Pre-collapse motion of the February 2021 Chamoli rock–ice avalanche, Indian Himalaya
Bibliographic record
Abstract
Abstract. Landslides are a major geohazard that cause thousands of fatalities every year. Despite their importance, identifying unstable slopes and forecasting collapses remains a major challenge. In this study, we use the 7 February 2021 Chamoli rock–ice avalanche as a data-rich example to investigate the potential of remotely sensed datasets for the assessment of slope stability. We investigate imagery over the 3 decades preceding collapse and assess the precursory signs exhibited by this slope prior to the catastrophic collapse. We evaluate monthly slope motion from 2015 to 2021 through feature tracking of high-resolution optical satellite imagery. We then combine these data with a time series of pre- and post-event digital elevation models (DEMs), which we use to evaluate elevation change over the same area. Both datasets show that the 26.9×106 m3 collapse block moved over 10 m horizontally and vertically in the 5 years preceding collapse, with particularly rapid motion occurring in the summers of 2017 and 2018. We propose that the collapse results from a combination of snow loading in a deep headwall crack and permafrost degradation in the heavily jointed bedrock. Despite observing a clear precursory signal, we find that the timing of the Chamoli rock–ice avalanche could likely not have been forecast from satellite data alone. Our results highlight the potential of remotely sensed imagery for assessing landslide hazard in remote areas, but that challenges remain for operational hazard monitoring.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".