Rapid fluvial remobilization of sediments deposited by the 2021 Chamoli disaster, Indian Himalaya
Bibliographic record
Abstract
Abstract High-magnitude mass flows can have a pervasive geomorphological legacy, yet the short-term response of valley floors to such intense disturbances is poorly known and poses significant observational challenges in unstable landscapes. We combined satellite remote sensing, numerical modeling, and field observations to reconstruct the short-term geomorphological response of river channels directly affected by the 7 February 2021 ice-rock avalanche–debris flow in Chamoli district, Uttarakhand, India. The flow deposited 10.4 ± 1.6 Mm3 of sediment within the first 30 km and in places reset the channel floor to a zero-state condition, requiring complete fluvial re-establishment. In the 12 months post-event, 7.0 ± 1.5 Mm3 (67.2%) of the deposit volume was removed along a 30-km-long domain and the median erosion rate was 2.3 ± 1.1 m a–1. Most sediment was removed by pre-monsoon and monsoon river flows, which conveyed bedload waves traveling at 0.1–0.3 km day–1 and sustained order-of-magnitude increases in suspended sediment concentrations as far as 85 km from the event source. Our findings characterize a high-mountain fluvial cascade with a short relaxation time and high resilience to a high-magnitude geomorphological perturbation. This system response has wider implications, notably for water quality and downstream hydropower projects, which may be disrupted by elevated bedload and suspended sediment transport.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 source (direct Gemma or distilled Codex), 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".