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Intrinsic water-use efficiency (iWUE) reflects how efficiently plants assimilate carbon relative to water loss at the leaf level. While widely studied using carbon isotope and gas-exchange measurements, most existing knowledge is derived from local observations.

Curtin University researchers have demonstrated a new way to uncover the ancient history of Australia's landscapes, which could offer crucial insights into how our environment responds to geological processes and climate change and even where deposits of valuable minerals may be found.

Most people can imagine why a shrinking Great Salt Lake would mean unhealthy dust storms for the Wasatch Front, or why refilling the lake through water conservation could reduce dust exposure. Now, there is a data-based modeling tool to visualize it, hosted at the University of Utah's Wilkes Center for Climate Science & Policy.

Reliable predictions of how the Earth's climate will respond as atmospheric carbon dioxide levels increase are based on climate models. These models, in turn, are based on data from past geological times in which the CO2 content in the Earth's atmosphere changed in a similar way to today and the near future. The data originate from measurable indicators (proxies), the interpretation of which is used to reconstruct the climate of the past.

While a wealth of nutrient export models exists, a knowledge gap persists regarding how climate and land-use changes specifically drive dissolved inorganic nitrogen (DIN) export in subtropical catchments.

Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: AGU Advances

Asteroids, moons, and comets near Earth act like fossils from the time when our solar system first formed. In a new article, de Kleer et al. [2025] explain how a powerful telescope called the Atacama Large Millimeter/submillimeter Array (ALMA) has changed the way scientists study these small worlds. ALMA can detect very weak thermal emission (heat) signals, allowing researchers to map the surface features of asteroids and accurately measure the masses of distant objects beyond Neptune, known as Kuiper Belt Objects.

ALMA is also used to study gases released by volcanic eruptions on Jupiter’s moon Io and probe the thick atmosphere of Saturn’s moon Titan. The review emphasizes the study of isotopes, which are slightly different forms of the same chemical element. These isotopes act like chemical fingerprints, helping scientists track how elements such as nitrogen and sulfur have changed over time. By comparing these local measurements with observations of young planetary systems around other stars, scientists can better understand how the ingredients for life survived the violent process of planet formation.

Citation: de Kleer, K., Brown, M. E., Cordiner, M., & Teague, R. (2025). Satellites and small bodies with ALMA: Insights into solar system formation and evolution. AGU Advances, 6, e2025AV001778. https://doi.org/10.1029/2025AV001778

—Xi Zhang, Editor, AGU Advances

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: Journal of Geophysical Research: Biogeosciences

Human activities add large quantities of nitrogen to the environment, much of which gets washed into streams and rivers. These waterways transport some of that nitrogen to the oceans, but they also remove a significant portion of it through a process called denitrification: Microbes facilitate a series of chemical reactions that turn nitrate into dinitrogen gas, which is then released into the atmosphere.

Existing research, largely in streams, shows a wide range of denitrification rates, but the factors affecting this process aren’t fully quantified, especially in rivers. Pruitt et al. compared denitrification rates in a stream and a river across three seasons to study how the process varies across waterway scales.

The researchers took hourly water samples from the Tippecanoe River and the Shatto Ditch in Indiana over 36-hour periods in spring, summer, and fall. They used open-channel metabolism and a membrane inlet mass spectrometry–based model to study how rates of denitrification fluctuated in both waterways as the seasons changed. They found the stream had higher denitrification rates per square meter than the river in all seasons. They attribute this in part to higher nitrate levels in the stream, as well as a proportionally greater contribution of microbial activity on the streambed. However, when the researchers scaled up, the denitrification rate in rivers per kilometer of channel length was equal to or even higher than that of streams.

The researchers also observed different seasonal denitrification patterns. In the stream, denitrification rates were highest in spring and lower in summer and fall, whereas in the river, denitrification rates were highest in the fall, followed by spring, and very low in summer. Fertilizer application and higher precipitation rates in spring likely drive the stream dynamics, they suggest, whereas higher rates of ecosystem respiration increasing denitrifier activity in the fall may explain the pattern seen in the river.

Additionally, nitrogen gas concentrations varied by hour, the authors report, which could help explain the large range of rates found by previous studies. They recommend that future work use both the open-channel method and an in situ chamber assay and compare the two sampling methods. The authors also suggest that separating incomplete from complete denitrification could be valuable to explore the release of nitrous oxide, a potent greenhouse gas, to the atmosphere. (Journal of Geophysical Research: Biogeosciences, https://doi.org/10.1029/2025JG009044, 2025)

—Nathaniel Scharping (@nathanielscharp), Science Writer

Citation: Scharping, N. (2026), Denitrification looks different in rivers versus streams, Eos, 107, https://doi.org/10.1029/2026EO260029. Published on 16 January 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.

Source: Global Biogeochemical Cycles

A critical part of Earth’s carbon cycle is the sinking of particulate organic material from the ocean’s surface to its depths. Much of that material is classified as “marine snow,” which is primarily made of snow-sized (>0.5 mm) detrital organic matter and phytoplankton.

Siegel et al. participated in a field campaign in the northeast Atlantic Ocean during the demise of the spring phytoplankton bloom during May 2021. They set out to observe how both physical processes, such as turbulence created by storms, and biological processes, such as consumption by animals and microbes, affected marine snow dynamics. The researchers used three research vessels, three instrumented gliders, a Lagrangian float, and 10 water-following surface drifters to measure the size distribution and characteristics of marine snow particles in the upper 500 meters of the water column.

The researchers found that near the ocean’s surface, turbulence induced by intense storms caused the breakdown of marine snow particles that later aggregated during calmer conditions. The succession of multiple storm events helped to foster the downward movement of marine snow through the water column. Below a depth of 200 meters, consumption by zooplankton and other organisms drove the removal of the snow particles and their breakdown into smaller ones. The combination of these processes affected how quickly particles sank through the water column and therefore the timescales over which the sinking organic carbon was sequestered from the atmosphere.

Over the course of the experiment, the researchers found that the marine snow particles became fluffier, larger, and more porous, and more marine snow appeared in the water column overall. Additionally, the average particle sinking velocity above 200 meters of depth increased from roughly 17 meters per day to almost 100 meters per day, likely attributable to the increase in the particle size of the marine snow aggregates.

The results highlight how both abiotic and biotic processes affect how marine snow moves through the water column. That understanding could have implications for how scientists quantify the effects of the ocean’s biological pump within the planet’s carbon cycle, the researchers say. (Global Biogeochemical Cycles, https://doi.org/10.1029/2025GB008676, 2025)

—Madeline Reinsel, Science Writer

Citation: Reinsel, M. (2026), Marine snow grows faster and fluffier as it sinks, Eos, 107, https://doi.org/10.1029/2026EO260030. Published on 16 January 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.

Scientists have discovered there is more to Antarctica than meets the eye. A new map of the landscape beneath the frozen continent's ice sheet has revealed a previously hidden world of mountains, deep canyons and rugged hills in unprecedented detail.

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

Advancing our understanding of climate change and its impacts requires a multidisciplinary effort to generate, evaluate, and integrate reliable climate records at appropriate spatiotemporal scales. Reliable and traceable climate observations are essential for evidence-based climate governance.

Essential Climate Variables (ECVs) serve as the foundation for monitoring the Earth system. For instance, ECVs such as the Earth Radiation Budget and Total Solar Irradiance (TSI) provide critical information on energy exchanges within the Earth system, underpinning assessments of long-term variability and anthropogenic influences.

These variables are estimated from satellites, ground networks, and models, producing vast datasets whose usefulness depends not on size, but on quality, consistency, and careful integration. As measurement coverage is uneven, instruments differ in calibration, and techniques can yield conflicting results. Thus, transforming raw data into reliable information requires rigorous quality control and collaboration across scientific and technical disciplines.

International frameworks such as the WMO Integrated Global Observing System (WIGOS) set standards for measurement, documentation, uncertainty reporting, and open data sharing. These systems promote traceability and reliability—ensuring the ability to track how each data point was produced and processed—so that scientists can reproduce analyses and policymakers can trust the results. In addition, emerging approaches, including physics-informed Machine Learning (ML) and Deep Learning (DL), enable enhanced detection of patterns, anomaly identification, and quality control in large, heterogeneous datasets. Thereby they are strengthening the role of ECVs in monitoring system integrity.

Moreover, geodetic observations of sea-level rise, cryospheric changes, and solid Earth deformation illustrate the key role of multidisciplinary ECV analysis. By providing a holistic understanding of environmental change, these data streams are foundational for developing next-generation predictive tools, including Earth’s Digital Twin, to monitor global and local dynamics.

In this context, the Global Climate Observing System (GCOS) plays a key role by fostering global collaboration to develop interdisciplinary ECVs that are traceable and reliable. GCOS supports efforts to advance climate science by ensuring high-quality data, which is vital for informed climate action and adaptive policy development. Through innovation and interdisciplinary approaches, this framework enables more effective responses to the challenges posed by climate change.

This special collection serves as a venue for contributions that shed light on the role of continuous monitoring of ECVs, coupled with rigorous quality assurance, as a foundation for policy decisions, ultimately bridging the gap between technical observation and actionable climate governance. We especially welcome novel research that advances the methodologies required to demonstrate how robust, traceable data can empower society to build resilience against a changing climate. Contributions will include (but not be limited to) research into: best practices in observation, collection, and processing and curation of data. It can also include physics-informed machine and deep learning methods to identify relationships and feedback loops between atmosphere, hydrosphere, biosphere, and lithosphere, as well as evidence-based policies and remediation measures.

This is a joint special collection between Earth and Space Science, JGR: Machine and Computation, and Earth’s Future. Manuscripts can be submitted to any of these journals depending on their fit with each journal aims and scope. Submissions are now open and welcome until 7 March 2027.

—Jean-Philippe Montillet (Jean-Philippe.Montillet@pmodwrc.ch, 0000-0001-7439-7862), Physikalisch-Meteorologisches Observatorium Davos World Radiation Center, Switzerland; Graziella Caprarelli (Graziella.Caprarelli@usq.edu.au, 0000-0001-9578-3228), University of Southern Queensland, Australia;  Gaël Kermarrec (0000-0001-5986-5269), Leibniz Universitat Hannover, Germany; CK Shum (0000-0001-9378-4067), Ohio State University, United States; Ehsan Forootan (0000-0003-3055-041X), Aalborg University, Denmark; Jan Sedlacek (0000-0002-6742-9130), Physikalisch-Meteorologisches Observatorium Davos World Radiation Center, Switzerland; Elizabeth Weatherhead (0000-0002-9252-4228), University of Colorado at Boulder, United States; Orhan Akyilmaz (0000-0002-8499-2654), Istanbul Technical University, Turkey; Wolfgang Finsterle (0000-0002-6672-7523), Physikalisch-Meteorologisches Observatorium Davos World Radiation Center, Switzerland; Yu Zhang, Ohio University, United States; Enrico Camporeale (0000-0002-7862-6383), University of Colorado Boulder, United States; and Kelly K. Caylor (0000-0002-6466-6448), University of California, Santa Barbara, United States

Citation: Montillet, J-P., G. Caprarelli, G. Kermarrec, CK. Shum, E. Forootan, J. Sedlacek, E. Weatherhead, O. Akyilmaz, W. Finsterle, Y. Zhang, E. Camporeale, and K. K. Caylor (2026), Bridging the gap: transforming reliable climate data into climate policy, Eos, 107, https://doi.org/10.1029/2026EO265001. Published on 16 January 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.

Research reveals when and why ancient tropical seas transitioned from oxygen oases to marine dead zones, providing clues to the long-term evolution of oceanic environments.

There are concerns about the potential impact of an incipient landslide at Farwell Canyon on the Chilcotin River in British Columbia, Canada.

On 30 July 2024, a large landslide occurred on the Chilcotin River in British Columbia, Canada, blocking the flow. The scale of the landslide was massive – on the BC website about the event, it is estimated that the landslide was about 1,000 metres in length, 600 metres in width, and roughly 30 metres deep. There is a good Youtube video with footage of the landslide:-

And this image, from the BC Government, captures the landslide itself:-

The 30 July 2024 landslide on the Chilcotin River in Canada. Image from the BC Government.

The landslide breached and the lake drained on 5 August 2024.

In the aftermath of that landslide, geotechnical monitoring was established for the riverbanks, which has identified another site on the Chilcotin River that appears to be vulnerable to a landslide. A tension crack has developed at a site known as Snhaxalaus, located just downstream of the the Farwell Canyon Bridge (the bridge is at [51.82790, -122.56296].

The Tŝilhqot’in National Government has published this image of the site:-

The site of the incipient landslide near to Farwell Canyon Bridge on the Chilcotin River in Canada. Image from the Tŝilhqot’in National Government.

The tension crack, and the large displacements, are clearly evident.

The major concern at this site is the potential impact on Chilko salmon. Following 2024 landslide, an Emergency Salmon Task Force was established, led by the Tŝilhqot’in National Government but also working with the Williams Lake First Nation. To manage the threat posed by the incipient landslide on the Farwell Canyon, the Task Force is planning to undertake “a proactive slope stabilization plan that includes manual scaling and targeted trim blasting”, which seems like a reasonable approach.

However, national and/or provincial funding is not in place to undertake this work ahead of the salmon migration later this year, so the Tŝilhqot’in National Government is planning to fund the work itself. The costs are estimated to be in the range of CAN$2.5M – $3M. Tŝilhqot’in National Government is concerned that a failure at this site ahead of the salmon migration could cause devastating damage to the salmon populations on the Fraser River.

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.

When Floridians talk about extreme weather, hurricanes dominate the conversation. Each season brings updates on storm tracks, cone predictions and wind speeds, all in the hopes of predicting the unpredictable. But a quieter, more deceptive threat is already reshaping the way people live and work in the Sunshine State: extreme heat.

SummaryThe Geocenter Motion (GCM) time series captures periodic variations arising from diverse Earth system changes. This study pioneers the use of Successive Variational Mode Decomposition (SVMD) in GCM research, enabling the precise extraction and analysis of these meaningful geophysical signals. SVMD outperformed Singular Spectrum Analysis (SSA) by effectively isolating signals and minimizing interference from components with similar variance contributions. However, a high maximum penalty factor in SVMD may lead to noise-dominated Intrinsic Mode Functions (IMFs). To overcome this limitation, we propose an extraction criterion that utilizes the standard deviation of the correlation coefficient and mean kurtosis as thresholds. Validations with simulations and the real GCM time series demonstrate its superiority over traditional single- and dual-threshold criteria, effectively retaining valuable information while excluding most noise-dominated IMFs. This improved approach is further employed to explore the geophysical driving factors of key periodic variations in the GCM time series, focusing on the annual, semi-annual, 10.5-year, 451-day, ∼160-day, and ∼120-day periods. Multi-source GCM analyses combined with the fingerprint method reveal distinct contributions from the Antarctic and Greenland ice sheets, terrestrial water storage, continental glaciers, and atmosphere-ocean interactions to different periodic signals. This study provides a robust methodology for decomposing GCM and attributing its variations to underlying Earth system changes, advancing our understanding and interpretation of global mass redistribution.

SummaryFull waveform inversion (FWI) is a popular method for subsurface parameter estimation. Despite its effectiveness in building high-resolution velocity models, the quality of the inversion result is significantly dependent on a fairly accurate, smooth initial model, which is often challenging to build. To weaken the influence related to the inaccurate initial model, we propose a deep learning (DL) matching-based FWI framework, namely DLM-FWI, where multiple convolution neural networks (CNNs) are used to construct an adaptive matching filter to better pinpoint the discrepancies between the synthetic and observed data. With the help of the CNN-based matching filter, the synthetic data will be regularized first, leading to intermediate data, and the model update will be conducted by minimizing the misfit between the intermediate and the observed data for improved data-fitting. More importantly, we integrate the whole inversion process into an automatic differentiation (AD) framework, simplifying the implementation of classic FWI. We apply the proposed DLM-FWI method to both synthetic and field datasets to validate its effectiveness. The results demonstrate that compared with classic FWI, DLM-FWI performs better in subsurface model reconstruction when the initial model is far from the global minimum.

Global change—a term that encompasses climate change and phenomena such as changes in land use or environmental pollution—is increasingly putting ecosystems around the world under pressure. Urban soils in particular are susceptible to stressors like heat, drought, road salt, nitrogen deposition, surfactants, and microplastics.

Multi-ignition wildfires are not overly common. But when individual fires do converge, the consequences can be catastrophic. The largest fire on record in California, the 2020 August Complex fire, grew from the coalescence of 10 separate ignitions.

By tracking swarms of very small earthquakes, seismologists are getting a new picture of the complex region where the San Andreas fault meets the Cascadia subduction zone, an area that could give rise to devastating major earthquakes.

Environmental phenomena and their consequences can disrupt social structures and destabilize political systems. An interdisciplinary research team demonstrated this using the example of the late Tang dynasty in medieval China.

Environmental research in the tropics is heavily skewed, according to a comprehensive study led by Umeå University. Humid lowland forest ecosystems receive a disproportionate amount of attention, while colder and drier regions that are more affected by climate change are severely underrepresented.