As glaciers thin and retreat worldwide, the stability of surrounding rock slopes is increasingly at risk. This study investigates the long-term deformation of two major on-going instabilities, Portage A and Portage B, situated above Portage Glacier in Alaska. By analyzing decades of historical imagery and remote sensing data, we reconstructed the spatial evolution of these slopes, revealing progressive deformation up-glacier over the past sixty years. To further assess the links between glacier change and slope deformation, we combine structural mapping with remote sensing observations and kinematic analyses. Our results identify three distinct kinematic domains and show that progressive deformation is initiated once the glacier surface lowered below a critical elevation. This creates kinematic freedom for the rock mass to move along structural discontinuities. At Portage Glacier, the onset and progression of the instabilities are not governed solely by glacier thinning but reflect a complex, site-specific interaction between structural discontinuities and cumulative weakening from external processes. Glacier retreat and thinning act as one component within a broader “cascade system”, where multiple factors interact. Additionally, preliminary results from our three-dimensional model provide additional insights into the mechanical response of the slopes under changing boundary conditions. These findings highlight the importance of integrating structural, kinematic, and remote sensing data to better understand paraglacial slope dynamics and anticipate future instabilities in rapidly deglaciating mountain regions.

How to cite: Lemaire, E., Hamdi, P., Dufresne, A., Higman, B., Walden, J., Manconi, A., Jacquemart, M., and Amann, F.: Structural and Kinematic Controls on Paraglacial Rock Slope Deformation at Portage Glacier, Alaska, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2146, https://doi.org/10.5194/egusphere-egu26-2146, 2026.

In May 2025, a catastrophic debris-ice avalanche with a volume of approximately 10 million cubic meters led to the complete destruction of the village of Blatten (Valais, Switzerland). Despite tremendous destruction, and due to extensive monitoring and mitigation measures of the authorities, the residents of Blatten could be evacuated in time. The aim of this talk is to review the dynamic processes associated to the catastrophe, and the implications to the population of Blatten.

Accelerated deformation of the rock slope of Kleines Nesthorn around two weeks before the debris-ice avalanche was the first process in a cascade of events. The rock collapse was followed by 1) rock debris accumulating and loading Birch Glacier, 2) glacier collapse followed by two-phase debris-ice avalanche on 28 May 2025, 3) debris deposition as thick as 32 m onto the village of Blatten, 4) river damming in the main valley, 5) lake formation up-valley, and 6) outflow of impounded water and formation of a new river bed. The role of the inherited geology and climate on preconditioning and triggering of enhanced rock-collapse activity with a subsequent debris-ice avalanche is still debated and focus of ongoing research. Kleines Nesthorn, consisting of various metamorphic bedrock types (interlayering of jointed granitic gneisses, amphibolites, and biotite-sericite gneisses), has a complex geologic origin. The exposition and altitude of the rock flanks indicate that those bedrocks very likely were affected by the presence of permafrost. The unfavourable geology in combination with melting permafrost (and increased hydrostatic pressures) are most likely the main causes of the natural disaster. Meteorologic conditions prevailing in May 2025, such as the heavy precipitation on 28 May 2025, most likely were saturating the collapsed debris that was temporarily accumulated on Birch Glacier, resulting in a higher water content of the collapsed debris and partly explaining the runout of the event.

The village of Blatten existed since at least 1433, and through the past centuries, the population of Blatten has learnt to live with the threat of a variety of geohazards. Historic documentation shows that the two most common geohazards are snow avalanches and floodings, with recurrence intervals of 2 and 16 years for the Lötschental Valley. These records, however, lack any documentation about rock avalanches, highlighting the absence of a baseline for this type of hazard. The geohazard map of Blatten has been updated in November 2025, and its results allow to build a safe Blatten 2.0 following a well-defined roadmap, with land for building available in moderated areas, and return of residents by 2029.

The event is unprecedented for the Swiss Alps both in terms of the dynamics of collapse and its devastating impacts, and highlights that disasters can happen even on very low probabilities. Due to timely evacuation and avoided loss of life, financial support from the insurance companies and donations, and specific regulations for the case of Blatten, the mood and willingness to return to Blatten is rather high, indicating that a Blatten 2.0 has a “prosperous” future if time schedules are kept.

How to cite: Bellwald, B.: Anatomy of the Blatten rock-collapse debris-ice avalanche (28 May 2025): Insights from a local Quaternary geologist, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6599, https://doi.org/10.5194/egusphere-egu26-6599, 2026.

On May 28th 2025, a massive rock avalanche buried the village of Blatten under 9 million m3 of ice and rock. Thanks to the expertise of many specialists and continuous monitoring and data collection from measurement systems in the field, it was possible to detect this danger in advance and to protect the life of many people. Already before this tragedy in Blatten, the movement of the Birch Glacier was monitored. These monitoring systems later also revealed the upcoming collapse of the Kleines Nesthorn and the associated collapse of the glacier. Consulting expert Geoformer and local authorities mandated Geoprevent to install an interferometric radar which was done at the day, when Blatten was evacuated. The radar installed provided valuable data about the displacement of the entire region around the Birch Glacier and helped the authorities to manage the situation. This system records even small movements of the mountain independent of rain, snow, fog or darkness and can therefore see things that a human eye cannot. It can be used up to distances of 5 km and to monitor areas of more than  5 km2. Only one day before the collapse of the glacier Geoprevent installed a camera on the Eastern moraine of the Birch Glacier to monitor it from the top. These images revealed a dramatic picture during the last hours and showed a rapid movement of the glacier, with measured velocities of several tens of meters per day in the hours leading to the collapse of the glacier. With this camera-based technology and the help of complex algorithms, our monitoring system was able to provide data about the displacement to the experts for their risk assessments and for monitoring and evaluating the situation continuously. Currently, the installation is changing from an emergency and short-term project to a mid- and long-term monitoring solution which should provide safety for the clearance and construction works in Blatten. Additional cameras and GPS systems were installed by our team to provide an even deeper insight into the instabilities and deformations around the Kleines Nesthorn. 

How to cite: Carrel, M., Gassner, J., Wetter, J., Stitelmann, Ó., St.Pierre - Ostrander, T., and Vincent, S.: Monitoring of the "Kleines Nesthorn" and of the Birch Glacier before and during the rock avalanche in Blatten, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3801, https://doi.org/10.5194/egusphere-egu26-3801, 2026.

Cascading slope failures in high mountain environments are observed with increasing frequency as glaciers retreat and slope stability is impacted by warmer conditions. On 28 May 2025, a large rock-ice avalanche (~9.3x10⁶ m³) originating from Birch Glacier, Switzerland, destroyed parts of the village of Blatten, and provided a rare, well-documented case of a rapid, highly mobile mass movement.

We combine seismic observations, geomorphological mapping, grain size and permeability measurements, and granular flow modelling to reconstruct the evolution of this event, from precursory instabilities to the main collapse. Seismic data scanned with machine learning algorithms reveal a two-week period of increasing rockfall and small glacier failures preceding the main collapse. The main collapse was reconstructed using force history inversion of low-frequency seismic signals from Switzerland’s national seismic network. Numerical simulations constrained by both seismic data and observed deposit extents indicate that an exceptionally low effective basal friction was required to reproduce the observed deposit extent and force history. This and the field observations of low-permeability deposit materials indicate that frictional weakening contributed to the unexpectedly high mobility of the main event.

Our results highlight the value of integrating seismic monitoring with field and modelling approaches to constrain the dynamics of complex rock-ice avalanches. The Blatten event illustrates how large alpine slope failures can transition into highly mobile flows. Our study provides one of the first detailed reconstructions of this hazard cascade, including precursory failure activity, and the dynamics and frictional characteristics of the main event. The frictional weakening inferred here provides a much-needed mechanistic basis for predicting runout and deposit geometry in large debris avalanches.

How to cite: Kang, J., Lucas, A., Mangeney, A., Gaume, J., Allstadt, K., Hibert, C., Toney, L., Vicari, H., Dietze, M., Jacquemart, M., Peruzzetto, M., Blatny, L., Kyburz, M., Rimpot, J., Farinotti, D., and Walter, F.: Dynamics and precursors of the 2025 Blatten rock–ice avalanche: Integrating seismic analysis, granular flow simulations, and field observations, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9045, https://doi.org/10.5194/egusphere-egu26-9045, 2026.

As is well known, global warming and consequent environmental changes are rapidly changing natural hazard scenarios, especially at high elevation, where the cryosphere is degrading at an accelerated rate. The increasing frequency of natural instability processes at high elevation and their change in seasonality are now well-established globally. In recent years, a growing attention has been paid to unprecedented process chains (e.g., the collapse of the Birch Glacier in 2025 in Switzerland or the huge Mount Meager event in 2010 in Canada). However, in recent years, some unusual instability processes in the Italian Alps highlighted emerging hazards that deserve further investigation. Over the three-year period from 2021 to 2023, and with intensification in 2024, the Rin da Clus torrent (Livigno, Central Italian Alps) was affected by recurring debris flow events, even in the absence of rainfall, triggered by the rapid thawing of the frontal sector of a rock glacier. In June 2024, and again in September 2024, the proglacial areas of numerous Alpine valleys in the Western Alps were devastated by an intense meteorological event, extraordinary for high mountains, which caused widespread and sometimes extreme torrential processes, initiated in Little Ice Age deposits. The most emblematic event was the collapse of the LIA frontal moraine of the Northern Grandes Murailles Glacier (Aosta Valley), which mobilized nearly 2 million cubic meters of debris. Finally, in July 2025, a portion of the debris talus on a permafrost slope in Val di Rhemes (Western Italian Alps) suddenly collapsed. Although these are isolated events and sometimes small (as in the case of the Livigno debris flows and the Val di Rhemes collapse), these phenomena draw attention to the effects of global warming on the stability of debris accumulations, which in high mountains are often steep enough to be potentially susceptible to instability. In fact, very little is known about the distribution and thermal conditions of ground ice, while the volumes of debris that can be mobilized are rarely known. These phenomena deserve careful consideration in the coming years, given the extent of debris covers in high-elevation areas, their susceptibility to instability because of slope and lack of vegetation, and the great distances that the resulting debris flows can travel.

How to cite: Chiarle, M., Matta, E., and Nigrelli, G.: Emerging hazards in the Italian Alps under climate and environmental change, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6995, https://doi.org/10.5194/egusphere-egu26-6995, 2026.

Mass movements in the Mont-Blanc massif (French Alps) are traditionally monitored by a network of human observers (AlpRisk and ObsAlp networks in the past and Regard d’Altitude currently) and annual LiDAR campaigns for some areas. These observations provide accurate locations and estimates of event size. However, those approaches have several limitations: observations are biased toward areas frequently visited by people and are potentially incomplete in remote regions. In addition, temporal accuracy is frequently poor (except during peak periods for mountaineers), as many observations are based on debris deposits rather than on the events themselves.

Seismic monitoring using permanent seismic stations installed in the area offers a promising complementary solution to these limitations. Rapid mass movements such as serac collapses and rockfalls generate particular seismic signals, providing excellent temporal resolution and continuous coverage, including in areas that are rarely observed directly. Their seismic signatures differ significantly from those of earthquakes, requiring dedicated methods for event localization and size estimation.

Based on field observations of mass movements and the Sismalp seismic event catalog, we compiled a reference catalog currently consisting of 107 seismic events associated with 91 field observations, including volume estimates for 55 events. This catalog was used to fine-tune and evaluate automated algorithms for the localization and size estimation of mass movements using seismic data.

Mass movement localization is performed using a combination of an amplitude decay method and the BackTrackBB algorithm based on signal coherence. We achieved a median location accuracy of 1.6 km and observed a significant improvement in localization accuracy with increased seismic station coverage. Event size was estimated using a simple linear model based on seismic energy, resulting in a median relative error of approximately 70 %.

Our results show that automated seismic monitoring of mass movements can be successfully applied in remote high-mountain environments. The performance of our method can be further improved by increasing the number of seismic stations and by improving data processing techniques.

How to cite: Kokowski, J., Helmstetter, A., Larose, E., Ravanel, L., and Cailhol, X.: Automated seismic monitoring of mass movements in the Mont-Blanc massif , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10264, https://doi.org/10.5194/egusphere-egu26-10264, 2026.

As climate change continues, many glaciers around the world are warming and melting. Among these glaciers, those located in steep mountains are susceptible to instabilities, increasing the risk of a sudden release of ice. This release of ice may turn into a granular flow as the ice fractures while rolling downhill, sometimes over long distances, posing significant risks to infrastructures and mountain communities at lower altitudes. As the frequency of ice avalanches is expected to increase in the coming decades, it is crucial to understand and estimate their runout distances and the geometry of the final deposit to assess potential threats.
In this study, we simulated 15 past, well-documented ice avalanches with known volumes of detached ice and estimated release and deposition areas. The selected avalanches cover a wide range of volumes, from 40,000 to 85 million cubic meters, and are mainly located in the Alps, with two additional events in the Aru range in China. These avalanches are composed of ice, whereas flows mixed with snow, rocks, or water exhibit a different flow rheology. To simulate the ice avalanches, we used a depth-averaged flow model with the Voellmy rheology. This method is commonly used to reproduce large geophysical flows such as landslides and snow avalanches. For each event, multiple simulations were performed to define the parameter set that reproduces the observed avalanche's runout and geometry. Among these parameters, cohesion is determined based on weather conditions, and the Voellmy friction parameters, dry and turbulent friction, are systematically adjusted. 
From the performed simulations, we found a strong relationship between the volume of the ice avalanche and dry friction, with dry friction decreasing as volume increases. Moreover, turbulent friction is found to depend mainly on flow volume and dry friction, but is also influenced by other factors, such as topography and temperature at the time of the event. These results also provide insight into the internal dynamics of ice avalanches, which align with the few cases for which velocities were estimated. Based on the estimated parameters, we propose a scaling law to simulate an ice avalanche relying on the released ice volume. This study aims to provide an initial set of parameters for estimating the runout and final deposit of ice avalanches, contributing to forecasting and mitigating the risks associated with potential ice avalanches.

How to cite: Escobar Rincon, A. F., Thibert, E., Bonnefoy-Demongeot, M., and Faug, T.: Back analysis of ice avalanches using depth-averaged modelling, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3573, https://doi.org/10.5194/egusphere-egu26-3573, 2026.

Alpine rock slope failures are frequent hazardous events. To reduce risk, unstable rock sections are increasingly monitored, e.g. with automatic displacement and tilt meters or high-resolution remote techniques. However, effective mitigation actions require the triggering of meaningful alarms. Currently, these alarm thresholds are often set manually based on expert knowledge, which may create too conservative or insufficiently sensitive thresholds that are not well enough adapted to changing conditions over time. Instead, we hypothesise that dynamically updated thresholds based on statistical analyses of continuous observations can provide a more robust, comprehensible, and performant approach to early warning.

Here, we present an approach to determine alarm thresholds for automatic monitoring devices based on statistical analyses of past observation data of two high-alpine sites. We analyse multiple years of automatic measurements gathered from high-frequency real-time monitoring systems. At the Hochvogel summit (DE/AT; 2,592 m a.s.l.), we monitor a 200,000 to 600,000 m³ complex rock slope instability with 12 sensors since 2019 and no major rock fall event yet. The second site at the Kitzsteinhorn north flank (AT; 3,029 m a.s.l), includes 6 sensors and a 600 m³ rockslide failure in August 2025 that has been recorded by the displacement sensors. Preliminary results show that this approach is able to reduce the frequency of false alarms over time and can detect critical accelerations earlier than fixed manual thresholds.

We anticipate that this statistical analysis of multiple years of observations including failure and non-failure events can guide decision-makers and monitoring system operators on how to set initial reasonable alarm thresholds and how the thresholds can be adjusted over months and years of system operation for an early detection of hazardous accelerations.

How to cite: Leinauer, J., Offer, M., Hartmeyer, I., Hofner, M., and Krautblatter, M.: Towards statistical alarm threshold determination for alpine rock fall monitoring systems, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12511, https://doi.org/10.5194/egusphere-egu26-12511, 2026.

Catastrophic mass movements threaten mountain communities worldwide. Rockfalls, avalanches, debris flows and sediment pulses in rivers are common geomorphological processes but can destroy homes and infrastructure with little warning. Population pressure, thawing permafrost and other climatic affects will likely exacerbate this threat in the near future requiring new risk management strategies and monitoring tools.

In recent years, seismology has emerged as an efficient observational method to capture rapid mass movements and study their dynamics as well as variations in event activity. Multi-million cubic meter rock-ice avalanches like the 2025 event destroying parts of the village of Blatten, Switzerland, are often detected by national seismic networks primarily designed to monitor earthquake activity. Smaller events like rockfalls and debris flows require denser seismic networks with station spacing of a few kilometres or less. Nevertheless, their seismic signature is usually clear when seismic stations are close enough.

The straightforward detection of mass movements using seismic instrumentation has motivated new monitoring approaches. However, the challenge remains to automatically identify the seismic mass movement signature in continuous data streams given a wealth of other signals like anthropogenic noise and earthquakes, which are recorded at the same time and may mask the sought-after mass movement signals. Recent applications of machine learning algorithms have provided promising first results and allowed for mass movement detection in cases where empirical threshold-based triggering rules yield impermissible amounts of false positives.

Here we present a new approach to detect mass movements signals in continuous seismic catalogues. To tackle the challenge of algorithm transferability between sites with different seismic background noise we treat mass movement signals as anomalies given their catastrophic nature and rare occurrence. We use the isolation forest algorithm to quantify the degree of anomaly (‘anomaly score’) associated with any recorded signal. Using data from polar fjord systems, our results show that anomaly detection can efficiently reduce continuous seismic data sets to a handful of signals, which are likely related to rock avalanches and glacier break-off events. On smaller scales, anomaly scores can be processed to identify general characteristics of debris flow seismograms recorded near active torrents. The anomaly score approach thus facilitates systematically searching for large-scale mass movement seismograms in earthquake monitoring data and may be a stepping stone for flexible and transferable detection algorithms for monitoring and warning purposes.

How to cite: Walter, F., Kamper, F., Paitz, P., Meyer, M., Matusiak, R., Volpi, M., and Amato, F.: Unsupervised Machine Learning Algorithms for Seismic Detection of Catastrophic Mass Movements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5696, https://doi.org/10.5194/egusphere-egu26-5696, 2026.

Rockfalls radiate complex seismic signals that encode both evolving source dynamics and path-dependent interactions, yet these signals are rarely exploited to support real-time trajectory inference within a unified framework.Here we develop a physics-constrained framework that integrates deep learning with forward motion modeling to jointly infer rockfall source location and motion mode from multi-station seismic observations, and to translate these inferences into early trajectory prediction.A spatiotemporal network combining temporal convolution and graph convolution exploits inter-station waveform variability to estimate three-dimensional source locations and discriminate motion regimes in near real time.Field experiments in China and the French Alps demonstrate meter-scale localization accuracy and enable early estimates of subsequent impact points before terminal deposition, providing actionable lead time for dynamic hazard response.Guided by rockfall source–path mechanisms, we further introduce spatial information as a physically meaningful proxy for propagation effects, which substantially improves motion-mode discrimination and yields spectrally coherent attention patterns consistent with observed impact and rolling processes.Finally, we show that localization accuracy is jointly controlled by dataset size and spatial scale, revealing that site-scale effects can outweigh gains from simply increasing sample numbers.Together, these results demonstrate that embedding physical cognition into deep learning enables seismic wavefields to be translated into real-time, interpretable constraints on rockfall dynamics, outlining a pathway toward physics-informed monitoring of gravity-driven hazards.

How to cite: Yan, S., Li, Z., and Wang, D.: Physics-Informed Seismic Inference of Rockfall Sources and Motion Regimes, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12337, https://doi.org/10.5194/egusphere-egu26-12337, 2026.

Rockfalls threaten infrastructure and lives and are driven by progressive, climate-induced rock damage that weakens slopes until failure. Resonance frequency analysis can be to used to track stress evolution at the cliff (decameter) scale (1), but it lacks sensitivity to the near-surface zone where weathering initiates. We therefore combine resonance monitoring with high-frequency ultrasonic testing to resolve stress changes in this critical surface layer.

We deployed six ultrasonic transducers (two emitters and four receivers) over a few square meters on a 50-m-high south-facing limestone cliff above the Chauvet cave (SE France), while resonance frequencies were continuously recorded with a seismometer. On the one hand, repeated ultrasonic measurements provide relative sonic velocity changes as a proxy for near-surface stress changes and damage. On the other hand, resonance frequencies reflect the apparent rigidity and fracture dynamics of the entire rock column, which have been shown to track progressive damage at this site (2).

The data reveal pronounced diurnal velocity cycles driven by temperature-controlled opening and closure of micro-fractures. A major summer rainfall event caused an abrupt ~10% drop in sonic velocity, indicating a transient loss of near-surface rigidity. By constraining the surface contribution to resonance-frequency changes with the ultrasonic data and finite-element modelling, we could also show that rainfall promotes opening of the rear fracture of the cliff.

These coupled observations indicate that rainfall induces pore-pressure changes and fracture-deformation effects that temporarily reduce stiffness and accelerate sub-critical crack growth, promoting long-term slope weakening. The combined ultrasonic-seismic approach thus provides a powerful framework for quantifying climate-driven damage and improving rock-slope hazard assessment.
 

1 ) Guillemot, A., Baillet, L., Larose, E., & Bottelin, P. (2022). Changes in resonance frequency of rock columns due to thermoelastic effects on a daily scale: observations, modelling and insights to improve monitoring systems. Geophysical Journal International, 231(2), 894-906.

2 ) Guillemot, A., Audin, L., Larose, É., Baillet, L., Guéguen, P., Jaillet, S., & Delannoy, J. J. (2024). A comprehensive seismic monitoring of the pillar threatening the world cultural heritage site Chauvet‐Pont d'Arc cave, toward rock damage assessment. Earth and Space Science, 11(4), e2023EA003329.

How to cite: Starke, J., Rousseau, R., Baillet, L., Guillemot, A., and Larose, E.: How Rainfall and Temperature Modulate Rock-slope Stiffness: Insights from Ultrasonic and Resonance Monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6634, https://doi.org/10.5194/egusphere-egu26-6634, 2026.

The high mountain areas of the Alps are particularly sensitive to rising temperatures as expressed by the well-documented glacier loss. The link between a warming climate and the frequency of large alpine mass movements is, however, less conclusive across the Alps as a whole. Landslide and rockfall inventories are typically limited to a single class of events, localized to a specific region or research objective, available only with large delays or out of date and frequently the data contains considerable observer bias [2]. Automated and near real-time detection of large mass movements using seismic infrastructure networks have been proposed at a national level, e.g. a for Switzerland [1]. Here, a validation mechanism using an expert group of local observers is used to validate detected events post fact. This allows to (i) detect, localize and classify large rockfalls and landslides at a regional level, (ii) reduce observer bias in manually curated catalogs, and (iii) provide first quantitative analysis of events within minutes. For example, for the main collapse of the Birchgletscher/Nesthorn in Blatten CH on May 28th, 2025, the first analysis of Magnitude 3.1 was available at 15:39:37 CEST, merely 14 minutes after the event occurred. 

In this work, we first analyze the events collected and cataloged using seismic detection over the past two decades in Switzerland and bordering regions under the auspices of the Swiss Seismological Service and the Rockfall Mailing List Collaboration. In a second step, we extend this to the whole Alpine Arc with events from national seismic inventories from Switzerland, Italy, Austria, France and Germany, spanning from 1990 to present. We compare this data to catalogs derived from personal observer networks [2], scientific literature, and personal communications. Initial analysis shows that alongside the increasing temperatures and melting glaciers, the number of large mass movements in the Alps has also been rapidly increasing. A clear elevation envelope from 1000 to 4000 m is found to contain almost all of the rockfalls in the Alps and the majority of which occur in areas under permafrost conditions. The joint multi-source inventory is a first step towards a comprehensive and up-to-date statistical analysis of the impacts of climate change on the occurrence of high alpine mass movements in the Alps. 

[1] Kastli, P., Clinton, J., Kraft, T., Diehl, T., and Haslinger, F.: A near-real-time public mass movement catalogue for Switzerland, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19568, https://doi.org/10.5194/egusphere-egu25-19568, 2025.

[2] PERMOS Rockfall Catalog. https://www.permos.ch/data-portal/rock-falls, accessed January 2025.

How to cite: Saibene, G., Amschwand, D., and Beutel, J. and the Rockfall Group Collaboration: Near Real-time Detection of Mass Movements at an Alpine Scale, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19133, https://doi.org/10.5194/egusphere-egu26-19133, 2026.

Rockfall runout and energy dissipation are controlled by complex interactions between block characteristics and terrain properties, yet simplified approaches remain widely used for rockfall hazard assessments in the praxis. This contribution revisits the rockfall energy line method (also known as the Pauschalgefälle method), presenting its conceptual basis and an online implementation tool for estimation of rockfall velocities and kinetic energies along slope profiles. The method projects an idealised energy line from the rockfall release area to the distal margin of the deposit zone, comparable to the shadow angle approach, with modified slope angles used to approximate terrain resistance due to surface roughness and vegetation.

Despite its simplicity, the rockfall energy line method is commonly employed as a first-order estimate and plausibility check in rockfall hazard assessments. Here, we evaluate the method against data from controlled, real-world rockfall experiments and examine its performance relative to advanced numerical rockfall models. The comparison illustrates how this simplified energy-based approach can complement process-based simulations, particularly where field data are limited.

Our benchmarking highlights some limitations of the current rockfall energy line method, particularly for large, idealised blocks travelling over smooth alpine meadow terrain. Based on these findings, we propose practical adaptations to the method that improve its applicability to extreme rockfall scenarios and provide guidance for its appropriate use in rockfall hazard assessment for the praxis.

How to cite: Glover, J. and Volkwein, A.: Benchmarking and adapting the rockfall energy line method for rockfall hazard assessment , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19539, https://doi.org/10.5194/egusphere-egu26-19539, 2026.

We present an open-source version of the software STONE [1] for the three-dimensional simulation of rockfall trajectories. The software implements a lumped mass kinematic model that simulates trajectories in a spatially distributed manner, in areas of up to thousands of square kilometers, starting from topographic (digital terrain model, DTM) and ancillary data easily manageable in GRASS GIS [2,3].

The rockfall phenomena that can be described with the STONE model are those involving the fall of individual blocks, which do not interact during their motion with other moving blocks/boulders, and whose trajectory can be described by a combination of parabola sections (free fall), bouncing on the ground and rolling.

In addition to a DTM, minimal data required for running the r.stone module are: a raster map of the numerical coefficient of friction, relevant for the rock rolling phase, two maps of numerical coefficients of normal and tangential restitution, which control the loss of kinetic energy at each bounce, and a raster map defining the initiation points of trajectories (sources). The latter is the most distinguishing input of the software, as the simulated motion of the falling block starts at these user-defined points of the topography.

In this contribution, in addition to presenting the new software, we discuss methods to obtain the location of rockfall sources on large areas, based on different strategies. These strategies mostly involve using maps of known source locations, either observed in the field or inferred from expert mapping, and generalizing them to other possible source locations with statistical and/or machine learning methods, under the common denominator of using information from a DTM as a starting point. In the definition of rockfall sources, specific triggering events can be taken into account, such as earthquakes [4,5] or intense rainfall events [6].

The main output of the model is the count of trajectories crossing each DTM grid cell, for given source locations and number of simulated trajectories. The output can be ascribed a probabilistic meaning, to obtain a physically-based susceptibility map for rockfalls. The absolute values in the raster output can be classified according to different criteria, mostly depending on the specific target relevant to the study, typically transport corridors (railways [3], roads [7,8]), buildings, and other urban infrastructure [9].

The software manual is available on the GRASS GIS addons repository [10].

[1] F. Guzzetti et al., Computers & Geosciences 28, 1079-1093. https://doi.org/10.1016/S0098-3004(02)00025-0

[2] F. Guzzetti et al., Environmental Management 34, 191–208. https://doi.org/10.1007/ s00267-003-0021-6

[3] M. Alvioli et al., Rockfall susceptibility and network–ranked susceptibility along the Italian railway. Engineering Geology 293, 106301. https://doi.org/10.1016/j.enggeo.2021.106301

[4] M. Alvioli et al., Landslides 21(1) 1-16 (2024). https://doi.org/10.1007/s10346-023-02127-2

[5] M. Alvioli et al., Geomorphology, 429, 108652 (2023). https://doi.org/10.1016/j.geomorph.2023.108652

[6] M. Alvioli & M. Melillo (in preparation)

[7] B. Pokharel et al., Bulletin of Engineering Geology and the Environment 82, 183. https://doi.org/10.1007/s10064-023-03174-8.

[8] M. Santangelo et al., Nat. Hazards Earth Syst. Sci. 19, 325-335 (2019). https://doi.org/10.5194/nhess-19-325-2019

[9] M. Santangelo et al., Journal of Maps 17, 124 (2021). https://doi.org/10.1080/17445647.2020.1746699

[10] https://grass.osgeo.org/grass-stable/manuals/addons/r.stone.html

How to cite: Alvioli, M., Guzzetti, F., and Antonello, A.: Three-dimensional rockfall modeling in GRASS GIS with r.stone, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16085, https://doi.org/10.5194/egusphere-egu26-16085, 2026.

How to cite: Vollmer-Quintullanca, M., Fischer, M., Dolores-Tesillos, E., and Zischg, A.: Back calculation of the 2005 Le Dar debris flow with EDDA 2.0 model: Initiation, entrainment, and deposition of a pro-/periglacial debris flow, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-18844, https://doi.org/10.5194/egusphere-egu26-18844, 2026.

Rockfalls represent a substantial threat to railway routes, due to their rapidity, destructive potential and high probability of occurrence on steep topographies. Approaches for the assessment of rockfall susceptibility range from statistical methods, for modeling large areas, to deterministic ones, for application in local analyses. A common requirement is the need to locate the source areas, often found uphill on cliffs, and the subsequent assessment of the runout areas of rockfalls stemming from such areas. Modelling rockfall phenomena is complex and requires various inputs, including: accurate location of the source areas,  geomorphological and  geological setting, and other geo-environmental factors.

We present an application conducted along the Rocca San Zenone - Giuncano Scalo railway line, in a study area (26 km²) in Central Italy,  where rockfall are abundant. The activity consisted in creating rockfall trajectory maps, in geotiff format, starting from possible source areas, and in classifying and validating the maps. The following software was used to create the trajectory map: i) rockyfor3D (https://www.ecorisq.org/ecorisq-tools); and ii) STONE (Guzzetti et al., 2002). The classification and validation phase was carried out using the R RF-Tools software (Rossi, 2023). Methodology for the Creation of Landslide Susceptibility Maps to produce rockfall susceptibility zoning, considering three scenarios: i) plausible scenario, ii) best case scenario; and iii) worst case scenario.

The rockfall modeling procedure was developed as part of a national project dedicated to the preparation of an operational methodology for assessing landslide susceptibility along the entire Italian railway network.

How to cite: Ardizzone, F., Rossi, M., and Santangelo, M.: Calibration and validation of rockfall modelling along a railway section in an mountain area in Central Italy, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10355, https://doi.org/10.5194/egusphere-egu26-10355, 2026.

On 25 December 2022, a rockfall of approximately 6,000 m³ detached from the east wall of the Mont de Nona rocky crest (Pré-Saint-Didier, Aosta Valley, NW Italy), impacting the mountain road connecting the La Thuile ski resort and the international route to France via the Piccolo San Bernardo Pass. The event triggered an emergency response by the Aosta Valley Geological Survey to support rapid, provisional risk-mitigation measures, including the construction of a rockfall embankment at the road and stabilization arrangements on the slope aimed at limiting the consequences of potential subsequent collapses.

This contribution presents an integrated, multi-sensor surveying workflow designed to document the post-event morphology and the final state of the emergency works in a steep, partly inaccessible alpine environment, and to provide an accurate topographic basis for subsequent hazard and risk evaluation. The survey combined: (i) Unmanned Aircraft System (UAS) photogrammetry supported by RTK GNSS ground control; (ii) a scanning total station acquiring high-resolution point clouds and imagery, particularly effective in areas with limited aerial visibility; and (iii) a high-performance GNSS receiver to precisely determine the occupied scanning-station positions within a global reference system, enabling rigorous georeferencing of the terrestrial dataset through a traditional traverse approach.

Post-processing integrated terrestrial and aerial point clouds into a single 3D dataset and applied classification tools to separate vegetation and bare ground, producing a Digital Elevation Model (DEM) of the site. The DEM was subsequently used to extract targeted 2D cross-sections along the slope–road system to support verification of the embankment geometry and to frame scenario-based assessments of residual rockfall hazard.

The case study demonstrates how complementary survey technologies can be effectively combined to deliver rapid, accurate, and operationally robust terrain models for alpine mass-movement emergencies. UAS mapping provides efficient coverage of large and impervious areas, while scanning total station data ensures high spatial resolution and completeness where aerial viewpoints are limited. GNSS-based georeferencing ensures that products are immediately interoperable with regional geodata and suitable for follow-up analyses, supporting decision-making in time-critical risk management contexts.

How to cite: Thuegaz, P., Bertolo, D., Stra, M., Agostino, F., and Rover, S.: Rockfall data: collection methods, analysis and use for hazard and risk assessments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-7837, https://doi.org/10.5194/egusphere-egu26-7837, 2026.

The ANR C2R-IA project (www.anrc2ria.fr) aims to develop reliable decision-support tools for the dynamic management of rockfall hazard. Its goal is to understand how meteorological forcing influences rockfall occurrence and to anticipate temporary increases in hazard in order to implement risk reduction measures. To this end, a predictive model of rockfall occurrence as a function of meteorological conditions is being developed using artificial intelligence tools (neural network training), which requires a comprehensive and well-labelled dataset. Several monitoring instruments have been deployed at the Saint-Eynard site (Grenoble, France). Among them, a permanent LiDAR scanner (PLS) acquires point clouds continuously, with one acquisition per hour, providing high temporal resolution representative of what could be used for operational monitoring or crisis management. An automated data-processing workflow has been developed in Python. It is based on a pairwise comparison of the clouds (Manceau et al., 2025) and includes the alignment of successive point clouds, filtering of points outside the cliff area, change detection using M3C2 distances computation, clustering with DBSCAN, and volume quantification of rockfalls using alphashapes. This well-structured processing has significantly reduced the detection threshold, identifying relief change of only 10 cm deep (compared to 40 cm previously; Le Roy et al, 2020) and 10 liters in volume, while the scanner is located approximately 1 km from the cliff. Depending on acquisition quality, the effective temporal resolution of detected rockfall events may range from one hour to several days. Combining relief-change detections with simultaneously deployed seismic monitoring should further refine event timing. The completeness of the event catalogue has therefore improved, increasing from fewer than 10 detected rockfalls per month to around 30. However, some false positives remain, mainly related to recurring artifacts despite preprocessing. To mitigate these errors, the previous pairwise comparison of the clouds has been refined to a multiple point-cloud comparison strategy, enabling the tracking of the temporal persistence of changes. This allows distinguishing changes corresponding to real rockfalls, which persist over time, from transient artifacts. This improvement leads to a more reliable and complete rockfall event database. It includes block shape ratios, identified failure mechanisms, and free-fall heights under overhanging sections, providing a suitable basis for future fusion with seismic data.

Manceau, L., Chanut, M.-A., Levy, C., Dewez, T., and Amitrano, D.: Enhancing Rockfall Detection Using Permanent LiDAR Scanner (PLS) Data and Automated Workflows at St. Eynard Cliff (Grenoble, France), EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6312, https://doi.org/10.5194/egusphere-egu25-6312

Le Roy, G., Helmstetter, A., Amitrano, D., Guyoton, F., & Le Roux-Mallouf, R. (2019). Seismic analysis of the detachment and impact phases of a rockfall and application for estimating rockfall volume and free-fall height. Journal of Geophysical Research: Earth Surface, 124, 2602-2622. https://doi.org/10.1029/2019JF004999

How to cite: Susini, S., Lévy, C., Chanut, M.-A., Dewez, T. J. B., and Amitrano, D.: Multiple comparisons of point clouds acquired by a permanent LiDAR (PLS) to improve the reliability of a rockfall event catalogue, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9717, https://doi.org/10.5194/egusphere-egu26-9717, 2026.

With the rapid development of the national economy, a large number of projects such as highways, railways, and hydropower stations have been constructed in the mid-western mountainous areas of China. The instability and rockfall of dangerous rock masses in engineering areas have become a serious and frequently occurring geological hazard. In order to reduce economic losses caused by the collapse of dangerous rock masses and ensure the safety of people's lives and property, research on the failure and disaster mechanism of dangerous rock masses, the kinematic characteristics during the collapse process, and risk management has become a major technical challenge that urgently needs to be addressed in the field of disaster prevention and mitigation. Taking the sudden falling-type collapse of dangerous rock masses on the steep cliff at Section K35+850 of the Lichuan-Wanzhou Expressway in western Hubei Province as the engineering background, this study employed unmanned aerial vehicle (UAV) photogrammetry technology to acquire the 3D point cloud and 3D realistic model of the dangerous rock masses on the subgrade steep cliff, and extracted the geometric characteristics of the residual dangerous rock masses. Based on the structural plane interpretation technology using the 3D model, the occurrence information of the dangerous rock masses and their controlling structural planes was obtained. Numerical simulations were performed using Rocfall and 3DEC software to deduce the movement trajectories and influence ranges of the residual dangerous rock masses, and a risk assessment was carried out by segmenting the falling area of the dangerous rock masses. Finally, reasonable disposal measures and technical suggestions for the risk management of the dangerous rock masses were put forward.

How to cite: Wang, B.: Research on the Hazard Assessment for Highway Slope Dangerous Rock Masses, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-2727, https://doi.org/10.5194/egusphere-egu26-2727, 2026.

The Baia Castle cliff, situated in the western sector of the Gulf of Naples (within the Campi Flegrei caldera, southern Italy), is subject to ongoing geomorphological processes and cliff retreat, which threaten both the archaeological heritage and coastal infrastructure. Additionally, the periodic uplift and subsidence activity, known as bradyseism, of the caldera is often accompanied by seismic events, which, as a secondary effect, can trigger rockfalls.

This study presents a comprehensive assessment of rock mass instability at the Baia Castle cliff through high-resolution UAV-based photogrammetry, semi-automatic point cloud analysis, and traditional field surveys. The combination of remote and in-situ methods, integrated with laboratory geomechanical analysis, enabled performing a detailed geostructural characterisation and kinematic analysis of the potential failure mechanisms affecting the tuffaceous cliff. Additionally, successive drone-derived DEMs before and after the June 2025 rockfalls that occurred in the area were compared to quantify the mobilised volumes and the cliff retreat. Furthermore, the failure events of 2025 were compared with the geostructural results preceding the rockfall and with the seismic site response analysis, to investigate any potential predisposing factors that localised the rockfall detachment.

How to cite: Massaro, L., Maresca, A., Mele, L., Flora, A., and Santo, A.: Rockfall hazard threatening archaeological sites in seismically active volcanic areas: the example of Baia Castle (Campi Flegrei), EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19753, https://doi.org/10.5194/egusphere-egu26-19753, 2026.