Exploring the flow evolution of the Chamoli event (Uttarakhand, India) of 7 February 2021: From geomorphological mapping to multi-model computer simulations
Bibliographic record
Abstract
On 7 February 2021, a 25 million m³ rock/ice avalanche in Uttarakhand, northern India, developed into a far-reaching and devastating debris flow/debris flood, which we refer to as the ‘Chamoli event’. Based on an extensive remote sensing-based geomorphological mapping campaign, the key mechanisms and characteristics of this process chain are largely, but not yet fully understood. Numerical mass flow simulations can help confirm or reject hypotheses regarding spatiotemporal aspects of flow evolution, its magnitude and dynamics, and therefore contribute to a better process understanding. More broadly, geomorphological mapping and numerical modelling of the Chamoli event help us to gain insights of the extent to which we are able to accurately simulate complex high-mountain geohazard process cascades. Such an understanding is invaluable for predictive modelling efforts targeted at informing disaster risk reduction strategies. In the present work, we back-calculate the flow dynamics of the Chamoli event with three state-of-the-art simulation models operating at different levels of complexity: (i) the one-phase mixture model RAMMS; (ii) the two-phase model GeoClaw, and (iii) the three-phase model r.avaflow. Input parameter sets are optimized against detailed reference data such as mapped trimlines and boulder locations, flow velocities and discharges obtained from video recordings, and erosion/deposition patterns derived by differencing pre- and post-event digital terrain models. The main aims of the study are to: (i) better understand the mechanisms of flow evolution of the Chamoli event; (ii) evaluate the level of model complexity that is necessary for accurating reproducing specific known characteristics of the process chain; and (iii) learn more about the sensitivity of model outputs to differences in initial conditions and model parameters, where these remain uncertain. The findings will facilitate the design of predictive modelling campaigns for hazard mapping purposes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".