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Mud is messy. For some, it's a plaything. To many, it can mean real hardship. Mud, though, is often overlooked, particularly when it lies out of sight. Deep down at the bottom of the sea, it is one of the most important natural archives of Earth's past—holding clues of shifting climates, coastlines, ocean conditions and carbon storage.

New research led by IIASA reveals a surprising link between two major climate-tipping elements: the Southern Amazon rainforest and the Atlantic Meridional Overturning Circulation (AMOC). While the study finds that a weakening AMOC may buffer dry season rainfall loss in the Amazon, it also highlights the urgent need to reduce emissions as broader climate risks continue to escalate.

Data that has been lost in the weeds—or more accurately the turfgrass—could help improve estimates of carbon dioxide emissions from urban areas, according to a team led by scientists at Penn State.

Source: AGU Advances

As the global climate continues to warm, fire seasons have intensified, and large-scale wildfires have become more frequent in many parts of the world. Factors such as vegetation type, land use patterns, and human activity all affect the likelihood of ignition, but wildfire proliferation ultimately depends on two factors: climate and fuel availability.

Kampf et al. studied relationships between fire, fuel, and climate in temperate regions around the world, focusing specifically on western North America, western and central Europe, and southwestern South America. Each of the three regions includes desert, shrub, and forest areas, as well as local climates ranging from arid to humid.

The researchers pulled information on total burned area and wildfire frequency in these regions between 2002 and 2021 from the GlobFire database, and they sourced data on land cover and biomass during the same period from NASA’s Global Land Cover Mapping and Estimation (GLanCE). They also used precipitation and evapotranspiration data from TerraClimate to calculate the mean annual aridity index (mean annual precipitation divided by mean annual evapotranspiration) for each region.

The researchers found that over the 20-year study period and across all three regions, fires burned smaller areas of land in zones with either very dry climates or very wet climates compared with zones of intermediate aridity. They suggest that this trend is explained by the lack of vegetation sufficient to fuel widespread fires in dry zones and, in wet zones, by weather conditions that dampen the likelihood of fires. In contrast, burned areas were larger in the intermediate zones where biomass abundance and weather conditions are more conducive to fueling fires.

Of the three regions studied, North America had the largest total burned area, fraction of area burned, and fire sizes. The researchers note that the fragmentation of vegetated areas in South America (by the Andes Mountains) and in Europe (because of extensive land use) has likely limited the sizes of fires and burned areas in those regions. They also point out that rising temperatures and aridity are increasing the risk of large wildfires in all three regions, suggesting that fire managers need to be flexible and responsive to local changes. (AGU Advances, https://doi.org/10.1029/2024AV001628, 2025)

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

Citation: Derouin, S. (2025), The Goldilocks conditions for wildfires, Eos, 106, https://doi.org/10.1029/2025EO250215. Published on 9 June 2025. Text © 2025. 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.

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

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."