Thame Valley Glacial Lake Outburst Flood 2024: Causes, impacts and future risks
Bibliographic record
Abstract
The Thame Valley Nepal GLOF on August 16, 2024, caused widespread destruction—damaging homes, schools, health facilities, and bridges, displacing 135 people, and destabilising the valley further. In response, Nepal’s National Disaster Risk Reduction and Management Authority (NDRRMA), in collaboration with ICIMOD, conducted a field investigation to examine the causes, assess damage, and identify future risks. This study aims to inform effective disaster management and mitigation strategies for reducing the long-term threats posed by GLOFs in the HKH region. The Thame Village disaster was triggered by the successive breaching of two glacial lakes, Upper and Lower Ngole Cho, located about 10 km upstream. A rock avalanche into Upper Ngole Cho caused a displacement wave that eroded its moraine dam, which then triggered the failure of Lower Ngole Cho. The breach at Lower Ngole Cho eroded up to 22 m in height and 51 m in width of its end moraine, generating a powerful GLOF that devastated Thame and surrounding areas. The flood evolved into a hyper-concentrated flow, carrying debris over 80 km along the Thame and Dudh Koshi rivers. Temporary ponding and repeated breaches intensified erosion, leading to massive destruction of farmland, hotels, schools, and hydropower facilities in Thame. Sediment deposition, severe erosion, land subsidence, and tension cracks further destabilised the village. Risk assessment shows Upper Ngole Cho remains highly vulnerable to future avalanches and overflows, while Rindhi Cho is classified as high risk due to unstable ice-dammed conditions, and Homey Cho as moderate risk requiring detailed investigation. Recommendations Immediate measures: Fill and protect tension cracks, stabilise riverbanks, and channelise river flow using berms, spurs, and check dams upstream of the village. Monitoring: Install hydrological and meteorological stations to support real-time flood risk assessment and early warning systems. Risk mitigation: Use high-resolution satellite imagery to track sediment changes; implement robust riverbank protection and erosion control. Long-term planning: Develop a comprehensive flood risk mitigation plan integrating structural measures (levees, embankments) with non-structural approaches (community preparedness, emergency response). Infrastructure resilience: Use ultra-high-resolution survey data to guide hydropower design and land-use planning, ensuring sustainable development and reduced exposure to hazards. The event underscores the extreme vulnerability of Thame Valley to GLOFs and the urgent need for integrated, long-term disaster risk management.
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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".