Debris-flow activity and sediment dynamics in the landslide-influenced Lattenbach catchment, Austria
Bibliographic record
Abstract
Deep seated landslides are common phenomena in Alpine areas. In case of a direct connectivity with the channel system, the catchment’s sediment yield and the probability of other forms of mass wasting processes such as debris flows may be increased significantly. Up to now, sediment dynamics related to deep-seated landslides and debris flows have not been quantified. The Lattenbach catchment (basin area 5,3 km², relief 2134 m) in Grins (Tyrol, Austria), is an example for an active DF-site, where there is geomorphological evidence of deep-seated landslide activity. In this study we shed light (1) on the location and size of active landslides in the catchment, that may deliver sediment to the channel system. Furthermore, we want to (2) quantify the contributed sediment volumes by these landslides (3) and estimate the exported sediment by debris flow. We apply an image correlation algorithm to high resolution ALS and TLS terrain models of derived over a period of 14 years to calculate surface movement rates within the catchment and locate deep seated landslide activity. We further assess the sediment yield of these landslides to the channel system and relate that with DF-volumes measured by a monitoring station at the catchment outlet. We find that there are five deep-seated landslide bodies directly connected to the channel system in the catchment. These are the largest source of sediment and significantly increases the overall sediment yield of the catchment. Our study shall contribute to the limited knowledge about the importance of deep-seated landslides for sediment dynamics and debris-flow activity, as their presence is predicted to be more frequent in the wake of global warming.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".