Sediment dynamics related to the triggering of debris flows in different alpine watersheds
Bibliographic record
Abstract
Debris flows initiate by a critical combination of abundant sediment, steep inclination, and water. The latter is mostly provided by rainfall that can lead to landslides at the hillslope or along the channel and/or erosion and bulking of sediment due to increased runoff. Location of sediment sources and channel recharge are related to short- and long-term geomorphological processes within the watershed. Up to now, there are only a few studies investigating sediment dynamics in high alpine watersheds that are regularly affected by debris flows. In this contribution we report of our ongoing efforts to monitor sediment dynamics and debris-flow activity in three very different watersheds in the Austrian Alps. We use a combination of remote sensing and in-channel monitoring techniques including UAV, air-borne and terrestrial laser scanning before and after debris-flow events. We find that debris-flows frequency and volumes are strongly related to movement rates of landslides present in the watershed. At high movement rates, most of the channel refill occurs within the time scale of hours. In the absence of active landslides, debris-flow activity is limited by rainfall-triggered embankment failures along the channel and continuous transfer of hillslope sediment into the channel. In the steepest and smallest monitored watershed, active landslides and continuous surface erosion from landslide scars leads to a high frequency of debris flows of all magnitudes, even in the absence of rainfall. Our study shall provide the basis for a more complete modeling framework for a better prediction of debris flows now and in a future climate.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".