The effect of roughness elements on wind erosion: The importance of surface shear stress distribution
Bibliographic record
Abstract
Abstract Representation of surface roughness effects on aeolian sediment transport is a key source of uncertainty in wind erosion models. Drag partitioning schemes are used to account for roughness by scaling the soil entrainment threshold by the ratio of shear stress on roughness elements to that on the vegetated land surface. This approach does not explicitly account for the effects of roughness configuration, which may be important for sediment flux. Here we investigate the significance of roughness configuration for aeolian sediment transport, the ability of drag partitioning approaches to represent roughness configuration effects, and the implications for model accuracy. We use wind tunnel measurements of surface shear stress distributions to calculate sediment flux for a suite of roughness configurations, roughness densities, and wind velocities. Roughness configuration has a significant effect on sediment flux, influencing estimates by more than 1 order of magnitude. Measured and modeled drag partitioning approaches overestimate the predicted flux by 2 to 3 orders of magnitude. The drag partition is sensitive to roughness configuration, but current models cannot effectively represent this sensitivity. The effectiveness of drag partitioning approaches is also affected by estimates of the aerodynamic roughness height used to calculate wind shear velocity. Unless the roughness height is consistent with the drag partition, resulting fluxes can show physically implausible patterns. These results should make us question current assessments of the magnitude of vegetated dryland dust emissions. Representing roughness effects on surface shear stress distributions will reduce uncertainty in quantifying wind erosion, enabling better assessment of its impacts and management solutions.
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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 | 0.000 | 0.000 |
| 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".