Influence of sediment settling velocity on mechanistic soil erosion modeling
Bibliographic record
Abstract
We report on a series of soil erosion experiments performed on the 2‐m × 6‐m EPFL erosion flume. Total sediment concentrations and the concentrations of seven size fractions (<2, 2–20, 20–50, 50–100, 100–315, 315–1000, and >1000 μ m) were measured during 14 high‐intensity (47.5–52.5 mm/h) rainfall experiments on three different slopes (2.2–12.4%). The short‐time and long‐time analytical solutions for the Hairsine‐Rose erosion model were rewritten to account for infiltration. The newly collected data were used to test the model under conditions that had not been explored before (steeper slopes, infiltration, more realistic soil composition). The analytical solutions could predict the observed total sediment concentration well. However, the observed sediment concentrations for the individual size classes could be predicted only when adjusted settling velocities were used. The adjusted settling velocities were estimated through manual optimization. The optimized settling velocities for the smallest and midsize particles (<100 μ m) were larger than calculated from Stokes' law or measured in a 0.47‐m tube, while the optimized settling velocities for the largest particles (>315 μ m) were smaller than measured. The effective settling velocities could also be calculated from the calculated amount of material in the shield of each size class at the end of the experiments. This calculated settling velocity distribution agreed very well with the optimized settling velocities. This study moves the Hairsine‐Rose model another step closer to an operational soil erosion model for field applications.
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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 | 0.001 | 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 | 0.001 | 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".