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In March 2026 I recorded 61 fatal landslides causing 520 fatalities, the highest March total on record.

This is my regular update for the number of fatal global landslides, focusing on March 2026. AAs usual, this data has been collected in line with the methodology described in Froude and Petley (2018) and in Petley (2012). References are listed below – please cite these articles if you use this analysis. Data presented in these updates should be treated as being provisional at this stage.

The headline figures are as follows:

March 2026: 61 fatal landslides causing 520 fatalities;

This is very surprising total once again – 61 fatal landslides is the highest March total in my long term dataset – the previous record was 49 events in 2024. The baseline mean (2004-2016) is c.23 fatal landslides.

Loyal readers will know that my preferred way to present the annual data is using the cumulative total number of fatal landslides calculated in pentads (five day blocks). To make this easier to interpret, I have converted the pentads into day numbers through the year (so 1 January is day number 1, 31 December is day number 365).

This is the data for 2026 to the end of March:-

The cumulative total number of fatal landslides through to March 2026, plotted with the long term mean number and the exceptional year of 2024 for comparison.

The factors that are driving this very high level of recorded fatal landslides are not clear to me at this point. Perhaps it is a change in the quality of information I’m collating, although this seems unlikely to be the sole cause. Perhaps it is associated with the rapid degradation that is occurring in mountain areas (more on this to come). Perhaps it is the result of climate change. Interestingly, March 2026 was exceptionally warm compared to the long term record, globally, but it was “only” the fourth warmest March on record. March 2024 was the warmest on record.

This all requires more detailed analysis, which I have yet to do. But, at the moment, 2026 is proving to be a bad year for fatal landslides. A major caveat though is that the early months of the year are not a good predictor of what might happen through the Northern Hemisphere summer months, driven mainly by the SW monsoon in South Asia, the summer monsoon in East Asia and patterns of tropical cyclones.

References

Froude, M. and Petley, D.N. 2018.  Global fatal landslide occurrence from 2004 to 2016.  Natural Hazards and Earth System Sciences 18, 2161-2181.

Petley, D.N. 2012. Global patterns of loss of life from landslides. Geology 40 (10), 927-930.

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.

Author(s): Neco Kriel, Mark R. Krumholz, Patrick J. Armstrong, James R. Beattie, and Jennifer Schober

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A beam of relativistic charged particles propagating in a plasma can drive plasma electrons to oscillate and together form a wave whose phase velocity matches the velocity of the driving beam. These plasma waves realize state-of-the-art compact accelerators through plasma wakefield acceleration. Her…

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SummaryIn many fields of geoscience, researchers study the Earth’s properties by solving inverse or inference problems. Probabilistic approaches have gained increased attention over the past decade because they address the non-linearity and non-uniqueness properties of many naturally-inspired inverse problems and allow uncertainties in the solutions to be estimated. However, implementing such methods is computationally expensive and requires expertise in inverse and inference theory, high performance computing, and the geoscientific theory to be inverted. This makes the methods inaccessible to many geoscientists. In this paper, we first review the theoretical background of a particular suite of probabilistic algorithms referred to as parametric variational inference (PVI), and introduce GeoPVI, an open-source Python package designed to facilitate the implementation of these methods. With GeoPVI, users can model uncertainties in their geophysical parameter estimates efficiently given their expertise in inverse theory. It differs from sampling-based, non-parametric variational methods in that the probabilistic solution – the posterior or post-inversion probability distribution function that describes uncertainty in the model parameters of interest – is parametrised by explicit mathematical expressions. These expressions allow for the efficient storage and transfer, and for the evaluation of the posterior probability density for any set of parameter values. We demonstrate how to use the package to solve a set of problems, including tomographic imaging using travel time data, full waveform inversion, surface wave dispersion inversion, and vertical electrical sounding. We provide built-in forward functions to simulate first arrival travel times and full acoustic waveform data (in two spatial dimensions), and external forward functions can be incorporated into the package easily. We also demonstrate how to change prior information efficiently post-inversion, using the method of variational prior replacement. Contributions from the community are welcome, to make the package more broadly applicable.

SummaryElectrical resistivity tomography (ERT) is a widely used and effective tool for hydrogeological investigations. Conventional ERT inversion approaches are based on gradient-based algorithms, which typically provide deterministic optimal solutions, which are subject to uncertainty. Such uncertainty could have significant impact on hydrogeological interpretation using ERT. Model appraisal is a critical step after inversion, however, conventional appraisal methods are qualitative and thus subjective. To address these limitations, this study introduces a probabilistic variational inference (VI) method, referred to as Stein variational gradient descent (SVGD), to quantify both resistivity distributions and associated uncertainties in ERT inversions. Synthetic examples are conducted to investigate the effects of configurations and noise, and to compare the performance of SVGD with conventional inversion and model appraisal techniques. A field case study and its model validation are also presented to demonstrate the practical advantages of uncertainty quantification in field. The results indicate that SVGD can effectively reduce artifacts introduced by regularization and provide more comprehensive quantitative insights into subsurface structures compared to conventional approaches. The study also reveals limitations in the interpretation of basic statistics of uncertainty estimates, highlighting the need to examine the entire posterior distributions of parameter values. Additionally, this study demonstrates that the final uncertainty arises from a trade-off among multiple factors, such as geometry of subsurface structures, measurement techniques and data noise levels. Finally, we also discuss some comparisons with other probabilistic frameworks in hydrogeophysics, highlighting its potential to improve uncertainty and probability quantification in ERT, and possible future developments in hydrogeophysical coupled inversion.

SummaryUnderstanding the internal structure of the Earth is achieved using geophysical data and inversion is a powerful mathematical technique used by resource explorers to do so. Inherent ambiguity means that an infinite number of petrophysical models exist that can explain the geophysical data, so constraints such as geological models and petrophysical data have been employed to reduce the solution space. The constraints, like the data, are subject to noise and error, resulting in uncertainty propagating to the final model because inversion is designed to use the algorithm and constraints to find the single ‘best’ solution. Current practice assumes the best solution is found by optimising for the lowest misfit between the data and model; however, if the data is uncertain, the model fit to that data is likewise uncertain and potentially misrepresentative. Optimising misfit also means that inversion is subject to overfitting. Overfitting occurs when a model achieves the lowest misfit values by inadvertently fitting to data noise. Overfitting inversion occurs when the model has too many free parameters with no constraints, resulting in near-surface anomalies that can be mistakenly identified as legitimate targets for exploration rather than model artefacts. This contribution describes the use of spatial uncertainty calculated from geophysical data, providing free parameter constraints to reduce overfitting for geophysical inversion. The spatial uncertainty estimate is taken from a geostatistical model calculated using Integrated Nested Laplacian Approximation (INLA). A region in the East Kimberley, northern Western Australia, is subject to gravity inversion using Tomofast-x, an open-source inversion platform. Inversion is conducted using different configurations. Inversion is run without spatial uncertainty constraints, as is current practice, and then with spatial uncertainty constraints to test their effect on the resulting petrophysical model. The geostatistical model offers different percentiles from the geophysical model representing the extrema of estimated gravimetry values in the 10th and 90th percentiles. Inversions are run using these ‘extrema’ alongside the current practice of using the 50th percentile (or ‘mean’) gravity models as the observed field. Examination of inversion using and not using spatial uncertainty constraints shows that overfitting can be reduced. Using the extrema percentiles as the observed field has lesser benefits to reduce overfitting.

SummaryThe Earth’s ancient magnetic field is challenging to constrain from the rock record in large part due to the presence of non-ideal magnetic recorders in addition to processes, like alteration, that affect the ability of a material to reliably record field strength. Of the magnetic minerals present on Earth’s surface, magnetite is one of the most commonly used to simultaneously recover palaeomagnetic direction and intensity. Recent work on archaeological artifacts and clinker deposits (sedimentary rocks baked by coal seam fires) has identified a potential new mineral capable of recording the full-vector magnetic field: ɛ-Fe2O3, a high-T metastable phase of hematite. The palaeomagnetic potential of ɛ-Fe2O3, specifically regarding palaeointensity, has not been studied in depth. Further, recent work on synthetic ɛ-Fe2O3 has raised questions about the reliability of this phase for palaeointensity recording. To understand whether ɛ-Fe2O3 is a trustworthy full-vector magnetic recorder, more work is needed to assess this phase in its natural form. Here, we present results from Thellier-style palaeointensity experiments using a lab-induced thermoremanent magnetization (TRM) on natural ɛ-Fe2O3 present in Quaternary age clinker samples from the Custer National Forest, Montana, USA. The experimental setup was designed in attempt to isolate the ɛ-Fe2O3 phase from other magnetic carriers. The results of our study suggest that natural ɛ-Fe2O3 can reliably record palaeointensity and palaeodirections, yielding palaeointensity estimates within 5% and directions consistent with the applied laboratory TRM field. These new results suggest that ɛ-Fe2O3 bearing artifacts and clinkers can be robust full-vector magnetic recorders. Overall, this study adds confidence to previously obtained archaeomagnetic data and to a novel palaeomagnetic recorder, clinkers, opening the door to a more detailed characterization of the recent field.

SummaryContinental collision is prevalent along the Tethyan tectonic belt, characterized by diverse deformation patterns across regions, including concentrated deformation in the Alps, integral deformation throughout the Tibetan Plateau, and separate deformation within the Iranian Plateau. However, the mechanisms governing the diversity of deformation in different collisional orogens along the Tethyan tectonic belt remain poorly understood. Accretion of continental terranes during the closure of the Paleo- and Neo-Tethys oceans generated a highly heterogeneous lithosphere along the southern margin of Eurasia, a crucial factor in interpreting continental deformation. This study employs 2D thermo-mechanical numerical modeling to assess how tectonic inheritance-induced rheological heterogeneities govern deformation patterns in continental collision orogens. Our simulation results reveal three end-member deformation patterns resulting from variations in the rheology of the upper plate within the collision system. When the upper plate is uniformly strong, it prevents deformation from propagating into the interior of the continent, resulting in concentrated deformation in the collision front. If the upper plate is uniformly weak, deformation occurs throughout the entire upper plate, resulting in an integral deformation pattern. When a rheologically weak block is embedded in the strong upper plate, deformation concentrates in the collision zone and the weak block, resulting in separate deformation within the upper plate. Changes in the rheology of the bounding plates, the convergence rate, and the total convergence amount would not alter the basic deformation pattern of the continental collision system, if the rheology of the upper plate remain unchanged. Based on our simulation results, we suggest that the rheological characteristics of the upper plate govern the deformation patterns in continental collision systems. Our simulation results provide first-order explanations for the observed diversity of deformation in different continental collision systems along the Tethyan tectonic belt.

SummaryThe dispersion of Scholte waves provides a fundamental basis for inverting shallow seafloor elastic parameters. With the expansion of marine exploration, an isotropic seabed approximation has become increasingly inadequate. Therefore, in this study, Scholte-wave dispersion was analyzed in vertically transversely isotropic (VTI) media and the sensitivities of key parameters were quantified. Using a reduced delta-matrix formulation, a numerically stable dispersion equation for fluid-solid-coupled VTI media was derived and validated with elastic wavefield modelling and frequency-velocity spectra. Sensitivity tests on three representative seabed models [velocity increasing with depth (VID), a low-velocity layer (LVL), and a high-velocity layer (HVL)] show that anisotropy amplifies phase-velocity sensitivity to P-wave velocity (VP), especially for higher modes. In contrast, sensitivities to Thomsen parameters ε and δ are secondary but non-negligible. As mode order increases, the sensitive frequency band broadens and penetrates to greater depths. For the HVL model, dispersion is particularly sensitive to the overburden above the high-velocity layer. By contrast, for the LVL model, sensitivity concentrates within the low-velocity layer itself and above it. These sensitivity patterns reflect the influences of different parameters on inversion results and support the development of dispersion curve inversion for anisotropic shallow seafloor.

Daily travel plans and early warnings for extreme weather all rely on traditional numerical weather prediction. However, both traditional numerical weather prediction and AI forecasting large models have long suffered from systematic biases, which compromise forecast accuracy.

The world's oceans may be quietly amplifying climate change in ways scientists are only beginning to understand. In a new study published in Proceedings of the National Academy of Sciences, University of Rochester scientists—including Thomas Weber, an associate professor in the Department of Earth and Environmental Sciences, and graduate student Shengyu Wang and postdoctoral research associate Hairong Xu in Weber's lab—uncovered a key mechanism behind methane production in the open ocean. Their research indicates that this mechanism could intensify as the planet warms, providing an alarming feedback loop for global warming.

Deadly heat wave events are occurring at temperatures and humidity levels previously thought to be survivable, according to a new paper by a team of international researchers, including from The Australian National University (ANU) and the University of Sydney. The research is published in the journal Nature Communications.

When tectonic plates move, they rarely do so smoothly. Sometimes they slide almost imperceptibly; at other times, stress is suddenly released—resulting in an earthquake. What exactly governs this behavior remains one of the key open questions in earthquake research.

A global study by the University of Basel, Switzerland, reveals a surprising picture: While 42% of treelines worldwide are shifting upslope, 25% are retreating. This seemingly contradictory trend involves more than just warming. Climate change and human land use are interacting.

When it comes to wildfires, the story may seem straightforward: As forests burn, they release greenhouse gases like carbon dioxide, carbon monoxide, and methane that warm the planet. But in the far northern parts of North America, wildfires don’t always follow the same script.

In a new study published in Nature Geoscience, researchers found that forest fires in Alaska tend to have a warming effect on Earth’s atmosphere but those in western Canada can contribute to net cooling.

“The most surprising aspect is that if you take away this permafrost component, fires in general in Alaska would switch” from a net warming to cooling effect.

Geography and permafrost help explain the discrepancy. When forest fires burn in Alaska, they not only burn the forest but also thaw permafrost. Both of these phenomena release carbon into the atmosphere. Northern Canada also has permafrost, and blazes there also burn trees and the soil layer that anchors them. However, as reported in an influential 2006 study, these fires are more likely to leave behind open spaces that can be blanketed by bright snow in winter. This brighter surface reflects more sunlight, triggering a net cooling effect.

“The most surprising aspect is that if you take away this permafrost component, fires in general in Alaska would switch” from a net warming to cooling effect, said Max J. van Gerrevink, a climate scientist at Vrije Universiteit Amsterdam in the Netherlands and lead author of the study.

Missing Permafrost

Van Gerrevink’s research builds on the landmark 2006 study, which provided an innovative approach to assessing the climate-warming potential of boreal wildfires but didn’t address a key contributing factor: carbon emissions from permafrost. This exclusion meant that while the 2006 finding held true for some boreal regions, it couldn’t be generalized across the board.

“We know that there’s more carbon released than was actually implemented in that study,” van Gerrevink said.

Van Gerrevink and his team tracked the satellite data of all wildfires in Alaska and western Canada from 2001 to 2019. They accounted for possible warming processes such as greenhouse gases released during a fire and permafrost thawing after a fire. They also considered possible cooling processes, including snow-covered landscapes or atmospheric aerosols reflecting sunlight and forest regrowth absorbing carbon dioxide.

“We also trained models, first on historical climate data making the models quite robust and then substituting climate data with future projections,” van Gerrevink added.

They found that even a small number of fires that burn intensely and thaw the carbon-rich permafrost can have a large warming effect. Importantly, as climate warms and snow cover declines, even fires that have a cooling effect may increasingly shift toward a warming in the future.

A 360° View of Wildfires

“Every fire is really ecosystem dependent. When a fire burns, it’s going to burn differently depending on what the surrounding ecosystem structure is,” said Kimberley Miner, an Earth scientist at the NASA Jet Propulsion Laboratory who was not involved in the study. “What this study is pointing out is that’s true in the Arctic too.”

In the new paper, van Gerrevink and his coauthors found that “climate-warming fires occur preferentially in dry, high-elevation, steep permafrost landscapes,” while “climate-cooling fires are driven by longer spring snow exposure and occur more frequently in continental regions near the tree line.”

“I think the study motivates us to think of fires as being more complex than [just] good or bad.”

Dense permafrost layers in some areas of the Northern Hemisphere, Miner explained, mean “we have to think about fires in a really different way, in a much more complete, almost 360° way—not just what’s happening aboveground,” but below the surface too.

Christopher Williams, an Earth system scientist at Clark University in Worcester, Mass., who also was not involved with the study, said its consideration of the relationship between permafrost and wildfire-related emissions could reshape the way scientists think about the ecological effects of fires.

“I think the study motivates us to think of fires as being more complex than [just] good or bad,” he said.

—Saugat Bolakhe (@scigat.bsky.social), Science Writer

Citation: Bolakhe, S. (2026), Alaska’s wildfires heat the planet, but Canada’s cool it, Eos, 107, https://doi.org/10.1029/2026EO260112. Published on 9 April 2026. Text © 2026. The authors. CC BY-NC-ND 3.0
Except where otherwise noted, images are subject to copyright. Any reuse without express permission from the copyright owner is prohibited.

Editors’ Highlights are summaries of recent papers by AGU’s journal editors. Source: Journal of Advances in Modeling Earth Systems

Thunderstorms play a central role in tropical weather as they do not only produce local, extreme rainfall, but also interact with their environment. These interactions, from local to large-scale, can strongly influence both the mean climate and its variability. A new generation of kilometer-scale Global Storm-Resolving Models (GSRMs) is expected to represent these multi-scale processes more realistically by explicitly resolving deep convection. Understanding how storms interact with environmental moisture and temperature, and how these interactions shape the climate system’s internal variability, remains a central challenge for GSRMs.

In a new study, Takasuka et al. [2026] analyze multi-year simulations from three GSRMs (ICON, IFS, and NICAM) to examine how these next-generation models represent convective storms and how these representations relate to their different approaches to modeling convection. Although the models capture the timing of peak rainfall over ocean well, they tend to simulate storms that are too numerous and too small. Moreover, the models differ in the lifecycle of convection, particularly in the transition from shallow to deep convection and in the storage of atmospheric moisture, resulting in different large-scale mean state (e.g. precipitation) and variability (e.g. the Madden-Julian oscillation).

The study highlights how mesoscale coupling between convection and the thermodynamic environment shapes larger-scale tropical weather and climate characteristics, while revealing persisting challenges in representing complex storm processes in GSRMs and identifying key areas where a more realistic representation of convective–environment interactions could lead to more reliable simulations.

Time-height evolution of moisture (color shading) and temperature (blue contours) from 48 hours before to 48 hours after the peak of deep convective storm events over the tropical ocean, shown for reanalysis data (a; observational reference) and three kilometer-scale global storm-resolving models: (b) ICON, (c) IFS, and (d) NICAM. Both moisture and temperature are expressed as deviations from the ±48-hour mean. Credit: Takasuka et al. [2026], Figure 6 (a-d)

Citation: Takasuka, D., Becker, T., & Bao, J. (2026). Precipitation characteristics and thermodynamic-convection coupling in global kilometer-scale simulations. Journal of Advances in Modeling Earth Systems, 18, e2025MS005343. https://doi.org/10.1029/2025MS005343

—Jiwen Fan, 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.

While Saturn has 274 confirmed moons in its orbit, its largest moon, Titan, is of particular interest to researchers due to its similarities to Earth. A new article in Reviews of Geophysics explores the geophysical parallels between Earth and Titan, and how scientists could use field work on Earth to learn more about both worlds. Here, we asked the lead author to give an overview of Titan…

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A shutdown of the Atlantic Meridional Overturning Circulation (AMOC) could trigger a substantial release of stored ocean carbon into the atmosphere over hundreds of years, according to a new study that simulated such a collapse under stable climate conditions. This would add 0.2°C of extra global warming. The new paper from researchers at the Potsdam Institute for Climate Impact Research (PIK), published in Communications Earth & Environment, highlights the AMOC's role as a key regulator of the global climate.