Can you tell us about the focus of your research?

What originally inspired you to go into this field?

Looking back, what was the most challenging aspect of your PhD and what was the most enjoyable?

How was your experience of post-PhD life so far?

What advice would you give to an Early Career Researcher about the challenges they might face in academic life?

Why did you decide to become an Early Career Scientist Representative and how has being a part of the ECS team impacted you?

What do you plan to achieve as the ECS rep this year?

What do you like about the role?

What advice would you give to other early career scientists who perhaps want to contribute to the ECS network but are hesitant?

Should we just rely on AI?

Scientific dialogue is a complementary path

Introduction

Despite advances in our understanding of rock mechanics, the frictional behavior of rocks, and the physics of instability in geological materials, the coexistence of slow and fast earthquakes, as well as various types of fault-zone seismic radiation such as tremor, remains enigmatic.

Can fault mechanics and friction laws reproduce the full spectrum of observed seismicity?

In this week’s blog post, Navid Kheirdast takes us through the fundamentals of fault mechanics and frictional behavior before introducing a simple yet powerful mechanical model composed of a main fault interacting with a population of off-fault fractures. Despite its simplicity, the model captures a remarkable range of behaviors observed in nature, reproducing the spectrum of fault slip from slow slip events to fast, dynamic earthquakes.

1. Faults and the Forces They Bear

Faults are interfaces in Earth's crust—boundaries between tectonic plates or between blocks within a plate—that are permanently subjected to background stress driven by plate tectonics and environmental loading (tides, fluid pressure, and so on). In response to this stress, the two sides of a fault slide past each other: this sliding motion is called slip.

To describe fault slip mechanically, we need two families of variables:

• Dynamic variables — the traction (force per unit area) acting on the fault surface, including frictional resistance and pore-fluid pressure.
• Kinematic variables — the relative displacement (slip) and slip rate across the fault.

The fault-slip problem is a boundary-value problem: knowing one set of variables (say, the background stress) allows us to solve for the other (slip rate). The relationship between stress and slip is governed by a friction law.

2. The Wide Spectrum of Fault Slip

Slip on natural faults spans an enormous range of speeds, from a few millimeters per year all the way to several meters per second. Three end-member behaviors stand out.

Creep

Creep is aseismic, quasi-static sliding at roughly the tectonic loading rate — that is, the fault slides continuously at the same speed that the two tectonic plates move relative to each other. No earthquakes occur on creeping faults. Famous examples include the Parkfield segment of the San Andreas Fault and parts of the North Anatolian Fault near the Sea of Marmara.

[caption id="attachment_43029" align="aligncenter" width="282"] Creep across the San Andreas Fault. Credit: Coffey et al. (2022).[/caption] [caption id="attachment_43030" align="aligncenter" width="322"] Creep across the North Anatolian Fault. Credit: Becker et al. (2023).[/caption]  

Slow Earthquakes

Sometimes slip is faster than the tectonic loading rate but far too slow to radiate significant seismic waves in the classical seismic stations, and only very accurate GNSS stations can record them. Such events — slow earthquakes (also called slow-slip events or SSEs) — may unfold over weeks to months rather than seconds. They are commonly observed in subduction zones: Cascadia, Nankai, Mexico, and Chile all host periodic slow-slip events.

Regular (Fast) Earthquakes

At the other extreme, slip accelerates to meters per second in a matter of seconds, releasing energy as the seismic waves we feel as earthquakes.

The central question is: what controls which of these behaviors a given fault segment will exhibit?

3. Fault Stability

There exists a critical stiffness for any fault segment under tectonic loading. If the fault is "stiff enough," any small perturbation in slip rate dies out—the fault creeps stably. If the fault stiffness is below the critical value, perturbations grow, and an earthquake results.

Computing this instability threshold is an eigenvalue problem: the critical eigenvalue depends on the frictional properties of the fault surface. This insight was established experimentally by Brace & Byerlee (1966), who showed through triaxial rock-mechanics tests that laboratory rock samples under confining pressure produce stick-slip cycles — the laboratory analogue of repeated earthquakes.

4. Two Friction Laws for Fault Mechanics

Two friction laws dominate earthquake modeling, each capturing a different aspect of fault behavior.

4.1 Linear Slip-Weakening Friction

The simplest physically motivated friction law expresses resistance as a function of cumulative slip alone:

• At the onset of slip, friction equals the static strength τ_s = μ_sσ.
• As slip accumulates, resistance decreases linearly until it reaches a dynamic (residual) strength τ_d = μ_dσ after a characteristic slip distance D_c.
• Beyond D_c, friction stays constant at τ_d.

[caption id="attachment_43033" align="aligncenter" width="351"] Slip-weakening diagram: τ vs. δ (slip), showing peak τs, linear weakening to τd over Dc, then flat residual.[/caption]

Limitations of the slip-weakening law:

1. No mechanism for friction recovery, or healing — once the fault has slipped past D_c, it cannot restore its previous strength.
2. No rate dependence — friction does not depend on how fast the fault is sliding. This means that on a fault at the brink of instability, any infinitesimal increase in slip rate immediately triggers an earthquake, with no intermediate regime. Laboratory experiments, however, show that real surfaces resist sudden speed changes — they do not break instantly.

4.2 Rate-and-State Friction

Rate-and-state friction (RSF), formulated by Dieterich (1979) and Ruina (1983) on the basis of laboratory experiments, addresses both limitations. It expresses the friction coefficient as a function of slip rate v and a state variable φ:

μ = f* + a ln(v/v*) + b ln(θ/θ*)

where a is a constant related to the direct strength increase due to a jump in velocity, v* is a reference slip rate, and θ encodes the history of contact. The state variable evolves with slip and time, providing the fault with memory.

Two evolution equations for θ are widely used:

• Aging law — state evolves both with slip and with time (contacts strengthen even when stationary).
• Slip law — state evolves only when slip occurs; no healing without motion.

[caption id="attachment_43035" align="aligncenter" width="421"] Velocity-step experiment: the top panel shows slip rate jumping from v1 to v2; the bottom panel shows the immediate increase in the friction coefficient μ, followed by gradual relaxation to the new steady-state value μss.[/caption]

Key insight from RSF: If you suddenly increase the slip rate, friction immediately increases (the "direct effect"), but then slowly decreases back to a new steady-state value as θ evolves. The long-term (steady-state) friction level at speed v is:

τ_ss(v) = μ_ss(v) · σ, where Δμ_ss = (a−b) ln(v₂/v₁)

• If a − b < 0: steady-state friction decreases with increasing slip rate → velocity-weakening behavior → potentially unstable.
• If a − b > 0: steady-state friction increases with increasing slip rate → velocity-strengthening behavior → inherently stable.

Limitation of RSF: It was calibrated in laboratory experiments at low slip rates (micrometers to millimeters per second), far below the meters-per-second speeds reached during large earthquakes. At coseismic rates, slip-weakening is thought to take over.

5. The Spring-Slider System — A Minimal Earthquake Cycle Model

The Burridge-Knopoff spring-slider is the simplest mechanical system that reproduces stick-slip cycles. A block rests on a frictional surface and is connected by a spring to a loading point that moves at a constant velocity v_pl, representing the far-field tectonic loading rate.

[caption id="attachment_43036" align="aligncenter" width="379"] Spring-slider diagram: block on frictional surface, spring of stiffness k, loading point moving at v_pl; arrows showing slip δ, slip rate v, normal stress σ.[/caption]

Two outcomes are possible:

1. Stable sliding (creep): If the spring is stiff enough, the block slides at exactly v_pl, smoothly and continuously.
2. Stick-slip (earthquake cycles): If the spring stiffness falls below a critical value k_c, the block sticks while the spring stretches, then suddenly lurches forward when the spring force exceeds static friction — mimicking an earthquake.

6. Critical Stiffness — Why Each Behavior Occurs

Linear Stability Analysis

To find the threshold between stable and unstable slip, Ruina (1983) performed a quasi-static linear stability analysis around the steady-state sliding solution of a slider on a rate and state surface. The method is to perturb the slip rate slightly away from steady state and ask whether the perturbation grows or decays. Details of this analysis is well explained by Segall (2010), therefore we recommend the interested reader to follow from the text book.

The critical stiffness gives:

k_c = σ(b − a) / D_c

Physical Interpretation

The critical stiffness k_c has a transparent physical meaning:

The Two Regimes at a Glance

7. Numerical Example — Pseudo-Dynamic Simulation

To see both regimes in action, we solve the spring-slider equations numerically, including a radiation-damping term ηv that approximates elastic wave effects without a full dynamic calculation:

σ(f* + a ln(v/v*) + b ln(v*θ/D_c)) = k(v_plt − δ) − ηv

with the aging law θ̇ = 1 − vθ/D_c.

With parameters chosen so that a/b = 0.9 (velocity-weakening) and k/k_c = 0.95 (the spring is just below the critical stiffness), the simulation produces periodic stick-slip cycles:

[embed]https://www.youtube.com/watch?v=-eJFD_BW920[/embed]

These two simulations illustrate the core message: the same friction law and the same fault, but a single parameter crossing a threshold, separate creeping from seismogenic behavior.

8. Lack of Key Observations

The spring-slider model, which resembles a single fault, captures key features of seismic cycles, such as stick-slip and creep-like fault behavior, interseismic and coseismic phases, and periodic ruptures, but it misses some important features, such as the Gutenberg–Richter magnitude-frequency relation, the coexistence of slow and fast ruptures, aftershock sequences, and the localization of seismic activity. In a recent paper by Almakari, Kheirdast et al. (2026), the authors showed that considering the size distribution of off-fault fractures around a main fault produces all of these features.

[caption id="attachment_43069" align="aligncenter" width="1024"] The fault-volume model as presented in Almakari et al. (2026). In this model, a main rough fault is embedded in a fault volume. Close to the main fault, there is a higher density of off-fault fractures; fracture density decreases with distance from the fault. The size distribution of off-fault fractures follows a power law. Off-fault fractures are oriented optimally with respect to the background stress loading, and all faults are strengthened by rate-and-state friction, with Dc scaling with fault length.[/caption]

The results of this model interestingly reproduce all statistical properties observed in real catalogs, including the Omori and inverse Omori laws, the Gutenberg–Richter law, and scaling of fast ruptures as M ∼ T³ and slow events as M ∼ T.

[caption id="attachment_43060" align="aligncenter" width="1024"] Statistics of fault-volume seismicity from Almakari et al. (2026): (a) Omori-law decay in seismic activity after mainshocks, (b) Gutenberg–Richter magnitude-frequency distribution, (c) inverse-Omori increase in seismic activity prior to the main rupture, and (d) scaling of the magnitude and distribution of events produced in the fault zone lies between the M ∼ T³ and M ∼ T limits.[/caption]

An intriguing result of the fault-volume model is the migration of events in the fault zone as time approaches the main shock; after the main shock, events tend to return to background seismicity:

9. Conclusion

Fault slip is one of the most unpredictable processes in nature. Yet, as this post has shown, much of its complexity can be traced back to a surprisingly compact set of mechanical ingredients.

The wide spectrum of observed fault behaviors — from quiet aseismic creep, through slow-slip events that unfold over months, to sudden earthquake ruptures — is not the result of fundamentally different physical processes. It emerges from the interplay between frictional properties and the elastic stiffness of the surrounding crust. Rate-and-state friction, calibrated from laboratory rock experiments, captures the two key ingredients: a fault's immediate resistance to speed changes (the direct effect) and its gradual loss or recovery of strength over time (the evolving state variable). When these are combined with a simple spring-slider geometry, a single dimensionless ratio — k/k_c — determines whether the fault creeps or earthquakes.

  Yet the single-fault spring-slider model, elegant as it is, cannot explain the full texture of real seismicity: the statistical distribution of earthquake sizes, the complex migration of activity in space and time, the coexistence of slow and fast events on the same fault system. The fault-volume model of Almakari, Kheirdast et al. (2026) demonstrates that bringing in off-fault fractures with a power-law size distribution — each obeying the same rate-and-state friction, each interacting mechanically with the main fault — is sufficient to recover all of these features at once. The Gutenberg–Richter law, Omori-law aftershock decay, inverse-Omori foreshock acceleration, and the characteristic scaling differences between slow and fast ruptures all emerge naturally from a single, self-consistent mechanical framework.

  The broader lesson is one of emergent complexity from simple rules: a friction law grounded in laboratory physics, applied consistently across a geometrically realistic fault zone, reproduces phenomena that have long resisted explanation. This suggests that the enigmatic coexistence of slow and fast earthquakes, and the apparently erratic migration of seismic activity, may not require exotic physics — only a more complete account of the fault's mechanical environment.

  Understanding these mechanics has direct implications for seismic hazard assessment. If slow-slip events and tremors are governed by the same friction physics as regular earthquakes, they are not merely curiosities - they are windows into the stress state of fault zones, and potentially precursors to larger events. The migration patterns revealed by the fault-volume model, in particular, may one day inform operational monitoring strategies.

  Much remains to be done. Validating model statistics against dense seismic catalogs are all open challenges. But the foundation laid by decades of friction experiments, stability theory, and increasingly realistic mechanical models gives good reason for optimism that a unified physical picture of fault-zone seismicity is within reach.

References

Almakari, M., Kheirdast, N., Villafuerte, C., Thomas, M. Y., Dubernet, P., Cheng, J., ... & Bhat, H. S. (2026). Fault volume digital twin to reproduce the full slip spectrum, scaling, and statistical laws. Journal of Geophysical Research: Solid Earth, 131(5), e2025JB032915.

Coffey, G. L., Savage, H. M., Polissar, P. J., Cox, S. E., Hemming, S. R., Winckler, G., & Bradbury, K. K. (2022). History of earthquakes along the creeping section of the San Andreas Fault, California, USA. Geology, 50(4), 516–521.

Becker, D., Martínez-Garzón, P., Wollin, C., Kılıç, T., & Bohnhoff, M. (2023). Variation of fault creep along the overdue Istanbul–Marmara seismic gap in NW Türkiye. Geophysical Research Letters, 50. [doi.org](https://doi.org/10.1029/2022GL101471)

Zhang, H., & Li, F. (2024). A review of prediction methods for global buckling critical loads of pultruded FRP struts. Composite Structures, 329, 117752. [doi.org](https://doi.org/10.1016/j.compstruct.2023.117752)

Brace, W. F., & Byerlee, J. D. (1966). Stick-slip as a mechanism for earthquakes. Science, 153(3739), 990–992.

Dieterich, J. H. (1979). Modeling of rock friction: 1. Experimental results and constitutive equations. Journal of Geophysical Research: Solid Earth, 84(B5), 2161–2168.

Ruina, A. (1983). Slip instability and state variable friction laws. Journal of Geophysical Research: Solid Earth, 88(B12), 10,359–10,370.

Rice, J. R. (1993). Spatio-temporal complexity of slip on a fault. Journal of Geophysical Research: Solid Earth, 98(B6), 9885–9907.

Cochard, A., & Madariaga, R. (1994). Dynamic faulting under rate-dependent friction. Pure and Applied Geophysics, 142(3–4), 419–445.

Ben-Zion, Y., & Zaliapin, I. (2020). Localization and coalescence of seismicity before large earthquakes. Geophysical Journal International, 223(1), 561–583.

Segall, P. (2010). Earthquake and volcano deformation.

For centuries, humans have treated the ocean as a resource to exploit. From whale hunting and overfishing to noise pollution, oil spills, plastic pollution and increasing CO₂ emissions, humankind has endangered whole species and ecosystems. But what if the ocean could fight back? What if the deep sea hid an intelligence capable of retaliation? The Swarm, a science-fiction eco-thriller by German author Frank Schätzing, explores this scenario. First published in 2004, the novel tells the story of an unknown intelligent life form in the deep sea that strikes back against humanity’s exploitation of marine resources. 

There’s plenty of life down there. The trouble is, it sees us coming and steps aside.

The first half of the book follows several scientists as they investigate unusual behaviors and anomalies in the world’s oceans. These include a new species of deep-sea worm with symbiotic bacteria colonizing the North Sea floor, destabilization of the Norwegian continental shelf, changes in whale behavior, and poisonous jellyfish. These strange phenomena observed worldwide accumulate into vividly described catastrophic natural events similar to the disaster scenes in movies like 2012 or The Day After Tomorrow.

The second half focuses on the formation of an international scientific task force, bringing together experts from multiple fields to identify the threat and attempt communication with the unknown deep-sea intelligence. This team includes biologists, behavioral scientists, geologists, sonar experts, and SETI (Search for Extraterrestrial Intelligence) scientists, all working together under the lead of the US Navy and CIA. They identify the origin and purpose of the alien life form in the deep sea: a swarm-intelligence, which is aggressively fighting back and threatening all of humankind.

They want us to know that we’re in the here and now, whereas they’re everywhere and forever.

In the following chapters, scientists use newly developed technologies and risk their lives to counter the attacks and mitigate their effects. Others focus on understanding the swarm intelligence while trying to establish communication. At the same time, conflicts emerge between the scientists and the military leading the mission, creating tension and complicating their efforts.

The Swarm is a thought-provoking and thrilling novel that explores environmental and ethical questions. It is both an entertaining read and a striking reminder of the importance of respecting and preserving our natural world.

Read more:

• Interview with Gerhard Borhmann: https://up2date.uni-bremen.de/en/article/the-swarm-bremen-marine-geologist-gerhard-bohrmann-as-fictional-characte

The multi-hazard problem is not what you think

The uncertainty cascade

What honest multi-hazard modelling looks like in practice

A hazard map is a static product: a single model run and a single moment in time, with uncertainty hidden behind clean colour gradients.

References

_____________________________________________________________________________________________________________________________________

I grew up in Bangladesh, a country defined by delta and monsoon, emphatically not by ice. So when I stepped onto the frozen surface of Hudson Bay near Churchill, Manitoba in December 2021, on my first Arctic field campaign, I was meeting sea ice for the first time outside of a textbook (Figure 1).

I didn't really understand what it meant to travel over sea ice until I was on the back of a skidoo driven by a community member named Bryan. It was early in the freeze up season, what locals sometimes call the "free up" season, in acknowledgement of how volatile the margins can be. At one point, standing on the ice, I could not tell which direction the coast lay. Bryan simply followed the faint lines left by other skidoos, navigational knowledge encoded in the landscape itself, invisible to me but perfectly legible to him. A short while later, hunters returning from the landfast ice edge warned us we were closer to it than we had realised. Almost on cue, a crack began to open in the surface nearby. Bryan turned us around.

In that moment, the abstract vocabulary of my remote sensing work, freeboard, sea surface reference, landfast ice extent, collapsed into something urgent. Bryan needed to know how thick that ice was. The hunters needed to know whether the edge was stable. These are not research questions. They are safety questions.

Satellites That Couldn't See Near the Coast

The campaign was meant to be a satellite validation exercise: coincident snow depth and ice thickness measurements supporting ICESat-2 and CryoSat-2 freeboard retrievals during my MSc at the University of Manitoba (freeboard is the part of the sea ice above the waterline). But after we collected the data, we discovered that ICESat-2 had produced almost no usable freeboard within the 25 km coastal buffer around our study area. The multiyear record showed the same pattern season after season: a persistent coastal blank (Figure 2). The satellite had been overhead and had properly collected photons. But the downstream algorithms could not establish a reliable sea surface reference, and the data were flagged or discarded before any calculation for freeboard was attempted. That distinction matters, because it tells us where the solution lies: not in the hardware, but in what we do with the signal (Kwok et al, 2019, Petty et al., 2020). If ICESat-2 could not deliver freeboard within 25 km of the coast, we had to go to get the data. The following year our team at Maryland and the University of Calgary chartered a helicopter to Cambridge Bay, Nunavut (Figure 3 shows the sites that were surveyed). Cambridge Bay is, in many ways, the textbook setting for the failure modes I had identified in Hudson Bay: narrow channels with strong tidal forcing, persistent landfast ice well into spring, heavily ridged shore fast ice, and the operational stakes of sitting on the Northwest Passage shipping corridor (Smith et al., 2013). All algorithmic problems described in the following occur here simultaneously. That image of clean central Arctic freeboard ringed by a stubborn coastal void became the puzzle driving my MSc thesis and now my PhD at the University of Maryland.

How Altimeters Measure Ice

A sea ice floe sits in seawater like an ice cube in a glass with the freeboard as the portion above the waterline. Given an independent estimate of snow depth and the densities of snow, ice, and seawater, freeboard converts to total thickness through hydrostatic balance.

The two altimeters at the heart of this work see the floe differently. ICESat-2's ATLAS photon counting lidar reflects from the air to snow interface and therefore measures total freeboard (snow plus ice above sea level). CryoSat-2's Ku band radar is conventionally assumed to penetrate the dry snow column and reflect from the snow to ice interface, yielding ice freeboard, although the validity of that assumption is now under active scrutiny (Nandan et al., 2017). The difference between the two retrievals along nearly coincident ICESat-2 and Cryosat-2 orbits provides the basis for satellite snow depth on sea ice. It is also the reason coastal data loss is doubly costly: when the algorithms fail, we lose both freeboard and the dual altimeter snow depth product simultaneously.

The hard part for either sensor is the reference. We need an accurate, instantaneous sea surface height beneath the satellite, which in practice means finding open water or thin ice leads (sea ice leads are long cracks formed when Arctic ice floes diverge or shear), measuring their elevation, and differencing it from the surrounding ice. In the central pack, where leads are abundant and geometrically clean, this works well (Kwok et al., 2015). However, near the coast the scarcity of open water leads in the landfast ice along with challenges poised by algorithmic limitations linked to interactions of lidar with the rugged landfast ice makes the traditional method of measuring freeboard and ice thickness difficult. ICESat-2’s lead classifier, built on a narrow set of photon descriptors, misreads the coastal lead population: refrozen and wind roughened leads are misclassified as ice and bias the sea surface reference upward, while dark leads are indistinguishable from low albedo thin ice and excluded by default (Petty et al., 2021, Liu et al., 2025). On top of this, the embedded global tide models carry large errors in shallow bays, fjords, and inter island channels, leaving residual biases sufficient to drive thin first year ice freeboard into physically implausible negative values (Saha et al., 2025, Stammer et al., 2014).

Rescuing the Coastal Record

This diagnosis points directly at where the rescue effort has to happen: the surface classifier. A growing body of work, including custom and machine learning classifiers operating on ICESat-2 photon clouds, has demonstrated that a meaningful fraction of voided coastal segments contains physically valid specular returns that the operational classifier discards as ‘dark’ leads (Liu et al., 2025).

As part of my ongoing PhD research at the University of Maryland College Park, I’m working towards developing algorithmic improvements to the current fixed threshold based algorithm on ICEsat-2 to a deep learning based workflow for sea ice surface classification. The improvement also tries to address the dark lead misclassification issue and leads to rescue of leads that were labelled as “dark” by the algorithm and therefore not considered for subsequent freeboard measurements from ICESat-2.

From Satellite Design to Community Design

The Arctic's coastal blind spot reflects genuine physical complexity at one of the hardest remote sensing environments on Earth, compounded by algorithms calibrated for the central pack and ancillary inputs (passive microwave sea ice thickness, global tide models) that degrade near the coast. The diagnosis is now clear, and filling the spatial gap is tractable with designing coastal specific classifiers, using multi sensor integration with NASA’s Surface Water and Ocean Topography (SWOT)/ NASA-ISRO Synthetic Aperture Radar (NISAR), and conducting validation campaigns around the near coastal zone.

And exactly this social and ecological importance of the near coastal zone caused me to wonder what it would look like to build satellite data products around community need rather than asking communities to adapt to what satellites already produce. It would mean going to Churchill and Cambridge Bay not just to validate algorithms but to ask what information is actually missing, at what time of season, at what spatial resolution, and in what format it would be usable. It would mean near-real-time dissemination pipelines as a design requirement, not an afterthought. It would mean feedback loops where local observers, hunters, and rangers contribute ground truth that shapes not only algorithm validation but the variables the product prioritises in the first place. The technology to do much of this exists. What has been missing is the willingness to treat community knowledge as a design input rather than a communication challenge. Bryan knew the ice better than the satellite did. The right question is not how we explain our data to him, but how we build systems that learn from what he already knows.

References

• Kwok et al., 2019: ATLAS/ICESat-2 L3A Sea Ice Freeboard, Version 1
• Petty et al., 2020: Winter Arctic Sea Ice Thickness From ICESat-2 Freeboards
• Smith et al., 2013: New Trans-Arctic shipping routes navigable by midcentury
• Nandan et al., 2017: Effect of Snow Salinity on CryoSat-2 Arctic First-Year Sea Ice Freeboard Measurements
• Kwok et al, 2015: Variability of Arctic sea ice thickness and volume from CryoSat-2
• Petty et al., 2021: Assessment of ICESat-2 Sea Ice Surface Classification with Sentinel-2 Imagery: Implications for Freeboard and New Estimates of Lead and Floe Geometry
• Liu et al, 2025: Enhanced sea ice classification for ICESat-2 using combined unsupervised and supervised machine learning
• Saha et al., 2025: Snow depth estimation on leadless landfast ice using Cryo2Ice satellite observations
• Stammer et al., 2014: Accuracy assessment of global barotropic ocean tide models
• Huntington et al., 2016: Effects of changing sea ice on marine mammals and subsistence hunters in northern Alaska from traditional knowledge interviews
• Mahoney & Einhorn, 2026: The Evolving Decline of Landfast Sea Ice in Northern Alaska and Adjacent Waters: Results from an Updated Climatology
• Druckenmiller et al., 2013: Trails to the whale: reflections of change and choice on an Iñupiat icescape at Barrow, Alaska
• Stirling, 1997: The importance of polynyas, ice edges, and leads to marine mammals and birds

Edited by Florina Schalamon and Leah Sophie Muhle 

• GNSS and precise positioning,
• Earth observation and climate monitoring,
• gravity field determination,
• geodetic infrastructure and reference frames,
• surveying and land administration,
• and the role of geodesy in daily life.

– Edited by Leire Retegui-Schiettekatte

About Ilias’s research

About Ilias’s role as incoming President of HS Division

Hollow Earth?

Falling into the oven

Suspicious rocks

Thurston Lava Tube at Hawaii Volcanoes National Park, Big Island, Hawaii. Author: Frank Schulenburg

Christa Kelleher and Roger Moussa received the Hydrology and Earth System Sciences (HESS) Outstanding Editor Award for their excellent editorial work during 2025. Cyprien Louis, Landon Halloran and Clément Roques received the 2025 Jim Dooge award for their outstanding paper on Seasonal and diurnal freeze–thaw dynamics of a rock glacier and their impacts on mixing and solute transport

Medals and lectures

On Tuesday afternoon, Wednesday afternoon and Thursday evening the recipients of this years medals from the HS division gave insightful presentations to the HS and GA community. This years awardees are:

• Sally Thompson who received the Henry Darcy medal
• Thorsten Wagener who was awarded the John Dalton Medal
• Larisa Tarasova who received the HS Division Outstanding Early Career Scientist Award

Early Career Scientists networking

At Monday lunchtime, the ECS team of the HS division, along with the International Association of Hydrological Sciences (IAHS) and Young Hydrological Society ran the increasingly-popular annual HydromMeet social gathering. With amazing weather, we were able to mingle and network in the sunshine and it was great to see so many new connections being made.

Save the dates for the EGU General Assembly 2027!

References:

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Yuan, Tao, Shijie Zhong, Glenn Milne, and Donna dePolo. 2026. “Reconciling Inferences of Mantle Viscosity and Late Quaternary Ice - the Importance of an Asthenosphere and 3-D Viscosity.” ESS Open Archive 2026 (0417). https://doi.org/10.22541/essoar.15001943/v2.

Crowley, John W., Richard F. Katz, Peter Huybers, Charles H. Langmuir, and Sung-Hyun Park. 2015. “Glacial Cycles Drive Variations in the Production of Oceanic Crust.” Science 347 (6227): 1237–40. https://doi.org/10.1126/science.1261508.

Haskell, N. A. 1935. “The Motion of a Viscous Fluid Under a Surface Load.” Physics 6 (8): 265–69. https://doi.org/10.1063/1.1745329.

Kreemer, Corné, William C. Hammond, and Geoffrey Blewitt. 2018. “A Robust Estimation of the 3‐D Intraplate Deformation of the North American Plate From GPS.” Journal of Geophysical Research: Solid Earth 123 (5): 4388–412. https://doi.org/10.1029/2017JB015257.

Milne, G. A., J. L. Davis, Jerry X. Mitrovica, et al. 2001. “Space-Geodetic Constraints on Glacial Isostatic Adjustment in Fennoscandia.” Science 291 (5512): 2381–85. https://doi.org/10.1126/science.1057022.

Olive, J. A., M. D. Behn, G. Ito, W. R. Buck, J. Escartín, and S. Howell. 2015. “Sensitivity of Seafloor Bathymetry to Climate-Driven Fluctuations in Mid-Ocean Ridge Magma Supply.” Science 350 (6258): 310–13. https://doi.org/10.1126/science.aad0715.

Peltier, W. R., D. F. Argus, and R. Drummond. 2015. “Space Geodesy Constrains Ice Age Terminal Deglaciation: The Global ICE-6G_C (VM5a) Model: Global Glacial Isostatic Adjustment.” Journal of Geophysical Research: Solid Earth 120 (1): 450–87. https://doi.org/10.1002/2014JB011176.

Raymo, M. E., and W. F. Ruddiman. 1992. “Tectonic Forcing of Late Cenozoic Climate.” Nature 359 (6391): 117–22. https://doi.org/10.1038/359117a0.

Willen, Matthias O., Bert Wouters, Taco Broerse, Eric Buchta, and Veit Helm. 2025. “Satellite Data Reveal Details of Glacial Isostatic Adjustment in the Amundsen Sea Embayment, West Antarctica.” The Cryosphere 19 (6): 2213–27. https://doi.org/10.5194/tc-19-2213-2025.

UNAVCO, 2026, GPS Spotlight: Station CHUR: https://spotlight.unavco.org/station-pages/chur/chur.html (accessed May 2026).

Note by the editor, B. Schaefli: this is a blog post that is re-published from the Geolog

What if the next editor of a post was... ME? 

What does an editor do?

Don’t feel like words are your thing?

How much time shall I allow?

How to volunteer?

Where would we be without healthy soil? A lot of our research live would be in turmoil I’m here today to spread some soil appreciation, Which may require some thought transformation.

Soil Is the base of our beautiful landscapes, Often where we go for some restorative escapes, Some key reasons to save soils from destruction They support 95% of global food production, And act as a filter for water purification, Don’t forget it’s major role in carbon sequestration 

So how do we generate the respect and love it deserves? By learning about the life-sources it serves! Think not of the chicken, the sheep or the cattle, When that’s really only half of the battle, Think of the livestock that lives under the surface, As they hold a much more meaningful purpose.

Earthworms are by far the most popular soil dwellers, Aerating the soil for the smallest of fellas Some organisms you would need a microscope to spot, But their presence can really tell us a lot, Enchytraieds and eathworms share a connection, But often they don’t get as much attention, Enchytraeids prefer acidic soils, high in peat, Fungi and bacteria is what they eat

Look for collembola as soil health indication, Due to their habitat specification Leaf litter and moss is their preferred habitation, Driving the engine of organic matter rotation, Both springtails and mites can help to distribute Many different microbes as they are so minute, Attached to their backs and through digestion, The many soil benefits not even in question

Last on our list are these nematode creatures, Which possess a whole litany of soil beneficial features. From nutrient cycling to plant protection, Parasitic species can kill pests through infection!

We simply need to get people excited, to get them involved, their passions ignited! Show them why soil biodiversity really matters, Providing the food they see on their platters, The monitoring law will help us by tracking, A suite of soil data that is currently lacking, Which soils need protection and perhaps some improvement Lets get people on board this healthy soil movement!

• Reflection geometry: Useful reflections are limited to specific satellite viewing angles and directions that depend on local topography. Some stations remain challenging regardless of how much data is available. For example, a station surrounded by buildings or steep terrain may block the low-angle signals that GNSS-IR relies on most.
• Surface characteristics: GNSS-IR performs best near wide, relatively flat, and stable reflecting surfaces. Over land, open and homogeneous areas are favourable, while coastal applications require an unobstructed view of the sea.
• Signal diversity: Not all satellite signals perform equally well for reflectometry. Different signal types show varying sensitivity to reflections, and increasing the number of satellites or frequencies generally improves robustness, but does not compensate for poor site geometry.
• Receiver and antenna setup: The hardware used at a station can strongly influence the quality of reflected signals. Recording data at higher rates can improve results, especially at sites with larger antenna heights. It is also essential that signal strength information is retained in the observation files, as this is the primary input for GNSS-IR analysis.
• Site documentation: Photographing the station environment is a simple but valuable step. It helps assess whether a site is suitable for GNSS-IR and guides the selection of appropriate analysis parameters.

Further reading
For those interested in learning more about GNSS-IR, the following resources provide a good starting point:
 
Larson, K.M., 2016. GPS interferometric reflectometry: applications to surface soil moisture, snow depth, and vegetation water content in the western United States. WIREs Water 3, 775–787. https://doi.org/10.1002/wat2.1167
 
Larson, K.M., Williams, S.D.P., 2023. Water level measurements using reflected GNSS signals. IHR 29, 66–76. https://doi.org/10.58440/ihr-29-2-a30
 
For an interactive introduction to the technique, visit gnss-reflections.org.

- Edited by: Leire Retegui-Schiettekatte

As an individuum I have to decide on which of the equally interesting and relevant sessions I gift my attendance, as a community we are diluting audience for arduously prepared contributions.

Yuting Cheng

VLBI single station experiment with ASO304 data in preparation for the Genesis mission.

Explainable AI and GNSS Reflectometry for global soil moisture retrieval.

 References

 Steinberg, J., and Patterson, S., 2024. The Silicon Valley Startup Using AI to Scour the Earth for Copper and Lithium. The Wall Street Journal, 28 July. Available at: https://www.wsj.com/tech/ai/kobold-metals-ai-copper-lithium-caad58da?msockid=05c8c5d6a23e6e380f4ad03aa3456fc0

Sea breezes in change

• A weakening in intensity: since the 1980s, sea breeze speeds (intensity) have decreased by up to 10% per decade, particularly during the spring and summer months.

• A likely change in their seasonality: Conversely, the occurrence of sea breezes has increased, most notably in the winter, rising by roughly 10% per decade.

Summer weakening

Seasonal change

Why this matters?

1. Heat Extremes: As summer breezes weaken, their natural cooling effect diminishes exactly when it is needed most: during severe heatwaves. This threatens to exacerbate urban heat stress⁴ and increase public health risks. Not only are these winds becoming too weak to reach and cool inland areas, but they might be also becoming less frequent in summer, precisely when maximum temperatures are regularly exceeding 40ºC.

1. Air Quality: While stronger winds disperse atmospheric pollutants, weaker and more frequent sea breezes can trap and recirculate pollutants along coastal and inland areas for days, worsening exposure for densely populated cities.
2. Hydrological Cycles: Changes in these winds may impact moisture transport, potentially influencing deep summer convection (which causes severe storms). Furthermore, other studies⁵ suggest that anthropogenic (human-driven) land-use changes have left sea breezes with less available water to transport inland. Consequently, weaker breezes carrying less water vapour from evapotranspiration (water released into the atmosphere by soil and plants) may fail to trigger the summer storms necessary to sustain the hydrological cycle in already dry and arid regions.

Looking Ahead

“Sea breezes may be local winds, but their response to climate change tells a much larger story: one of complex interactions between warming, atmospheric dynamics, and the lived experience of climate along our coasts.”

This post has been edited by the editorial board

References:
1. Bedoya-Valestt, S., Azorin-Molina, C., Plaza-Martin, N.P. et al. Weaker and more frequent Mediterranean sea breezes in a warming climate. Sci Rep (2026). https://doi.org/10.1038/s41598-026-47025-4

2. Moon, W., Kim, B.-M., Yang, G.-H. & Wettlaufer, J. S. Proc. Natl Acad. Sci. USA 119, e2200890119 (2022). https://doi.org/10.1073/pnas.2200890119

3. Cresswell-Clay, N. et al. Twentieth-century Azores High expansion unprecedented in the past 1,200 years. Nat. Geosci. 15, 548–553 (2022). https://doi.org/10.1038/s41561- 022-00971-w

4. Di Napoli, C., Lavers, D.A., Bechtold, P. et al. Relief or Aggravation? A 31-Year study of sea-Land Breezes and Their Impacts on Coastal Heat Stress. Earth Syst Environ (2025). https://doi.org/10.1007/s41748-025-00917-3

5. Pausas, J. G. & Millán, M. M. Greening and browning in a climate change hotspot: The Mediterranean Basin. BioScience 69, 143–151 (2019). https://doi.org/10.1093/biosci/biy157

Antarctica’s ice shelf cavities – the hidden underside

Drilling into the unknown 

A glimpse beneath the ice

From snapshots to timeseries

From beneath the ice to the wider ocean

Further Reading

• Beneath Antarctica’s largest ice shelf, a hidden ocean is revealing its secrets
• A Long-Term Look Beneath an Antarctic Ice Shelf

Edited by Mirjam Paasch and Mack Baysinger

The Depiction of Water in Art

The Challenge of Water in Photography

Water and a Photographic Practice

Edited by B. Schaefli

Academia is often imagined as a space driven by merit, curiosity, and scientific collaboration. Still behind publications, conferences, and research achievements, many women in STEM continue to navigate environments shaped by subtle exclusion, normalized inequalities, and power imbalances that are not always openly discussed. In Earth Sciences, where collaboration and field-based research are fundamental, conversations about gender inequality are becoming increasingly visible. However, visibility does not necessarily mean resolution. Experiences such as being underestimated, interrupted, professionally devalued, or discouraged still affect many women throughout their academic careers, many often in ways that are difficult to quantify, but deeply impactful over time. To reflect on these issues, this blog post week, Katherine Villavicencio spoke with Dr. Karen Gariboldi, Senior Researcher from the Department of Earth Sciences at the University of Pisa, whose work focuses on marine micropaleontology and paleoenvironmental reconstruction. Drawing from her personal and professional experience, she shared thoughtful perspectives on impostor syndrome, academic power dynamics, mentorship networks, institutional responsibility, and the kind of cultural transformation that academia still urgently needs. Her reflections remind us that gender equality is not only about policies or representation in numbers. It is also about creating academic environments where people feel respected, heard, supported, and genuinely allowed to belong.

I graduated from the University of Milan Bicocca with a degree in Geological Sciences and Technologies, following the Marine Geology curriculum. My Master’s thesis focused on diatom assemblages (microalgae with a siliceous skeleton) characterizing the rocks of the Miocene Pisco Formation in Peru. I then completed a PhD in Earth Sciences at the University of Pisa, again working on diatom assemblages from the Pisco Formation. I investigated the information they provide from a biostratigraphic, paleoclimatic, and sedimentological/diagenetic perspective. I am currently a Senior Researcher at the Department of Earth Sciences at the University of Pisa, where I continue my work as a marine micropaleontologist, also in Arctic regions, with a focus on the Holocene as well, not only the Miocene. Diatoms, however, remain my main area of expertise.

I think there is now much greater awareness of gender-based inequalities; however, this doesn’t mean that these issues have been fully overcome. There is still a lot of work to be done, but the fact that these topics are discussed so widely is, in my opinion, a big step forward. Many studies clearly show that in academia—especially in STEM fields—the gap between women and men tends to widen as you move toward higher or more “powerful” positions.

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