Glacier calving and moraine collapse triggered the glacial lake outburst flood in South Lhonak Lake, Indian Himalaya
Bibliographic record
Abstract
Abstract Glacial lake outburst floods (GLOFs) are destructive and threaten downstream communities in the Himalaya. Through satellite image analysis, we investigate the 2023 GLOF event at South Lhonak Lake, Sikkim, India, focusing on the lake’s historical evolution and the geomorphic controls that caused the GLOF. Multi-temporal data from 10 satellite missions revealed a significant increase in glacier surface lowering from −0.19 m year −1 (1970–1983) to −0.87 m year −1 (2015–2023). Initially a supraglacial lake in 1962, it evolved into a moraine-dammed lake by 1983 and expanded 12-fold from 0.11 km 2 (1962) to 1.4 km 2 (2023). Between 27 September and 6 October 2023, satellite imagery revealed an unusually strong retreat of 49.6 ± 7.1 m, indicating glacier calving and presence of massive icebergs visible on the lake. Our analysis shows 7 large glacier retreat and calving events between 2017 and 2023, further weakening the lateral moraines. This, combined with intermittent rainfall triggered the moraine dam collapse, leading to the GLOF. These findings emphasize the need for long-term monitoring of Himalayan glacial lakes.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".