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A recent article in Reviews of Geophysics explores land subsidence drivers, rates, and impacts across the globe. It also discusses the need for improved process representations and the inclusion of the interplay among land subsidence and climatic extremes, including their effects in models and risk assessments. Here, we asked the lead author to explain the concept of land subsidence, its impacts, and future directions needed for improved mitigation.

What is land subsidence? 

Land subsidence (LS) refers to the relative sinking or lowering of the Earth’s land surface. LS is a pressing global issue that warrants action since subsidence can adversely impact infrastructure, humans, and the environment across various landscapes and climates (Figure 1). It may be driven by one or more natural processes and/or human activities that compound to cause localized or expansive ground deformation. Differential LS causes structures and roadways to crack and buckle. LS can also reduce the water storage capacity of aquifers. Notably, LS can be recoverable (e.g., natural variations in groundwater levels) or permanent (e.g., overdraft causing irreversible compaction).  

Figure 1. Reported LS rates and drivers around the world based on literature. (a) Map of primary LS drivers (colors) indicating mean (circles) and maximum (triangles) rates (shape sizes). A shared color scheme (shown in (b)) demarcates the main causes of LS in (a) and (b). (b) 50 largest mean LS rates for global locations (numbered along x-axis and listed above). LS rates are often nonlinear, temporally dependent, and occur at various time scales. Rates shown were not all observed or estimated over the same time period. Credit: Huning et al. [2024], Figure 1.

Why is it important to understand and monitor land subsidence? 

Various LS drivers and physical processes exist and interact with one another (Figure 1). LS is often closely related to natural resources demand, which increases with growing urbanization and megacities. The proximity of LS to critical infrastructure like water conveyance, transportation, and utility systems is a significant concern since LS could cause catastrophic lifeline failures, outages, and/or loss of life. Also, feedbacks between climatic extremes (e.g., droughts, floods, wildfires, heatwaves) and LS impacts exist, but are not fully understood.

Although a chronic hazard, LS may initially go unnoticed as sinking typically occurs slowly. This influences perceived risk and contributes to reactive policies, regulations, and mitigation steps targeting LS and its implications rather than proactive measures. Furthermore, the compounding effects of extreme events and their impacts can exacerbate LS. More pronounced interactions are likely with projected rises in climate extremes.

How do scientists monitor and measure land subsidence across the globe? 

Scientists use various techniques and technologies to measure LS, including ground-based surveys, subsurface instrumentation, and satellite-based observations. Satellite-based Synthetic Aperture Radar (SAR) has revolutionized LS monitoring and mapping. It is an active remote sensing system that emits microwave pulses and receives echoes. Such systems can operate under various conditions (e.g., day and night, in cloudy skies) and produce high-resolution imagery. With SAR-based information, scientists can infer surface deformation by computing phase differences between SAR snapshots over a region using techniques like interferometric SAR (InSAR). SAR-based observations commonly inform impact assessments for agriculture, structural health, and resource management.

What are the major natural and anthropogenic drivers of land subsidence? 

Naturally-occurring processes and human activities can independently drive LS or enhance existing LS rates (Figure 2). Some examples of natural drivers of LS include: natural consolidation, volcanic or tectonic activity, seasonal groundwater level variations, and soil organic material decomposition. Extraction of natural resources (e.g., fossil fuels, groundwater), removal of wetlands and peatlands, and loading from rapid urbanization serve as examples of human-related activities contributing to LS. Natural resource extraction is a leading anthropogenic driver of LS (Figure 1), which often rises with increasing population. Also, extreme events such as wildfires or heatwaves can trigger LS in permafrost areas by thawing the permafrost layer, altering the soil structure, and releasing greenhouse gases that accelerate warming.

Figure 2. Schematic illustrating feedbacks and effects of land subsidence, extreme events, and human activities. Credit: Huning et al. [2024], Figure 3.

How is land subsidence projected to change in the future? 

Estimating future LS rates is challenging. Projecting human activities driving LS and the effectiveness of restoration and mitigation efforts is complicated, uncertain, and variable. LS projections also depend on other factors (e.g., infrastructure investments, land use-land cover changes). They are further complicated by uncertain projected hydrologic variables like precipitation. Yet, more people are expected to be exposed to LS with greater economic losses anticipated in the future.

Sea level rise (SLR), rising temperatures, and extreme events often compound LS. Subsiding coastal areas and deltas face higher inundation risk from the compounding effect of SLR. Extreme events and LS impacts are expected to increasingly affect one another (Figures 2-3) as extremes (e.g., drought) intensify with warming. Amidst drought, groundwater levels drop through decreased recharge and increased pumping, often leading to soil compaction and LS. As soils dry and crack, heightened microbial processes decompose soil organic matter and release carbon. Such processes can enhance warming while triggering LS and feedbacks. As temperatures rise, permafrost thaw-driven LS is also expected to expand, increasing the infrastructure at risk for damage and failure.

Figure 3. Example feedback loops involving land subsidence, climatic trends, extreme events, infrastructure, and cascading hazards. (a) Peatland‐carbon, (b) permafrost‐carbon, and (c) salinization‐subsidence feedbacks and (d) infrastructure‐subsidence, (e) flood‐subsidence, and (f) drought‐subsidence cascading hazards. Black (orange) arrows denote a positive feedback (strengthening of impacts). Credit: Huning et al. [2024], Figure 4.

What additional research, data, or modeling is needed to help track and mitigate land subsidence and its impacts? 

Integrated models incorporating multiple LS drivers and processes are necessary for better estimating LS rates, extent, and ramifications at the spatiotemporal resolutions essential for mitigation, adaptation, and policy. Additional data and research are needed to understand the interplay of extreme events, infrastructure, climatic trends, and human activities with LS dynamics and effects (Figure 3), and inform LS mitigation efforts.

Improved climate modeling, management practices, and risk assessments require better representations of LS feedbacks, carbon emissions, and LS processes. Such advancements necessitate accurate, longer, and spatial observations and analyses with improved process understandings. Global adoption of consistent monitoring and reporting frameworks will also support such efforts by leading to new insight into LS observations and regions at-risk for LS, LS-enhanced flooding, etc. Interdisciplinary efforts will help transform science into action focused on LS hazard and risk mitigation.

—Laurie S. Huning (laurie.huning@csulb.edu, 0000-0002-0296-4255), California State University, Long Beach, United States

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: Huning, L. S. (2025), Rising concerns of climate extremes and land subsidence impacts, Eos, 106, https://doi.org/10.1029/2025EO255019. Published on 9 June 2025. 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 © 2025. 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.

Tsunamis pose a risk to the entire California coast. But should a major one strike, how bad could it be?

Author(s): D. Turnbull, R. K. Follett, M. Sherlock, D. J. Strozzi, J. Katz, D. Cao, N. R. Shaffer, K. Aytekin, D. H. Edgell, L. Stanton, and D. H. Froula

A spherical-implosion platform diagnosed with the “beamlets” scattered-light detector provides high sensitivity to the impact of plasma screening on inverse bremsstrahlung absorption. Contrary to the more restrictive screening length suggested previously [D. Turnbull et al., Phys. Rev. Lett. 130, 14…

[Phys. Rev. E 111, 065206] Published Mon Jun 09, 2025

A new paper in the journal Landslides has presented a review of a large landslide that killed 29 people in Sichuan Province.

On 8 February 2025, a large rock avalanche occurred in Junlian County in Sichuan Province, China. I wrote about this event, now known as the Junlian rock avalanche, at the time. With remarkable and commendable pace, Bo Zhao and colleagues have published an initial review of the event (Zhao et al. 2025) in the journal Landslides. Whilst the paper is behind a paywall, this link should allow readers to access the full text.

The landslide is located at [27.99885, 104.60801]. The Google Earth image below shows the site in 2020 – the marker is on the source area of the Junlian rock avalanche:-

Google Earth image of the site of the 8 February 2025 Junlian rock avalanche, China.

The image below, published by Xinhua, shows the aftermath of the landslide:-

The aftermath of the 8 February 2025 Junlian rock avalanche in Sichuan, China. Image by Xinhua.

Zhao et al. (2025) have determined the key statistics for this landslide. The initial failure was 370,000 m3, increasing to 600,000 m3 through entrainment. The landslide had a runout distance of 1,180 metres and a vertical elevation change of 440 m, giving a landslide mobility index of 0.37. This is a typical value for a rock avalanche of this volume.

Zhao et al. (2025) show that the initial failure was structurally controlled, which is no surprise. It occurred in a Triassic interbedded sandstone and mudstone formation. They estimate that the average velocity was 19.3 m/second.

The authors consider in some detail the triggering event. The site experienced 10 days of low intensity rainfall prior to the failure. Zhao et al. (2025) suggest that this led to the build up of pore water pressure, initiating the failure. Total rainfall in the month proceeding the collapse was in the order of 85 mm. This rainfall seems somewhat unexceptional, suggesting to me that a progressive failure mechanism was in play.

The Junlian rock avalanche killed 29 people and left two people injured. It is a fascinating example of a major failure with high consequences in a remote mountainous area. Anticipating such events remains a major challenge in landsldie research. Many thanks to the authors for providing such a rapid description of this event.

Reference

Zhao, B., Zhang, Q., Wang, L. et al. 2025. Preliminary analysis of failure characteristics of the 2025 Junlian rock avalanche, China. Landslides. https://doi.org/10.1007/s10346-025-02556-1.

Return to The Landslide Blog homepage Text © 2023. 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.

AbstractGeodetic observations of postseismic deformation due to afterslip and viscoelastic relaxation can be used to infer fault and lithosphere rheologies by combining the observations with mechanical models of postseismic processes. However, estimating the spatial distributions of rheological parameters remains challenging because it requires solving a nonlinear inverse problem with a high-dimensional parameter space and potentially computationally expensive forward model. Here we introduce an inversion method to estimate spatially varying fault and lithospheric rheological parameters in a mechanical model of postseismic deformation using geodetic time series. The forward model combines afterslip and viscoelastic relaxation governed by a velocity-strengthening frictional rheology and a power-law Burgers rheology, respectively, and incorporates the mechanical coupling between coseismic slip, afterslip, and viscoelastic relaxation. The inversion method estimates spatially varying fault frictional parameters, viscoelastic constitutive parameters, and coseismic stress change. We formulate the inverse problem in a Bayesian framework to quantify the uncertainties of the estimated parameters. To solve this problem with reasonable computational costs, we develop an algorithm to estimate the mean and covariance matrix of the posterior probability distribution based on an ensemble Kalman filter. We validate our method through numerical tests using a two-dimensional forward model and synthetic postseismic GNSS time series. The test results suggest that our method can estimate the spatially varying rheological parameters and their uncertainties reasonably well with tolerable computational costs. Our method can also recover spatially and temporally varying afterslip, viscous strain, and effective viscosities and can distinguish the contributions of afterslip and viscoelastic relaxation to observed postseismic deformation.

AbstractWe present a novel technique for the characterization of small-scale absorption and scattering properties from cross-correlation functions (CCFs) of seismic ambient noise. We use a continuous data set recorded over four years at the Piton de la Fournaise volcano. Attenuation properties are estimated in the frequency range from 0.5 to 4 Hz, by comparing energy envelopes from CCFs with those from the radiative transfer theory (RTT) and the diffusion approximation. Our technique exploits the different propagation regimes observed at long and short propagation distances, which allows us to quantify attenuation properties in two stages: firstly, we measure absorption from short propagation distances including auto-correlation functions (source-receiver collocated case) to profit from the long coda durations. This set of estimates also allows to observe spatial variation of absorption either from RTT or the diffusion approximation. Once absorption is estimated, we proceed to characterize scattering from long propagation distances where scattering effects dominate absorption. Our inversion strategy to characterize scattering is called the ’ball-diff’ ratio because we propose to use the ratio of the integrated energies contained in the ballistic and early diffuse regimes. This technique can considerably reduce the effect of the uneven distribution of noise sources. Finally, in order to validate our method, the scattering and absorption properties estimated from CCFs of seismic noise are compared with those from earthquake data, for which we used magnitudes between 1.5 and 2.5. Good agreement was found between the estimates of these two approaches.

AbstractDetermining when and where the next big earthquake will occur is a fundamental challenge in earthquake forecasting. Although it is reasonable to assume that the next major earthquake will occur in regions where stress has been increased by previous events, the most common and reliable earthquake forecasting models assume that the magnitude of next earthquakes is independent from what happen before and, implicitly, from the stress state. In this study, we investigate the correlation between stress distribution and the occurrence of large earthquakes using a realistic physical model. Our findings reveal that the next big earthquake is more likely to occur on the periphery of previous large earthquakes, where stress has accumulated but not yet been relaxed. Additionally, we explore how stress redistribution influences the magnitude distribution of aftershocks. These results can inform the introduction of correlations between large earthquakes in existing seismic forecasting models, potentially enhancing their accuracy and reliability.

AbstractIt is shown that the SPOCK equation of state is equivalent to the Variable Polytrope Index equation of state.

SummaryA brief reply to the comment by Ruedas.

With careful planning and a little luck, researchers found a surprising upside to hurricanes after a Category 4 storm disrupted their expedition off the coast of Mexico.

Methane emissions from Canada's non-producing oil and gas wells appear to be seven times higher than government estimates, according to a new study led by researchers at McGill University. The findings spotlight a major gap in the country's official greenhouse gas inventory and raise urgent questions about how methane leaks are monitored, reported and managed.

Antarctica could see a doubling of extreme weather events—such as atmospheric rivers—by 2100, with implications for future sea level rise.

Deep sea sediments contain treasure troves of information about marine ecosystems and past climate scenarios, yet remain understudied clues into Earth's environmental future, according to researchers.

Researchers have developed a laboratory earthquake model that connects the microscopic real contact area between fault surfaces to the possibility of earthquake occurrences. Published in the Proceedings of the National Academy of Sciences, this breakthrough demonstrates the connection between microscopic friction and earthquakes, offering new insights into earthquake mechanics and potential prediction.

Dust particles thrown up from deserts such as the Sahara and Gobi are playing a previously unknown role in air pollution, a new study has found.

Climate change is reshaping the global water cycle, disrupting rainfall patterns and putting growing pressure on cities and ecosystems. Some regions are grappling with heavier rainfall and flooding, while others face prolonged droughts that threaten public health, disrupt economies and increase the risk of political instability. In one recent example, a years-long drought between 2015 and 2020 brought Cape Town, South Africa, to the brink of running out of water—a moment officials dubbed "Day Zero."

On 10–11 May 2024, the strongest solar storm since 2003 hit Earth. The storm caused spectacular aurorae around the world, including as far south as Kansas in the midwestern United States. Unfortunately, it also had negative effects, such as days-long disruptions in GPS signals needed by farm tractors that, in turn, caused delays in planting operations at a critical time in the spring.

Solar storms, which throw torrents of protons, neutrons, and other particles at our planet, have had severe effects in decades past. A massive storm in May 1967, for example, significantly disrupted military communications (and ultimately led the United States to strengthen its space weather capacity) [Knipp et al., 2016]. Another, in March 1989, disabled power grids, hitting Quebec, Canada, especially hard [Boteler, 2019].

The biggest recorded modern event took place in February 1956. Were it to be repeated today, such an event could disrupt aircraft electronics and expose passengers to substantially elevated radiation doses.

The largest known solar event in history, 50–100 times larger than the one that happened in 1956, occurred in 774 CE [Miyake et al., 2012]. An event on par with the 774 storm is considered a worst-case scenario for modern aviation [Mishev et al., 2023].

With the 11-year solar cycle approaching its maximum in 2025, we are in a time of heightened potential for such events to disrupt daily life.

Fortunately, technology for observing solar storms and the particle showers they rain down on Earth has developed significantly over the past several decades. Both ground-based and satellite observations are critical for measuring solar storms and their effects [National Academies of Sciences, Engineering, and Medicine, 2024] and for generating space weather forecasts (e.g., by NOAA’s Space Weather Prediction Center (SWPC)). The global aviation sector, for example, uses these forecasts to predict solar radiation storm warning levels and radiation dosage levels to help keep flights safe.

The small number of high-energy neutron monitoring stations used to observe the effects of solar events at Earth’s surface limits data availability and thus the accuracy and spatial resolution of forecasts.

Good predictions rely on the availability of high-quality and comprehensive data. However, the small number of high-energy neutron monitoring stations currently used to observe the effects of solar events at Earth’s surface limits data availability and thus the accuracy and spatial resolution of forecasts. But solutions are within reach.

In addition to space weather scientists, hydrologists use data from these monitoring stations, albeit for a different purpose: They rely on the high-energy neutron detections to calibrate the low-energy neutron detectors they use as one way to collect snow cover and soil moisture measurements that are important for hydrological modeling and agricultural applications. Recent studies showed that the larger networks of low-energy neutron detectors used by hydrologists can supplement and effectively increase the coverage of the smaller network of high-energy neutron monitors [Baird, 2024]. Now, scientists are devising a strategy to combine forces for their mutual benefit.

Wanted: Better Observational Capabilities

Massive lead-lined neutron monitors (NMs) are typically used to monitor the arrival of cosmic ray particles at Earth’s surface. These particles include high-energy secondary neutrons (carrying energies of ~50–100 megaelectron volts) that are generated by collisions of primary solar and galactic cosmic rays with other particles in the atmosphere, a process that can be reconstructed using NM data and numerical models [Mishev et al., 2014].

This 18-tube neutron monitor is housed in a Quonset hut on the campus of the University of New Hampshire in Durham. Credit: James Ryan, University of New Hampshire

Satellites, including those in the GOES (Geostationary Operational Environmental Satellite) system, also provide operational data about primary cosmic rays in real time, but they cannot resolve particle energies in the detail required for estimating radiation doses affecting aviation or for modeling solar particle energy spectra [National Academies of Sciences, Engineering, and Medicine, 2024].

A global network of NMs, each run by different universities or other entities, has been in operation for the past 7 decades [Väisänen et al., 2021]. Unfortunately, today, only 20 NM sites around the globe provide real-time data; another roughly 30 NMs have been shut down because of a lack of long-term funding to maintain them. Geopolitical factors and closed data policies in some parts of the world additionally limit data quality and access internationally.

The U.S. Senate’s 2020 Space Weather Research and Forecasting Act emphasized the need for better observational capabilities to address this crisis of critical infrastructure. The 2020 PROSWIFT Act and the most recent National Academies’ solar and space physics decadal survey [National Academies of Sciences, Engineering, and Medicine, 2024] further underscored the challenges and need for supporting long-term operational NM networks.

Hydrologists Have Their Own Networks

Hydrologists have, in the past 15 years, deployed networks of detectors similar to neutron monitors (NMs) to measure snow and soil moisture.

Applying methods developed beginning several decades ago [e.g., Kodama et al., 1979], hydrologists have, in the past 15 years, deployed networks of detectors similar to NMs to measure snow and soil moisture [Zreda et al., 2012]. These cosmic ray neutron sensors (CRNSs) are, however, much smaller than NMs, and they are sensitive to much lower neutron energies (~0.025 to 100 kiloelectron volts).

At these lower energies, the number of detected neutrons depends not only on incoming secondary cosmic rays but also on the abundance of hydrogen in the surrounding environment (e.g., in the form of snow or soil moisture). In soil, for example, cosmic ray neutrons collide with hydrogen atoms, lose energy in the process, and become thermalized (i.e., they slow down). CRNSs are designed to count these water-sensitive neutrons.

The sensors can measure these low-energy neutrons within a roughly 20-hectare circular area and up to about 30 centimeters above the ground surface, an extraordinarily large volume relative to their size. Figure 1 shows how example CRNS measurements of neutron counts and soil water content from central Nebraska clearly respond to rainfall, as measured by the local Mesonet station, and match potential evapotranspiration data well.

Fig. 1. Neutron counts (corrected for variations in air pressure, water vapor, and high-energy neutron intensity, top left) and estimates of soil water content (bottom left) from 4 April to 2 May 2021 collected from a cosmic ray neutron sensor (CRNS) in central Nebraska are shown. Rainfall data from a local Nebraska Mesonet station (top right) and potential evapotranspiration (ET) data (bottom right) over the same period match the soil water content changes measured by the CRNS.

Area-averaged estimates of snow and soil moisture like this match scales relevant for hydrological modeling and agricultural management (e.g., irrigation and fertilizer application, crop yield prediction), providing a big advantage compared with estimates from point-scale measurements, given the high spatial variability that naturally exists from one meter to another. CRNS detectors offer other benefits as well. Their measurements, collected roughly hourly, are nondestructive; they have extremely low maintenance costs; and they can be deployed outdoors for long-term environmental monitoring.

Today, more than 300 CRNS instruments are operating across all seven continents, with networks in Australia, China, Europe, India, South Africa, the United Kingdom, and the United States. These networks have led to exciting advances in hydrology.

A CRNS measures soil moisture and snow water equivalent at a study site in eastern Nebraska. Graduate students Sophia Becker and Tanessa Morris are collecting soil samples for calibration. Credit: Trenton Franz, University of Nebraska–Lincoln

For example, CRNSs have been shown to be excellent sources of ground validation data for remote sensing soil moisture data products like SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity) that support weather and agricultural forecasting efforts, among other applications [Montzka et al., 2017]. CRNS data have also been shown to significantly improve predictions of streamflow by catchment models by improving estimates of near-surface water storage [Dimitrova-Petrova et al., 2020]. Mobile CRNSs have also been deployed on commuter trains in Europe, providing soil moisture and snow observations across unprecedented scales [Schrön et al., 2021].

Despite their clear utility, CRNS networks, like the global NM network, often lack long-term funding. Moreover, in the United States, no single federal agency is mandated to monitor soil moisture, a void that hinders the development of a national coordinated soil moisture monitoring network.

An Exciting Opportunity

The CRNS research community has been highly dependent on the NM network because real-time reference data are required to correct CRNS measurements for variations in incoming cosmic radiation. In a recent advance bridging the two neutron monitoring communities, Baird [2024] showed that potential benefits also extend in the other direction.

He used 50 CRNS stations in the United Kingdom to investigate whether they can inform space weather monitoring, concluding that they “can identify persistent space weather periodicities, transient space weather periodicities, and transient aperiodic space weather signals” and that these capabilities are “largely unaffected by the influence of soil moisture in the data.” Although these identifications are not as reliable as those from neutron monitors, the much larger number of CRNSs compared with NMs offers promise for expanding data collection.

Baird also found that the CRNS data recorded some medium to large solar events, such as Forbush decreases (FDs), which are decreases in galactic cosmic rays reaching Earth following solar coronal mass ejections. The CRNSs detected 4 out of 28 FDs that had been identified by NMs between 2014 and 2022.

An exciting opportunity exists to use cosmic ray neutron sensor (CRNS) networks globally to augment the roughly 20-station NM network.

CRNS data have also been used to simulate ground level enhancements (GLEs) of radiation levels at Earth’s surface caused by bombardments of intense solar cosmic rays. These emitted particles, primarily protons, are accelerated to high energies during solar flares or coronal mass ejections. GLEs are rarer than FDs, occurring once per year on average, but are more detrimental to humans and aviation. GLEs are also nearly impossible to predict and prepare for because they arrive at Earth only minutes after a solar flare or coronal mass ejection occurs, whereas FDs take several days to arrive.

Given the newfound connection between low-energy neutron observations and space weather phenomena, an exciting opportunity exists to use CRNS networks globally to augment the roughly 20-station NM network. This ability would offer an unprecedented number of ground monitors to help researchers understand and analyze larger FD and GLE events and their impacts all around Earth.

Two Communities Join Forces

The hydrology and space weather communities have worked together informally since the 2010 launch of the Cosmic-Ray Soil Moisture Observing System in the United States [Zreda et al., 2012]. But the need for additional collaboration has been identified in the literature and during joint sessions at AGU and European Geoscience Union meetings.

In response to this increased interest, the first Coordinated Cosmic-Ray Observation System Conference was held in October 2024 at the University of Nebraska–Lincoln. The hybrid event gathered 50 experts from academia, government, and industry to explore both the scientific potential of ground-based neutron monitoring across energy spectra and opportunities for productive cross-disciplinary partnerships.

Conference participants produced a concept paper identifying key issues on which the participating communities can work together. These issues involve critical needs for improved infrastructure and enhanced data accessibility.

Documenting soil moisture conditions more comprehensively and meeting data needs for environmental modeling and operational products, for example, require the deployment of additional CRNS stations globally—ideally, 30 stations per 1 million square kilometers. In the United States, this level of coverage equates to about 250 stations spread across the country’s roughly 8 million square kilometers.

With respect to space weather, NOAA’s SWPC has stated a need for real-time NM data (1-minute resolution with 5-minute latency) and additional NM monitoring sites to improve the spatial resolution of aviation forecasts. More NM sites are also needed to better understand the anisotropy (uneven distribution) of incoming cosmic ray particles globally, particularly during GLEs and other perturbed geomagnetic conditions, and how it may influence space weather impacts experienced around the planet.

By collaboratively addressing these and other gaps in the neutron-detecting networks used for space weather and soil moisture monitoring, we can advance scientific understanding of critical environmental and planetary processes and better serve the needs of operational systems designed to foster safety and prosperity.

References

Baird, F. (2024), The potential use of hydrological neutron sensor networks for space weather monitoring, Ph.D. thesis, University of Surrey, Guildford, U.K., https://doi.org/10.15126/thesis.901065.

Boteler, D. H. (2019), A 21st century view of the March 1989 magnetic storm, Space Weather, 17(10), 1,427–1,441, https://doi.org/10.1029/2019SW002278.

Dimitrova-Petrova, K., et al. (2020), Opportunities and challenges in using catchment-scale storage estimates from cosmic ray neutron sensors for rainfall-runoff modelling, J. Hydrol., 586, 124878, https://doi.org/10.1016/j.jhydrol.2020.124878.

Knipp, D. J., et al. (2016), The May 1967 great storm and radio disruption event: Extreme space weather and extraordinary responses, Space Weather, 14(9), 614–633, https://doi.org/10.1002/2016SW001423.

Kodama, M., et al. (1979), An application of cosmic-ray neutron measurements to the determination of the snow-water equivalent, J. Hydrol., 41(1–2), 85–92, https://doi.org/10.1016/0022-1694(79)90107-0.

Mishev, A. L., L. G. Kocharov, and I. G. Usoskin (2014), Analysis of the ground level enhancement on 17 May 2012 using data from the global neutron monitor network, J. Geophys. Res. Space Phys., 119(2), 670–679, https://doi.org/10.1002/2013JA019253.

Mishev, A., S. Panovska, and I. Usoskin (2023), Assessment of the radiation risk at flight altitudes for an extreme solar particle storm of 774 AD, J. Space Weather Space Clim., 13, 22, https://doi.org/10.1051/swsc/2023020.

Miyake, F., et al. (2012), A signature of cosmic-ray increase in AD 774–775 from tree rings in Japan, Nature, 486, 240–242, https://doi.org/10.1038/nature11123.

Montzka, C., et al. (2017), Validation of spaceborne and modelled surface soil moisture products with cosmic-ray neutron probes, Remote Sens., 9(2), 103, https://doi.org/10.3390/rs9020103.

National Academies of Sciences, Engineering, and Medicine (2024), The Next Decade of Discovery in Solar and Space Physics: Exploring and Safeguarding Humanity’s Home in Space, Natl. Acad. Press, Washington, D.C., https://doi.org/10.17226/27938.

Schrön, M., et al. (2021), Neutrons on rails: Transregional monitoring of soil moisture and snow water equivalent, Geophys. Res. Lett., 48(24), e2021GL093924, https://doi.org/10.1029/2021GL093924.

Väisänen, P., I. Usoskin, and K. Mursula (2021), Seven decades of neutron monitors (1951–2019): Overview and evaluation of data sources, J. Geophys. Res. Space Phys., 126(5), e2020JA028941, https://doi.org/10.1029/2020JA028941.

Zreda, M., et al. (2012), COSMOS: The Cosmic-ray Soil Moisture Observing System, Hydrol. Earth Syst. Sci., 16, 4,079–4,099, https://doi.org/10.5194/hess-16-4079-2012.

Author Information

Trenton Franz (tfranz2@unl.edu), School of Natural Resources, University of Nebraska–Lincoln; Darin Desilets, Hydroinnova LLC, Albuquerque, N.M.; Martin Schrön, Helmholtz Centre for Environmental Research UFZ, Leipzig, Germany; Fraser Baird, University of Surrey, Guildford, U.K.; and David McJannet, Commonwealth Scientific and Industrial Research Organisation, Canberra, Australia

Citation: Franz, T., D. Desilets, M. Schrön, F. Baird, and D. McJannet (2025), Two neutron-monitoring networks are better than one, Eos, 106, https://doi.org/10.1029/2025EO250212. Published on 6 June 2025. Text © 2025. 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.

Author(s): Santu Luo, Dingxin Liu, Wang Xi, Renwu Zhou, Mingyan Zhang, Rusen Zhou, Xiaohua Wang, Mingzhe Rong, and Kostya (Ken) Ostrikov

The phenomenon of discharge mode transition in air plasmas, which refers to the selective production of reactive species under different external conditions, has been a longstanding issue and has regained major attention recently due to emerging plasma applications. In this letter, we present a nove…

[Phys. Rev. E 111, 065205] Published Fri Jun 06, 2025

AbstractSlow Slip Events (SSEs) play an important role in the seismic cycle, participating in the moment budget of active faults. SSEs can be monitored via space geodesy (e.g., Global Navigation Satellite System, GNSS). One of the major challenges when studying geodetic data is that they record the deformation due to many active sources (e.g., tectonic, hydrological, volcanic, and anthropogenic). Here I present a procedure to automatically reconstruct the spatio-temporal history of SSEs in the Cascadia subduction region. The solution is updated daily and made publicly available. These results constitute the base for future prospective SSEs forecasting experiments.

Heat waves, droughts and forest fires are some of the extreme climate-related events that are expected not only to become more frequent but also to increasingly strike at the same time. This finding emerges from a new study led by Uppsala University, in which researchers have mapped the impact of climate change in different regions of the world.