Field insights from the August 16, 2024 Thame glacial lake outburst flood in Nepal: how geomorphology can affect a cascading hazard chain
Bibliographic record
Abstract
Glacial lake outburst floods (GLOFs) are devastating to downstream communities in high mountain Asia. GLOF hazards are difficult to characterize because of the complexity and variability in factors that control susceptibility, such as warming temperatures, rainfall, and slope instability. Compounding this uncertainty is the potential for downstream hazards such as landslide dam outburst floods. The August 16, 2024 Thame GLOF in the Himalaya illustrates how local geomorphology can influence a cascading hazard chain. Initiating in the Thyanbo Lakes near the Tashi Lapcha Pass in the Solukhumbu region of Nepal, the Thame GLOF destroyed at least houses, an elementary school, and a medical clinic in the village of Thame, as well as displacing 135 people due to the debris inundation and burial of a majority of the town’s farmland. As part of a regional project with the Asian Development Bank, BGC Engineering and partnering organizations including Nepal’s National Disaster Risk Reduction and Management Authority and the International Centre for Integrated Mountain Development, visited Thame in December 2024 to assess GLOF risk from the remaining lakes and to inform reconstruction of the village. The team observed several characteristics of the watershed’s geomorphology that affected the triggering conditions and amplified the consequences of this GLOF. First, the GLOF burst through the lower of two adjacent glacial lakes from rapid water displacement, but not outburst, from the upper lake. Second, debris fan and rock avalanche deposits on both sides of the valley floor formed a constriction which ponded during the event, resulting in increased knickpoint erosion, sediment supply, and inundation of Thame. Third, the GLOF down-cut up to 10 meters through glaciolacustrine deposits at the terminus of the valley, triggering retrogressive landsliding that now poses risk to the remaining buildings in Thame. The Thame GLOF highlights the importance of considering geomorphology in assessing the potential magnitude and humanitarian risks of GLOFs, as well as the cascading hazard chain that can develop. Site-specific geomorphic and geologic studies will continue to be valuable in building our understanding of GLOFs and how to assess risk to downstream communities.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".