A rockfall-induced glacial lake outburst flood, Upper Barun Valley, Nepal
Bibliographic record
Abstract
On April 20, 2017, a flood from the Barun River, Makalu-Barun National Park, eastern Nepal formed a 2–3-km-long lake at its confluence with the Arun River as a result of blockage by debris. Although the lake drained spontaneously the next day, it caused nationwide concern and triggered emergency responses. We identified the primary flood trigger as a massive rockfall from the northwest face of Saldim Peak (6388 m) which fell approximately 570 m down to the unnamed glacier above Langmale glacial lake, causing a massive dust cloud and hurricane-force winds. The impact also precipitated an avalanche, carrying blocks of rock and ice up to 5 m in diameter that plummeted a further 630 m down into Langmale glacial lake, triggering a glacial lake outburst flood (GLOF). The flood carved steep canyons, scoured the river’s riparian zone free of vegetation, and deposited sediment, debris, and boulders throughout much of the river channel from the settlement of Langmale to the settlement of Yangle Kharka about 6.5 km downstream. Peak discharge was estimated at 4400 ± 1800 m 3 s −1 , and total flood volume was estimated at 1.3 × 10 6 m 3 of water. This study highlights the importance of conducting integrated field studies of recent catastrophic events as soon as possible after they occur, in order to best understand the complexity of their triggering mechanisms, resultant impacts, and risk reduction management options.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 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 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".