Joint influences of topographical and flow conditions on debris-flow erosion and deposition: insights from the 2020 Heixiluo event in the Dadu River Basin
Bibliographic record
Abstract
Debris flows have a high capacity for transporting sediments from hillslopes or steep channels into rivers via erosion and deposition. However, the lack of well-defined critical conditions for the two processes hampers the development of a fully physical model incorporating them. In this paper, we explore the topographical and flow conditions of erosion and deposition through a case study of the Heixiluo catchment in southwestern China, where a high-magnitude debris flow has deeply incised three consolidated landslide dams. The highest erosion rate was up to 1.3 m/min, or 568 m 3 per unit channel length. The channel topographical conditions controlled the local maximal erosion and the transition from erosion to deposition. We examine the relationship of erosion depth with unit discharge, channel slope, and channel bankfull width, and identify the critical values below which the deposition occurred. The channel slope and unit discharge have a higher correlation with the erosion depth than with the bankfull width. An outburst-flood erosion equation incorporating flow discharge, channel slope, and erodibility is integrated to compute the progressive channel erosion at two cross-sections. The back-calculated erodibility is two orders of magnitude higher than in other cases of water floods. This study provides new insights into the role and scale of channel topography and flow discharge on debris-flow erosion and deposition.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".