Fluidization and snow cover effects in rock-ice-snow avalanches: Lessons from Piz Cengalo, Fluchthorn, and Piz Scerscen events
Bibliographic record
Abstract
This study investigates the frictional dynamics of geophysical granular flows by analyzing three significant rock-ice avalanches: 2017 Piz Cengalo, 2023 Fluchthorn and the 2024 Piz Scerscen. These events, characterized by varying terrain features, material compositions, entrainment material and flow regimes, provide a comprehensive basis for examining the influence of snow cover and fluidization on avalanche behavior. We developed a Voellmy-type rheological model that incorporates snowpack-induced friction variations, the physical properties of sliding materials, and fluidization effects. Our findings indicate that the presence of snowpack decreases Coulomb friction and reduces momentum loss due to ground roughness, necessitating adjustments in rheological parameters to accurately represent the friction of dense granular mixtures on snow-covered terrains. By quantifying the relationship between fluctuation energy and fluidization-induced lubrication, we validated our model against recorded avalanche cases. This approach effectively captures the fluidization process, where shear-induced fluctuation energy disrupts particle bonds and offsets granular potential energy, leading to transitions in flow regimes. These macroscopic flow states significantly influence avalanche characteristics, including deposit patterns, runout distances, powder avalanche formation, and impact pressures. Our work enhances the theoretical foundation of geophysical granular flow modeling and offers promising perspectives for assessing risks associated with rock-ice-snow avalanches in high-altitude regions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".