Modeling lake outburst and downstream hazard assessment of the Lower Barun Glacial Lake, Nepal Himalaya
Bibliographic record
Abstract
Climate change-driven retreat of glaciers is producing thousands of glacial lakes across mountain regions. These lakes generally grow, coalesce into larger lakes that may produce increased downstream hazards and risks due to glacial lake outburst floods (GLOFs). This study assesses such hazards of Lower Barun Lake located near Mount Everest, Nepal. We model a series of scenarios, including two potential avalanches that enter the lake from the surrounding slope and eight potential GLOFs from the lake. To evaluate the susceptibility of the frontal moraine to overtopping, we characterize the initial avalanche-induced surge of water over the moraine caused by the kinetic energy of arriving masses and possible tsunami-like events. Further, we present physical hydrodynamic models that reveal the hazard from the potential overtopping and GLOF events along the Barun-Arun river valley. Special attention is given to analyze the flow hydraulics at six downstream settlements. To estimate potential impacts at each location, two extreme-magnitude, two high-magnitude, two moderate-magnitude, and two low-magnitude GLOFs were hydraulically evaluated for the present lake dimension and the modeled future growth of the lake. As with most hydrological processes, the magnitude and frequency of GLOFs from Lower Barun Lake have an inverse, albeit uncertain, relationship, but the potential impacts on people and infrastructure are extremely sensitive to the events’ magnitude. The flow dynamics results indicate that an overtopping flood without erosion of the damming moraine causes minimal impact in the valley. The extreme-magnitude and high-magnitude GLOF cases, where the moraine is incised, have a larger impact but differ greatly in magnitude at each of the downstream settlements. The moderate-magnitude and low-magnitude GLOFs, while the most frequent type, have limited volume and peak discharge, causing less impact downstream. Our calculations only portray the part of the hydrograph representing lake overfill due to a volume of ice or rock entering the lake, and the volume of the lake that could drain from a breach of the damming moraine down to specified depths over specified time periods.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".