Estimating hydrodynamic roughness in a wave‐dominated environment with a high‐resolution acoustic Doppler profiler
Bibliographic record
Abstract
Hydrodynamic roughness is a critical parameter for characterizing bottom drag in boundary layers, and it varies both spatially and temporally due to variation in grain size, bedforms, and saltating sediment. In this paper we investigate temporal variability in hydrodynamic roughness using velocity profiles in the bottom boundary layer measured with a high‐resolution acoustic Doppler profiler (PCADP). The data were collected on the ebb‐tidal delta off Grays Harbor, Washington, in a mean water depth of 9 m. Significant wave height ranged from 0.5 to 3 m. Bottom roughness has rarely been determined from hydrodynamic measurements under conditions such as these, where energetic waves and medium‐to‐fine sand produce small bedforms. Friction velocity due to current u*c and apparent bottom roughness z0a were determined from the PCADP burst mean velocity profiles using the law of the wall. Bottom roughness kB was estimated by applying the Grant‐Madsen model for wave‐current interaction iteratively until the model u*c converged with values determined from the data. The resulting kB values ranged over 3 orders of magnitude (10−1 to 10−4 m) and varied inversely with wave orbital diameter. This range of kB influences predicted bottom shear stress considerably, suggesting that the use of time‐varying bottom roughness could significantly improve the accuracy of sediment transport models. Bedform height was estimated from kB and is consistent with both ripple heights predicted by empirical models and bedforms in sonar images collected during the experiment.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".