in-situ observations capture the three-dimensional structures and multiple variables during mesoscale and submesoscale processes.

Satellite altimetry has long been the principal tool for observing mesoscale eddies. However, the newly launched Surface Water and Ocean Topography (SWOT) mission is revolutionary, as it offers sea surface height measurements at an unprecedented resolution, enabling the direct observation of submesoscale features for the first time.

High-resolution regional models with grid spacings of a few kilometers or less enable researchers to simulate these processes and test dynamical theories under controlled conditions.

What are some of the challenges in observing and modeling these processes?

In our review paper, we tackled the challenges in observations by adhering to four principles, namely high-resolution (more observations in a relatively small region), synchrony (observations conducted at the same time), persistence (observations for a long time), and interdisciplinary (observations of multiple ocean properties). These principles are anticipated to offer valuable guidance for future observational endeavors to surmount the corresponding challenges.

Modeling also poses difficulties. Even state-of-the-art climate models are unable to explicitly resolve submesoscale processes. Consequently, their effects have to be approximated via parameterizations. The development of accurate parameterizations continues to be an active area of research. Moreover, as the model resolution improves, the widely employed hydrostatic approximation may lose its validity, necessitating more intricate non-hydrostatic formulations. Data assimilation for such rapidly evolving features presents a particularly arduous challenge.

How do fine-scale processes interact with biogeochemical cycles in the Indian Ocean?

Mesoscale and submesoscale motions exert a strong regulatory influence on biogeochemical cycling.

Mesoscale and submesoscale motions exert a strong regulatory influence on biogeochemical cycling through the control of nutrient supply to the sunlit upper ocean. Cyclonic eddies elevate nutrient-rich deep waters into the euphotic zone, thereby promoting phytoplankton blooms. In contrast, anticyclonic eddies typically suppress surface productivity by deepening the mixed layer.

In the Arabian Sea, eddies and filaments can contribute up to 70% of the nutrients that support the monsoon-driven biological bloom. These fine-scale dynamics also have an impact on carbon dioxide exchange; mesoscale variability accounts for approximately 40% of the CO₂ flux variability in the western Arabian Sea. Moreover, eddies modulate oxygen minimum zones in the Arabian Sea and Bay of Bengal, where low oxygen levels have a profound effect on marine ecosystems.

How is climate change expected to influence these fine-scale processes in the Indian Ocean?

With the continuous progression of climate change, alterations in upper-ocean stratification, propelled by warming and modified freshwater inputs, are anticipated to transform the conditions giving rise to fine-scale instabilities. High-resolution climate model simulations suggest that in a warming global scenario, the eddy-active region associated with the Agulhas Current system may shift westward and poleward. This shift is correlated with the intensification of Agulhas leakage, which refers to the transport of warm Indian Ocean water into the Atlantic. These changes could exert far-reaching effects on global ocean circulation.

Warming is augmenting the frequency and intensity of marine heatwaves in the Indian Ocean.

Moreover, warming is augmenting the frequency and intensity of marine heatwaves in the Indian Ocean. These heatwaves disrupt vertical mixing and nutrient supply, thereby having cascading impacts on biological productivity. Nevertheless, substantial uncertainties persist in quantifying these long-term responses.

In general, there are two-way interactions between climate change and fine-scale processes. Alterations in one component will induce changes in the other, and the former will be subject to feedback from the latter.

What are the remaining questions or knowledge gaps where additional research is needed?

Our review reveals several key priorities. In the short term, specialized multi-scale observational campaigns are acutely required, especially in regions with insufficient sampling, to capture the three-dimensional structure and rapid evolution of submesoscale features. Additionally, a more in-depth understanding is needed regarding how eddies interact with barrier layers—regions characterized by strong salinity stratification that are unique to the northern Indian Ocean—and how these interactions regulate air-sea fluxes and marine heatwaves.

Longer-term challenges encompass integrating fine-scale dynamics into climate models and refining submesoscale parameterizations. Emerging tools from artificial intelligence and machine learning hold potential for representing unresolved processes and enhancing data assimilation. Finally, considering the logistical and financial requirements of fine-scale ocean research, sustained international collaboration will be indispensable.

—Lei Zhou (zhoulei1588@sjtu.edu.cn, 0000-0002-0433-3991) Shanghai Jiao Tong University, China; Dongxiao Wang (dxwang@mail.sysu.edu.cn, 0000-0001-8778-2188) Sun Yat-Sen University School of Marine Sciences, South China Sea Institute of Oceanology, China; Lin Wang (wanglin58@mail.sysu.edu.cn, 0009-0003-1062-5207) Sun Yat-Sen University, China; and Chunhua Qiu (qiuchh3@mail.sysu.edu.cn, 0000-0001-9684-6067)  Sun Yat-sen University School of Marine Sciences, China

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.

Citation: Zhou, L., D. Wang, L. Wang, and C. Qiu (2026), Small-scale Indian Ocean dynamics underpin marine ecology and climate, Eos, 107, https://doi.org/10.1029/2026EO265025. Published on 4 June 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.

body {background-color: #D2D1D5;} Research & Developments is a blog for brief updates that provide context for the flurry of news regarding law and policy changes that impact science and scientists today.

The Trump administration’s National Science Foundation (NSF) has begun dismantling the infrastructure of a $368 million deep-ocean observing program critical to monitoring marine ecosystems, global currents, marine heat waves, and more, according to a 21 May announcement. 

The Ocean Observatories Initiative (OOI), funded by the NSF, has been collecting long-term oceanographic data at multiple deep-ocean sites since 2016. The information about ocean temperature, chemistry, currents, biological conditions, and more is used by scientists to understand a multitude of marine research questions including the activity of the Atlantic Meridional Overturning Circulation (AMOC), a critical ocean current.

“I worry that … we’ll be losing this enormously valuable site where we could really contextualize and detect these changes going forward.”

“There’s a real danger that we lose the ability to keep looking for long-term changes [in the ocean]” as climate change alters Earth systems, said Hilary Palevsky, a marine biogeochemist who has used OOI data for a decade to study how the ocean absorbs carbon dioxide. “I worry that … we’ll be losing this enormously valuable site where we could really contextualize and detect these changes going forward.”

The NSF plans to remove all in-water arrays and infrastructure—including hundreds of deep-sea instruments—from four of the five currently-operating sites within the project: the Global Station Papa Array (in the Gulf of Alaska), Coastal Endurance Array (off the coasts of Oregon and Washington), Global Irminger Sea Array (southeast of Greenland), and Coastal Pioneer Array (off the coast of North Carolina). The removal is expected to occur over the next 15 months, though the process has already begun at the Endurance Array. 

The National Science Foundation’s planned descoping of the Ocean Observatories Initiative will include dismantling four of the five currently operating arrays of equipment. Credit: NSF/OOI

The Trump administration attempted previously to downscale OOI operations, proposing to cut its funding in 2025 and 2026, though Congress never approved the cuts. 

The administration’s decision to dismantle the arrays “aligns with NSF’s wider strategy to have a nimbler approach to prioritizing support for evolving scientific priorities and emerging technologies as well as a deliberate approach to smart life cycle management within its portfolio of research infrastructure,” Michael England, an NSF spokesman, told the New York Times. 

A Dearth of Data

As each array is dismantled, data streams will end, though all previously collected data from OOI networks will remain accessible, Jim Edson, principal investigator for the OOI, wrote in a letter to the oceanographic community. 

Palevsky said there’s “a lot of real concern” among the oceanographic community that the Endurance Array is being dismantled just as an intense El Niño event—and associated marine heat wave—is expected this summer. “It would be especially important to be able to document the effect that [El Niño] is having on coastal physical circulation and ecosystems,” she said. 

 
Related

•  Announcement on NSF OOI Descoping
•  NSF OOI Descoping Update for the Community 
•  Trump Administration to Dismantle Ocean Monitoring System
•  Democrats Pledge to Fight Trump’s Removal of Ocean Monitors
•  Get Involved: AGU’s Science Policy Action Center 

“We encourage the community to use the ten-plus years of OOI data by including it in proposals, publications, presentations, and conversations with colleagues. Continued engagement demonstrates the scientific impact and wide-ranging applications enabled by the OOI and its data, underscoring its importance as a resource for the oceanographic community,” the 21 May announcement stated. 

There are other sources of data that researchers like Palevsky can use. But oceanographic research often requires stitching together different data sets, including OOI observations, satellite observations and observations from the U.S. research fleet. Many of these other sources of data are also facing uncertain futures. 

Palevsky also worries about the loss of expertise that will occur as the program scales down. Installing these deep-sea observing networks was a huge achievement for U.S. science that will not be easy to replicate, she said. “If, in five years, we as a community decide we want to again be able to deploy this kind of complicated infrastructure in places that have really difficult oceanographic conditions … it’s going to be a lot of reinventing the wheel to figure out how to put things out again.”

“The complete cessation without community input or a community conversation about what’s going to happen to all this equipment and what’s going to happen with all of the expertise,” she said, “feels like a huge loss.”

—Grace van Deelen (@gvd.bsky.social), Staff Writer

These updates are made possible through information from the scientific community. Do you have a story about how changes in law or policy are affecting scientists or research? Send us a tip at eos@agu.org. 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.

“I’ve just always felt like art and science are flip sides of the same coin.”

Scientists use tools ranging from models to microscopes to make sense of the world around them. Some might say artists do the same thing using tools such as paintbrushes and musical instruments.

“I’ve just always felt like art and science are flip sides of the same coin, with maybe different outcomes or different processes, but they’re both just getting at the truth of the world,” said Sara Bouchard, a sound artist and composer and adjunct faculty member in the Department of Kinetic Imaging at Virginia Commonwealth University’s (VCU) School of Art.

A recent National Science Foundation–funded collaboration between scientists and artists brought this principle to life.

In fluxART, artists partnered with scientists from FLUXNET, an international network of researchers using eddy covariance techniques to measure how gases move between ecosystems and the atmosphere.

Researchers and artists collaborated on art projects based on data collected at FLUXNET towers. A view from the top of one such tower near Sisters, Ore., is seen here. Credit: Alexander Irving

The scientist-artist pairs worked together in yearlong residencies and produced art pieces—ranging from music compositions and video installations to ceramic works and paintings—that they presented at the Patricia Valian Reser Center for the Creative Arts in Corvalis, Ore., in early 2026.

“Part of the framing of the residency was around flux as this metaphor for connection and belonging and relationships.”

“The metaphor that people use to describe what this science network measures, or does, is that it’s monitoring the breath of the biosphere,” said Maoya Bassiouni, an environmental scientist at the University of California, Berkeley, who directed and developed the residency. “Those fluxes are sort of this giving and receiving between the land and the atmosphere, and it’s exactly what the scientists are doing in the community. So, part of the framing of the residency was around flux as this metaphor for connection and belonging and relationships.”

Bassiouni, who also created artworks in the residency, presented a lecture about the series alongside two other fluxART artists in late May at the National Center for Atmospheric Research’s (NCAR) Mesa Lab in Boulder, Colo.

An installation at NCAR’s Mesa Lab Library featuring all four fluxART projects also opened on 27 May and will be on display through the end of 2026.

En Masse

Bouchard, the sound artist, was paired with Chris Gough, a biogeochemist who serves as the executive director of the Rice Rivers Center at VCU.

Gough studies how factors such as climate and disturbances affect ecosystems, particularly forests and wetlands. Bouchard learned more about Gough’s work by spending a year in his lab.

Virginia Commonwealth University’s Rice Rivers Center Marsh, an AmeriFlux site whose data were used in this project, is located along the James River, seen here. Credit: Megan May Photography

The result was a composition for choir and percussion called En Masse, which explores the connections between communities and ecosystems in a time of climate crisis. The piece’s five movements represent the movement of carbon through the environment: “Air,” “Wood,” “Soil,” “Fire,” and “Breath.”

In addition to vocals and instruments, the composition features birdsong, recordings from a compost pile, sonified data from Gough’s lab, and spoken words gathered from real people sharing their climate anxieties. An excerpt from the “Fire” movement reads,

Future! / Heavy weight on my ribcage / dusty, fragmented
Fire! / Clenched jaw, copper taste in my mouth / stark, shifted
Fire! / I worry about my kids / desperate, unbreathable
Fire! / and their future / squeezed, extreme
Future! Fire! Fire! Fire!

Both Bouchard and Gough said they were moved by the piece as it was performed in Corvalis and by seeing the mix of artists and scientists who attended, many traveling from other states.

“I was struck by how engaged both the scientific and artistic communities were,” Gough said. “We walked out, and it was a full room of people. It was energizing, and I think it felt meaningful in a way that stepping up on a conference stage to deliver the traditional convention talk [isn’t].”

September: Orange

In another pairing, video artist Julia Oldham partnered with Christopher Still, a plant ecophysiologist at Oregon State University.

The partnership started with Oldham visiting a 175-foot-tall (53-meter-tall) FLUXNET tower near Sisters, Ore., that Still and his team monitor.

Video artist Julia Oldham visited a FLUXNET tower near Sisters, Ore., with scientist Christopher Still in preparation for creating an art piece based on data gathered at the tower. Credit: Alex Irving

At the top of the tower, a PhenoCam takes photos of the surrounding Deschutes National Forest every half hour. Still uses data from these images to examine how the greenness of the canopy changes over time because such changes can provide information about fluxes in carbon, water, and energy.

“I learned more about what Chris uses the PhenoCam for and got superexcited about the fact that Chris is using color data to understand forests,” Oldham said. “I thought that that was a really beautiful point of overlap for us as a scientist and an artist, to think about color and forests and what we can learn from color as a scientific tool.”

The pair created two pieces. 18//Flux shows how the colors and light from one PhenoCam site changed from 4 a.m. to 9 p.m. throughout the year for 13 years. Each frame is divided into 13 strips, with each strip representing 1 hour of the monitoring period.

The two had conversations throughout the duration of the project about the growing role of wildfires in the area. In fact, one of the FLUXNET towers they were using in the project burned down.

Their conversations led to September: Orange, a three-channel video showing footage from 24 different PhenoCams in the northwestern United States and Canada. When all of the landscapes are the same shade, the video briefly pauses. In September, when wildfires sweep through Cascadia, orange becomes the dominant color. The piece is accompanied by field recordings from Oregon forests and sonified canopy greenness data.

“I think the installation was a wild success, and I had a lot of people tell me how much they enjoyed it and appreciated it,” Still said. “Most people don’t respond to a 2D graph of data…whereas I think almost everyone responds to images, and photographs are really meaningful to people. So I think that is a really brilliant way to draw people into the science.”

—Emily Gardner (@emfurd.bsky.social), Associate Editor

Citation: Gardner, E. (2026), Artists and scientists partner to bring atmospheric data to life, Eos, 107, https://doi.org/10.1029/2026EO260178. Published on 3 June 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.

Source: Geochemistry, Geophysics, Geosystems

This is an authorized translation of an Eos article. 本文是Eos文章的授权翻译。

大约 6 亿年前，各大洲在地球上漂移，尚未最终定格在现在的位置。在埃迪卡拉纪时期，各大洲的位置对于科学家来说一直难以确定。地球的磁场似乎表现得异常不稳定，而利用标准方法根据磁场记录来计算大陆位置的做法却得出了一些难以置信的结果。尤其是，科学家们对一块名为波罗的大陆的古老大陆的位置存在争议，这块大陆如今是欧洲的一部分。

为了探究这一问题，Xue等人前往挪威埃格尔松德，采集了波罗的大陆地壳被撕裂、岩浆从下方涌出时形成的岩石样本。随着这些岩浆冷却凝固，它们记录了地球磁场的瞬时变化，并在此过程中存储了有关波罗的大陆位置的信息。

对这些样本的研究结果揭示了远比科学家们最初设想的更为复杂的古代岩石图景。这些岩石中至少包含了六种不同的磁信号，构成了一幅复杂的混合图景。其中一些信号似乎是在更现代的地质过程改变原始岩石时形成的。埃迪卡拉纪时期可能保存了三种不同的信号，其中两种与将波罗的板块置于赤道附近的最合理的埃迪卡拉纪信号相悖。这些相互矛盾的信号进一步支持了地球磁场在当时异常活动的观点，使原本就扑朔迷离的图景更加复杂。

基于新的研究结果，研究人员将埃迪卡拉纪时期埃格尔松德古地磁极的位置确定在北纬20.8°、东经89.0°——这与之前的研究结果有所不同——并提出波罗的板块当时位于赤道附近，毗邻古老的劳伦古陆，但相对于之前的重建结果，其位置略有顺时针旋转。这项研究表明，保存在古代岩石中的磁信号极其复杂，并凸显了将这些记录分解成各个组成部分的重要性。研究人员认为，这样做可以为埃迪卡拉纪时期地球磁场的神秘行为提供新的线索。(Geochemistry, Geophysics, Geosystems, https://doi.org/10.1029/2025GC012730, 2026)

—科学撰稿人Saima May Sidik (@saimamay.bsky.social)

This translation was made by Wiley. 本文翻译由Wiley提供。

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Text © 2026. AGU. CC BY-NC-ND 3.0
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Editors’ Vox is a blog from AGU’s Publications Department.

As the AGU Books Program celebrates its 70th anniversary in 2026, we reflect on the longevity of scientific work published in book format and the enduring nature of readership—sometimes for decades after publication. We spoke with Volume Editors and Authors of AGU books published in each of the past 3 decades about why they decided to pursue book projects and why readers are still discovering their work years later.

2000s: Filling Gaps in the Existing Research

Ernie R. Lewis and Stephen E. Schwartz decided to write a book after finding a gap in the literature when conducting their own research. Sea Salt Aerosol Production: Mechanisms, Methods, Measurements, and Models, published in 2004, explores the major influences that sea salt aerosol exerts over diverse areas of geophysics.

Why did you decide to write an AGU monograph? 

Sea salt aerosol is the dominant background aerosol in the atmosphere and the topic of Lewis and Schwartz’s 2004 book. Credit: Richard Dorrell, Wikimedia Commons

We were looking for a quality venue for publication that would lend respect to the book and could accommodate many large, complicated color figures, which were essential to the book. AGU’s Geophysical Monograph Series met these requirements.

We had been examining the literature pertinent to the production of sea salt aerosol, the dominant background aerosol in the atmosphere, to develop means of representing it in chemical transport models for aerosol influences on clouds and climate. We found major discrepancies in reported production flux (orders of magnitude) and in its dependence on controlling variables. Ultimately, we decided we needed to write a book dealing with the physical processes and comparing the numerous prior studies.

How has the study of sea salt aerosols evolved since the publication of your book?

This field has grown enormously since publication of the book in 2004, especially with new studies identifying the role of organics affecting production of aerosol particles, particle composition, hygroscopic properties, and rate of exchange of water between gas and condensed phase.

Why do you think your book continues to be of value to readers?

Perhaps the greatest strength of the book is its emphasis on processes and material properties. The chapter on fundamentals is nearly 100 pages; the chapter on measurements and models required to determine production fluxes is nearly 200 pages. The material in these chapters is essential to understanding the governing processes.

We are gratified by the continuing influence of the book, a measure of which is that the book has been cited over a thousand times, with an average annual citation rate of more than 70 over the past several years—some 20 years after publication.

2010s: Finding the Cutting Edge from AGU Events

A successful 2012 AGU Chapman Conference convinced Venkataraman Lakshmi that a book was needed to document key outcomes from the conference. He went on to co-edit Remote Sensing of the Terrestrial Water Cycle, published in 2014, which examines the use of satellite data for quantifying the spatial and temporal variations in the hydrological cycle.

Why did you decide to edit a book? 

The reason to edit any book is a lack of content on the subject and that the topic is cutting-edge in the research sphere. All the books I have edited with AGU, including Remote Sensing of the Terrestrial Water Cycle, have been outcomes of either sessions organized at the AGU Annual Meeting or a Chapman Conference. The book then serves as a state-of-science for the community and is still widely read.

AGU Annual Meetings and Chapman Conferences have been integral to Lakshmi’s path as a book editor. Credit: Beth Bagley

How has the field of remote sensing as it relates to the terrestrial water cycle evolved since the publication of your book?

The field of remote sensing of the terrestrial water cycle doubles in knowledge every few years. New Earth observing missions have been launched or will be launched soon, and these missions hold promise for unraveling the mysteries of the hydrological cycle.

Why do you think your book continues to be of value to readers? 

The book captures what we can expect from Earth observing missions and sets the stage for how the science questions regarding the water cycle have evolved over the past few decades.

2020s: Building on the Success of Earlier Work

Yongliang Zhang and Larry J. Paxton, from the Johns Hopkins Applied Physics Laboratory, edited not one but five books, published in 2021. This five-volume collection, Space Physics and Aeronomy, presents the latest scientific observations, models, and theories about the Sun and the solar wind, magnetospheres in the solar system, Earth’s ionosphere, Earth’s upper atmosphere, and space weather.

Why did you decide to edit a set of books?

Following a successful AGU 2014 session on auroral dynamics to which about 60 abstracts were submitted, we were invited by editors of three publishers in the United States and Europe to edit a book on auroras. We accepted the invitation from AGU–Wiley as there was a lot of interest in auroral study in the AGU community. We submitted a proposal for a book titled Auroral Dynamics and Space Weather. The book,published in 2015, was successful and a few years later, we were invited to edit multiple books as a major reference work in the field of heliophysics. We took the opportunity and finished the five-book set in 2021.

How have space physics and aeronomy evolved since the publication of your books?

First, new satellite missions and more ground observations are available that fill some of the measurement gaps that existed when we published the books. Second, recent advances in AI capability together with increasing data volume in space physics enable a better specification of the space physics phenomena as well as space weather forecasting.

Why do you think your books continue to be of value to readers? 

These five volumes (six, counting Auroral Dynamics and Space Weather) provide, in one set, a detailed overview of the science of the space environment from the Sun to the Earth and its variability, or “space weather.” A series of books like this is invaluable as a survey of real knowledge that provides readers the opportunity to discover new insights in heliophysics.

As of early 2026, two major imperatives that drive NASA research are facilitating the space economy and supporting the Moon to Mars initiative with an emphasis now on supporting the return to the Moon. Heliophysics, the focus of our books, enables the outward journey to near-Earth space, the Moon, and beyond. Scientists at all stages in their careers are sure to find in these six volumes useful insights that they can use to address new NASA funding opportunities.

Heliophysics, the focus of Paxton and Zhang’s set of books, is essential to new NASA missions. A view of Earth taken by NASA astronaut and Artemis II commander Reid Wiseman from the Orion spacecraft in April 2026. Credit: NASA

These three experiences are just a snapshot of the more than 750 volumes published by AGU Books since the 1950s. While the methods and technologies used in scientific research have evolved dramatically, as has the process and formats for publishing books, the need for volumes covering the breadth of Earth and space sciences remains strong. The AGU Books Program has proven that books—whether the outcome of a gap discovered in the literature, a popular conference session, or the success of previous works—have a lasting place in the ecosystem of scientific publishing.

—Dara Liling (dliling@agu.org, 0009-0005-6828-2811), American Geophysical Union, USA; Venkataraman Lakshmi (0000-0001-7431-9004), University of Virginia, USA; Ernie R. Lewis (0000-0002-2023-7406), Brookhaven National Laboratory, USA; Larry J. Paxton (0000-0002-2597-347X), Johns Hopkins University Applied Physics Laboratory, USA; Stephen E. Schwartz (0000-0001-6288-310X), Stony Brook University, USA; and Yongliang Zhang (0000-0003-4851-1662), Johns Hopkins University Applied Physics Laboratory, USA

Citation: Liling, D., V. Lakshmi, E. R. Lewis, L. J. Paxton, S. E. Schwartz, and Y. Zhang (2026), 7 decades of books leave a lasting legacy, Eos, 107, https://doi.org/10.1029/2026EO265024. Published on 3 June 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.

The volcano Ol Doinyo Lengai in Tanzania is unique on Earth: Its lava is rich in carbon compounds that melt at significantly lower temperatures than typical silicon-rich lavas from other terrestrial volcanoes.

It is possible, however, that carbon volcanoes could exist elsewhere, including on exoplanets, or—as suggested in a recently published article in Icarus—perhaps even on planet Mercury.

Despite being known from antiquity, Mercury is very hard to study because of its closeness to the Sun. As a result, the best data so far were gathered within the past 20 years by NASA’s MESSENGER (Mercury Surface, Space Environment, Geochemistry, and Ranging) probe. In particular, scientists identified mysterious pits they dubbed “hollows” scattered across Mercury’s surface. The hollows’ relatively bright appearance indicates they were formed in recent geological times, and could even be still forming today. The origins and geochemical makeup of these hollows are unknown.

“Mercury looks like the Moon a little bit, so we don’t expect large volcanoes,” said Maximilian Paul Reitze, a planetologist at Universität Münster’s Institut für Planetologie who is first author of the Icarus study. Without volcanic conditions like those on Earth or even on Jupiter’s moon Io, researchers expect Mercury to be largely geologically dormant. In other words, to explain hollows, “we need some volcanism under the conditions we expect on Mercury,” Reitze said.

Hence the interest in Ol Doinyo Lengai, known as the Mountain of God to the Maasai and Sonjo peoples. This volcano produces lava made up of carbonatites, igneous rocks composed of more than half carbon (and which are known to host critical minerals). These lavas flow at temperatures roughly 100°C lower than Mercury’s blazingly hot daytime temperature of 424°C. If the planet has a carbon-rich subsurface, as Reitze and his collaborators proposed, then the hollows could be Mercury’s version of Ol Doinyo Lengai.

This theory, however, has its skeptics.

“We know that there is carbon in [Mercury’s] crust, but the amount is very low,” said Paul Byrne, a planetary scientist at Washington University in St. Louis, who was not involved in the Icarus study. He also pointed out that the surface regions where carbon is most concentrated don’t correspond to higher concentrations of hollows. “For this to be some kind of carbon-based lava, it would imply a lot more carbon than we might think, given how widespread the hollows are.”

The Making of a Weird Planet

Mercury’s proximity to the Sun means that NASA’s Mariner 10 spacecraft provided humanity’s first-ever views when it flew by in 1974 and 1975. Three decades later, the MESSENGER mission was the first probe to orbit Mercury, mapping the planet’s full surface and turning up unexpected features like the hollows. The BepiColombo mission, a joint project of the European Space Agency and the Japan Aerospace Exploration Agency, is only the third mission ever to visit the planet, so when its two spacecraft settle into orbit in November 2026, it will almost inevitably reveal something unexpected, because it’s a weird planet.

“Basically, Mercury is a molten ball bearing wrapped in a thin blanket of rock.”

Unlike Earth, Mars, or the Moon, Mercury has a freakishly large core and a thin mantle.

“Basically, Mercury is a molten ball bearing wrapped in a thin blanket of rock,” Byrne said. “One explanation is that early in the planet’s life, either one large or several smaller impacts stripped the outer portion away.”

The question then becomes what got vaporized, and what was left behind, particularly when trying to understand hollows. Many planetary researchers proposed that sulfides in the mantle could drive volcanism, but Reitze had doubts.

“The problem with sulfides I see is that they’re stable up to 1,000°C or so, which cannot explain the explosive volcanism that’s needed to form those hollows,” he said.

Instead, he and his coauthors contacted a colleague working on Ol Doinyo Lengai, who obtained a sample of the lava for laboratory study while it was still molten. Because carbonatite lava reacts chemically with Earth’s air very quickly, the researchers needed to isolate it to understand how the unaltered materials might behave under conditions on Mercury, particularly infrared spectra that could be confirmed by the BepiColombo mission.

Ol Doinyo Lengai, a volcano in Tanzania, is unique because of its carbonatite lava. Credit: Ben Shoshana/Wikimedia Commons, CC BY-SA 4.0

In the hypothesis proposed by Reitze and colleagues, impacts from meteorites heat the carbon-rich magma below Mercury’s surface, melting it and driving eruptions. The hollows, which are found frequently on the slopes of Mercury’s craters or their central peaks, are the remains of those eruptions. Over time, further meteorite bombardments and intense solar radiation destroyed older hollows, which is why the ones in MESSENGER data were all formed within the past 270 million years—a short time ago, geologically speaking.

“Anytime people have been confident about anything in planetary science, [planets have] shown you wrong.”

“The carbonatite angle is an interesting one, and I certainly wouldn’t rule it out,” Byrne said. “Anytime people have been confident about anything in planetary science, [planets have] shown you wrong. I’m certainly open to it, but is it the only explanation for all of the hollows? I am skeptical of that.”

Byrne and Reitze both dream of a future Mercury lander, a very challenging and expensive proposition nobody expects will happen soon. In the meantime, they agreed that BepiColombo data will help settle the question of whether the most Mercury-like place on Earth is a volcano in Tanzania.

—Matthew R. Francis (@BowlerHatScience.org), Science Writer

Citation: Francis, M. R. (2026), A unique African volcano could solve a mystery on Mercury, Eos, 107, https://doi.org/10.1029/2026EO260176. Published on 2 June 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.

Source: Geophysical Research Letters

Atmospheric rivers act like “rivers in the sky,” shuttling intense bands of warm, heavy moisture from lower to higher latitudes. When an atmospheric river encounters cold air or mountainous terrain, the moisture it carries condenses and falls as heavy rain or snow. In Antarctica, the arrival of an atmospheric river can help build surface ice mass. Much of Antarctica is very dry; an atmospheric river can bring the moisture needed to potentially offset some ice loss.

Antarctica’s varied topography and dry conditions have made detecting atmospheric rivers over the continent challenging. Previous efforts to do so have suggested that atmospheric rivers contribute up to 30% of Antarctica’s total annual precipitation, but these methods may not be capturing the full picture of atmospheric river activity.

Takahashi et al. developed a new 3D atmospheric river detection algorithm to better capture how atmospheric rivers affect Antarctica’s complex terrain. Previous methods have mostly been 2D, meaning they do not accurately account for the vertical variations within an atmospheric river.

To evaluate the algorithm, the researchers applied it to two datasets: (1) daily snowfall totals measured during the 44th Japanese Antarctic Research Expedition (JARE44) at Dome Fuji from February 2003 to January 2004 and (2) the ERA5 (European Centre for Medium-Range Weather Forecasts atmospheric reanalysis) dataset of daily weather patterns and conditions in Antarctica from 1979 to 2023.

The results of the study’s new algorithm showed 16 significant snowfall events during the JARE44 expedition, all of which were not detected by the older 2D method. The new 3D method identified 17 days of atmospheric river activity, which corresponded with 10 heavy snowfall events and accounted for approximately 40% of the total precipitation. Between 1979 and 2023, atmospheric rivers occurred about 10% of the time yet contributed 30%–60% of total precipitation in the Antarctic interior.

The 3D method in the new study suggests that atmospheric river events contribute a greater proportion of total snowfall than previously thought—between 30% and 90%, depending on the Antarctic region. The researchers also suggest that long-term changes in Antarctic snowfall are closely linked with the changes in atmospheric river activity. This connection is especially apparent in East Antarctica, where the link between snowfall increases and atmospheric rivers had not yet been clearly identified in previous studies. (Geophysical Research Letters, https://doi.org/10.1029/2025GL120986, 2026)

—Rebecca Owen (@beccapox.bsky.social), Science Writer

Citation: Owen, R. (2026), Rivers in the Antarctic sky, captured in 3D, Eos, 107, https://doi.org/10.1029/2026EO260179. Published on 2 June 2026. Text © 2026. AGU. CC BY-NC-ND 3.0
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Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: Journal of Geophysical Research: Solid Earth

Enhanced Geothermal Systems (EGS) can expand low-carbon energy production, but fluid injection may trigger earthquakes whose locations and mechanisms are difficult to predict. Feng et al. [2026] investigate induced seismicity at China’s first EGS site in the Gonghe Basin using a comprehensive observational dataset. Machine learning processing of data from 20 surface seismic stations produced a high-resolution earthquake catalog with well-constrained locations and focal mechanisms. Stress inversion and modeling, constrained by borehole stress measurements, reveal mechanically weak faults with low friction coefficients, indicating that low-to-moderate fluid overpressure can trigger seismic slip. Site-scale analysis shows that seismicity reflects shear reactivation of pre-existing natural faults, rather than the creation of new tensile fractures. Further integration with borehole image logs reveals a fine-scale relationship between the main seismogenic zones and stress heterogeneity, expressed as rotations of the principal stress axes that likely reflect localized lithological contrasts and fault-damage zones.

Together, these integrated analyses show that geothermal-induced seismicity is controlled by inherited fault architecture at the site scale and localized stress heterogeneity at the borehole scale. By linking seismic observations to borehole stress and image-log evidence, the study provides a more physically constrained framework for seismic-hazard assessment and stimulation design in enhanced geothermal reservoirs.

Citation: Feng, P., Wang, R., Zhang, H., Zhang, C., Schultz, R., & Yang, L. (2026). Pre-existing structures and stress variations jointly control the induced seismicity in enhanced geothermal system of Gonghe Basin, China. Journal of Geophysical Research: Solid Earth, 131, e2025JB033158. https://doi.org/10.1029/2025JB033158  

—Xiaowei Chen, Associate Editor, JGR: Solid Earth

Text © 2026. The authors. CC BY-NC-ND 3.0
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body {background-color: #D2D1D5;} Research & Developments is a blog for brief updates that provide context for the flurry of news regarding law and policy changes that impact science and scientists today.

A Colorado judge has granted a preliminary injunction to the University Corporation for Atmospheric Research (UCAR). The move temporarily blocks the federal government from moving forward with one part of its effort to dismantle UCAR’s National Center for Atmospheric Research (NCAR) by transferring stewardship of a state-of-the-art supercomputing facility.

Together, UCAR—a nonprofit consortium of universities and colleges—and the National Science Foundation (NSF) operate and maintain the NCAR-Wyoming Supercomputing Center (NWSC) in Cheyenne, Wyo. The facility provides scientists with enormous computational power necessary to run sophisticated analyses of weather, climate, and other Earth systems.

 
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In February, as another step in a chain of actions taken to dismantle NCAR, the NSF informed UCAR and NCAR that it would transfer management and operations of NWSC to a third-party operator.

In turn, UCAR filed a lawsuit alleging that the action violated federal law under the Administrative Procedure Act (APA). To halt NSF’s action under the act, the agency’s attempt to remove UCAR’s stewardship of the facility must be shown to be “arbitrary, capricious, an abuse of discretion, or otherwise not in accordance with law.”

Judge Richard Brooke Jackson of the U.S. District Court for the District of Colorado wrote in a 1 June court order that the action was both arbitrary and capricious “for at least two reasons.” First, NSF didn’t offer an explanation for its decision, and second, it didn’t follow an outlined process to consider public feedback.

The decision means that UCAR will temporarily retain its stewardship of NWSC. 

“NSF’s failure to provide any explanation for its decision—let alone a reasonable one—thwarts meaningful judicial review and renders the challenged action arbitrary and capricious,” Jackson wrote.

He went on to note that efforts to transfer stewardship of UCAR assets, including the supercomputing center, to other institutions, pose the risk of “irreparable harm” to UCAR. One of the chief harms would be brain drain, the judge noted multiple times, writing that “UCAR cannot easily replace employees with the level of education, specialized training, and institutional knowledge necessary to operate and maintain the NWSC’s ‘highly integrated, high-performance supercomputing system.'”

In addition to brain drain, Jackson cited financial injuries to UCAR that would be “difficult, if not impossible” to quantify, as well as an overall threat to the consortium’s mission.

“Any degradation in forecasting, modeling, or related scientific capabilities carries real-world consequences, including potential harm to property and human life. Given those stakes, the public interest strongly favors maintaining the status quo unless and until NSF demonstrates that its transfer decision complies with the APA,” he concluded.

In a statement posted to the UCAR website, the consortium’s interim president, Eric Barron, said UCAR was pleased that Judge Jackson recognized how harmful the proposed transfer would be for the the nation’s scientific enterprise.

“UCAR’s top priority is to advance Earth system science in service to society,” he wrote. “Today’s decision ensures that the NWSC will be able to continue its vital work on behalf of the United States and its stakeholders without interruption.”

—Grace van Deelen (@gvd.bsky.social), Staff Writer, and Emily Gardner, (@emfurd.bsky.social), Associate Editor

These updates are made possible through information from the scientific community. Do you have a story about how changes in law or policy are affecting scientists or research? Send us a tip at eos@agu.org. Text © 2026. AGU. CC BY-NC-ND 3.0
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