Quantitative assessment of the erosion and deposition effects of landslide-dam outburst flood, Eastern Himalaya
Bibliographic record
Abstract
Abstract Both regular flow and infrequent outburst floods shape the mountain landscape, but their relative contributions have been widely debated, in part due to the paucity of quantitative data on historical outburst floods. In June 2000, an outburst flood was triggered by a landslide-dam failure in a rapidly exhumed region of the Eastern Himalaya. To investigate the role of this kind outburst flood on landscape evolution, we employ topographic differencing, satellite imagery, and 2D hydraulic simulations to quantify the equivalent erosion and deposition within ~ 80 km flood route downstream of the breach. The flood lasted for ~ 10 h, with a peak discharge of 10 5 m 3 /s, leading to average erosion of 10 m, and contributed ~ 1–2 × 10 3 times more sediment than over long-term mean fluvial processes. The flood produced extensive lateral erosion, which triggered a threefold widening of the valley floor and abundant subsequent landslides. The ubiquitous boulder bars deposited in the channel inhibited incision, and facilitated lateral erosion after the flood. The resulting channel configuration and extensive bank erosion continue to affect fluvial dynamics until the next catastrophic flood that remobilizes the boulders. Our quantitative findings highlight the profound importance of recurrent outburst floods for gorge development and landscape evolution in Eastern Himalaya.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".