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

Source: Space Weather

TianQin is a geocentric space-borne gravitational wave detector, which is proposed to detect the gravitational wave by measuring tiny displacements using inter-satellite laser interferometry. However, the space surrounding the orbit and laser links of TianQin is not a vacuum—but filled with plasma, which can bend the laser links and induce pointing accuracy noise in the gravitational wave detection.

Based on a global magnetohydrodynamic model, Zhou et al. [2026] use a ray-tracing method to obtain the laser deflection caused by laser propagation through plasma, and to evaluate the pointing accuracy noise.  The result shows that the laser deflection effect caused by large-scale space plasma distribution under quiet to moderate space weather conditions does not represent a fundamental risk to the TianQin mission. However, during severe space weather events, the laser propagation effect could become a considerable noise in the gravitational wave detection.

This work establishes a connection between space weather and gravitational wave detection. Furthermore, this work raises awareness of the impact of space weather on other high-precision electromagnetic wave measurements in space.

Citation: Zhou, S. W, Su, W., Zhou, S. Y., Li, C. F., & Zhang, J. X. (2026). The pointing error due to laser propagation in space plasma for TianQin gravitational wave detection. Space Weather, 24, e2025SW004784. https://doi.org/10.1029/2025SW004784

—Jiuhou Lei, Editor, Space Weather

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’ Highlights are summaries of recent papers by AGU’s journal editors.

Source: AGU Advances

Solar eruptions can trigger geomagnetic storms that disrupt satellites, GPS, and power grids, affecting daily activities and technology. Therefore, it is extremely important to understand these storms in order to mitigate their impact. Previous studies mainly focused on interplanetary conditions.

Ghag et al. [2026] investigate the interaction between solar ultraviolet light (EUV) during storms and the Earth magnetic field, taking into account its misalignment and offset with respect to the Earth’s rotational axis, which depend on time. Such misalignment and offset induce variations in EUV exposure in turn influencing the ionosphere and its interaction with the magnetosphere.

The study applies the Multiscale Atmosphere-Geospace Environment (MAGE), a physics based fully coupled whole geospace model. The causal relationship between storm timing and storm effect is explored revealing insights on our capability to predict storm impact based on the time dependent Earth system state.

Citation: Ghag, K., Lotko, W., Pham, K., Lin, D., Merkin, V., Raghav, A., & Wiltberger, M. (2026). Universal time influence on stormtime magnetosphere ionosphere coupling. AGU Advances, 7, e2025AV002071. https://doi.org/10.1029/2025AV002071

—Alberto Montanari, Editor-in-Chief, 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.

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.

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
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

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

Source: Journal of Advances in Modeling Earth Systems

The purpose of atmospheric data assimilation is to obtain a 3-dimensional gridded representation of the fields of the atmospheric state variables (temperature, wind, pressure, etc.) for a specific time based on atmospheric observations. The product of data assimilation, called analysis, can be used to prepare weather maps and to start model-based weather forecasts. Analyses collected over a long period of time can also be used for research and to monitor variability and changes in the climate.

The main challenges of data assimilation are that observations are not collocated with the grid-points of the analysis, and most observations do not observe the variables of interest directly and have errors. For example, satellite-based observations, which form the bulk of the operationally assimilated observations, measure the intensity of electro-magnetic waves at the top of the atmosphere; a physical quantity that depends on the atmospheric state in highly complicated ways. The background-error covariance matrix is a key component of a data assimilation system, responsible for spreading information from observations to the unobserved locations and state variables. A good estimate of this matrix is essential to produce analyses in which the fields of the state variables are realistic and consistent with each other. Obtaining such an estimate is particularly challenging for tropical locations, where physics-based knowledge does not lead to a straightforward practical formulation.

In a new study, Melinc et al. [2026] propose a novel machine learning-based (ML-based) approach to define a background-error matrix that is equally effective in the midlatitudes and tropics. This approach takes advantage of the power of ML to learn quantitative relationships between different state variables at different locations-relationships that are either not known, or cannot be easily used for the formulation of a background-error matrix based on physics-based knowledge.

Citation: Melinc, B., Perkan, U., & Zaplotnik, Ž. (2026). A unified neural background-error covariance model for midlatitude and tropical atmospheric data assimilation. Journal of Advances in Modeling Earth Systems, 18, e2025MS005360. https://doi.org/10.1029/2025MS005360

—Istvan Szunyogh, Associate Editor, JAMES

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’ Highlights are summaries of recent papers by AGU’s journal editors.

Source: Journal of Geophysical Research: Earth Surface

Antarctica’s snow and ice surfaces play a key role in how the continent exchanges heat and moisture with the atmosphere. A key property controlling this exchange is aerodynamic roughness length (z_o), which measures how “bumpy” the surface is. Rougher surfaces, such as snow sastrugi (wind-formed ridges and grooves), interact more strongly with the air above, affecting snow movement, melting, and local environmental conditions. Despite its importance, z_o is often treated as a single, constant value over large areas in Earth system models because it is difficult to measure.

Zheng et al. [2026] use a multi-temporal Unmanned Aerial Vehicle (UAV) oblique photogrammetry to map fine scale z_o variability at Qinling Station in East Antarctica. The results show that z_o can vary substantially depending on surface type, measurement scale, model choice, and meteorological conditions. The complex response of surface microtopography to meteorological events is a noteworthy new finding. For example, in snow sastrugi areas, z_o can vary by an order of magnitude over time, increasing after snowfall and decreasing under strong winds. These findings highlight that capturing fine-scale surface roughness is essential for accurately modeling snow–atmosphere interactions in Antarctica and could help improve current weather and climate models for polar regions.

Citation: Zheng, Z., Zheng, L., Wang, K., Clow, G. D., & Cheng, X. (2026). UAV oblique imagery reveals order-of-magnitude changes in snow aerodynamic roughness length under shifting meteorological regimes at Qinling Station, East Antarctica. Journal of Geophysical Research: Earth Surface, 131, e2025JF008781. https://doi.org/10.1029/2025JF008781

   —Elizabeth Orr, Associate Editor, JGR: Earth Surface

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’ Highlights are summaries of recent papers by AGU’s journal editors.

Source: Community Science

Citizen science continues to spread across the world. It is becoming an acceptable and reliable practice to monitor and report on local conditions. Yet, it must adapt to local conditions and constraints – such as the profile of participants, their level of education, or the time that is available for them. So, how does citizen science adapt to Low- and Middle-Income Countries (LMIC)?

In Ashepet et al. [2026], we learn from the ATRAP (Action Towards Reducing snail-borne Parasitic diseases) project, which focuses on the monitoring of snail-borne disease in Uganda and the Democratic Republic of Congo (DRC). The researchers show how citizen science requires consideration such as material and social benefits for the participants, and how social structure and practices need to be taken into account. The paper also challenges the universality of the European Citizen Science Association (ECSA) 10 principles of citizen science. 

Citation: Ashepet, M. G., Mulmi, J., Michellier, C., Jacobs, L., Pype, K., & Huyse, T. (2026). Citizen science principles in practice: Lessons from Uganda and the democratic Republic of Congo. Community Science, 5, e2025CSJ000149. https://doi.org/10.1029/2025CSJ000149

—Muki Haklay, Editor, Community Science

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’ Highlights are summaries of recent papers by AGU’s journal editors.

Source: Water Resources Research

In the March 2026 issue of Water Resources Research, Zhang et al. [2026] interrogate conceptual hydrologic models’ ability to capture prolonged drought dynamics. The Australian Millennium drought serves as an example in the study. The results are quite sobering because the vast majority of more than 40 models fail. Unfortunately, calibration doesn’t generally help either and might result in massive overfitting. In essence, conceptual models miss deep aquifer storage components and associated hydrodynamic processes leading to a lack of time scales important in drought modeling. The study is a constructive reminder that model parsimony is not necessarily a good thing and that detailed representation of complex physical processes is part of hydrologic sciences.

Citation: Zhang, Z., Fowler, K., & Peel, M. (2026). Can conceptual rainfall-runoff models capture multi-annual storage dynamics? Water Resources Research, 62, e2025WR042226. https://doi.org/10.1029/2025WR042226

—Stefan Kollet, Editor, Water Resources Research

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’ Highlights are summaries of recent papers by AGU’s journal editors.

Source: AGU Advances

Aerosols are tiny particles suspended in the air. They can cool the climate by making clouds brighter and longer-lasting. Scientists rely on satellite observations to measure the aerosol-cloud interaction, but distinguishing human impacts from natural weather patterns remains a challenge.

Christensen et al. [2026] reveal that the Amazon River itself creates cloud patterns that mimic the signatures of pollution. Using 15 years of satellite data, researchers found that the temperature difference between the cool river and the warm land drives a local “river breeze” circulation. This natural process creates clouds with smaller and more numerous water droplets, which exhibit very similar features that satellites look for to identify pollution. Consequently, clean clouds over the river can appear polluted in satellite datasets. These findings highlight the critical need to account for local geography and natural weather patterns to accurately assess how human activities are influencing Earth’s climate.

Citation: Christensen, M. W., Varble, A. C., Tai, S.-L., Wind, G., Meyer, K., Holz, R., et al. (2026). The Amazon River-breeze circulation limits detection of aerosol-cloud interactions in warm clouds. AGU Advances, 7, e2025AV002188. https://doi.org/10.1029/2025AV002188 

—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.

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

Source: AGU Advances

The evolution of rivers that split into multiple channels is a scientific challenge in terms of modeling and prediction. On the other hand, these rivers are widespread and play a key role for ecosystems’ life, groundwater recharge, and therefore, water security. They are also extremely sensitive to hydroclimatic changes, leading to shifts in precipitation, erosion and sediment transport.

Zhao et al. [2026] investigate the drivers of river evolution for 97 multithread river reaches worldwide, spanning diverse climates and morphologies. The study reveals the key role of intermittency for river evolution. In particular, higher flow intermittency could lead to more even flow partitioning among threads, therefore impacting hydrology and ecosystems. With flow variability increasing after climate change, rivers are likely to increase their thread count, thus impacting livelihoods and ecosystems.

Citation: Zhao, F., Ganti, V., Chadwick, A., Greenberg, E., McLeod, J., Liu, Y., et al. (2026). Global hydroclimatic controls on multithread River dynamics. AGU Advances, 7, e2025AV002166. https://doi.org/10.1029/2025AV002166

—Alberto Montanari, Editor-in-Chief, 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.

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

Source: Journal of Geophysical Research: Atmospheres

The 2018 Camp Fire was the deadliest and most destructive wildfire in California history. The Camp Fire spread extremely rapidly, driven by strong winds and dry fuels, but also by organized long-range spotting, i.e. lofting and downwind fallout of burning embers to ignite new fires.

Using operational Doppler radar and satellite observations, Lareau [2026] provides the first high resolution depiction of spotting behavior during an extreme wildfire. Observations show that spot fire events for the Camp Fire occurred 5-10 kilometers ahead of the fire front, quickly merging into new fire lines. Spot fires are not random but aligned within coherent fallout zones that are shaped by plume dynamics and background winds. These results show that operational weather radar can identify lofting and fallout regions in real time, providing a new way to anticipate spotting-driven fire spread and improve early warnings for fast-moving wildfires.

Citation: Lareau, N. P. (2026). Plume-coupled long-range spotting drove the explosive spread of the 2018 Camp Fire. Journal of Geophysical Research: Atmospheres, 131, e2025JD045798. https://doi.org/10.1029/2025JD045798

—William Randel, Editor, JGR: Atmospheres

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’ Highlights are summaries of recent papers by AGU’s journal editors.

Source: Journal of Geophysical Research: Earth Surface

Sediment transport shapes the Earth surface in different ways, by forming desert dunes and by sculpting the topography of rivers, but the physics of sediment transport initiation is still incompletely understood. For decades, models have generally assumed two basic entrainment mechanisms: a grain resting on the sediment bed is either lifted directly by fluid forces, or it is emitted from the soil indirectly, as product of a granular splash caused by the heavy impact of another grain.

However, recent breakthroughs in grain-based simulations and high-speed visualization have been offering a much clearer look at the processes that trigger grain motion. Insights from these recent advances have revealed a rather broad spectrum of indirect particle-particle and particle-fluid interactions driving entrainment, including the rearrangement of surface grains after splash and changes in near‐bed flow structure due to moving grains. These interactions exert non-local influences on transport thresholds, giving rise to a dynamic process known as collective particle entrainment—a mechanism that remains poorly understood at a fundamental level.

In a new study, Chartrand [2026] shows that collective particle entrainment is size-dependent: large grains interact primarily with their peers, while smaller grains are mobilized by both large and similar-sized particles. This distinction leads to divergent transport signatures, with a new stochastic model predicting temporally correlated motion for small grains and uncorrelated, white-noise entrainment statistics for larger particles.

Although theoretical modeling will be required to shed further light on the physics of collective entrainment, the author’s study is a step toward a quantitative model of sediment transport from a probabilistic perspective. Looking ahead, Chartrand’s ideas could now be extended to other environments, potentially transforming our understanding of entrainment in other contexts such as wind-blown transport and extraterrestrial atmospheric processes.

Citation: Chartrand, S. M. (2026). Collective particle entrainment explored with experimental data and coupled transfer functions. Journal of Geophysical Research: Earth Surface, 131, e2025JF008657. https://doi.org/10.1029/2025JF008657

—Eric Parteli, Associate Editor, JGR: Earth Surface

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.