Impact of wave–current coupling on the bottom boundary layer in Bohai Bay
Bibliographic record
Abstract
Sediment resuspension primarily occurs within the bottom boundary layer (BBL) of water bodies, particularly in silty coastal environments, and helps form the fluid mud layer (FML). In this study, we report data on the water level, waves, currents, and suspended sediment concentration (SSC) collected from the Bohai Sea over one year, at a vertical resolution of 4 cm, by using the acoustic wave and current profiler, acoustic Doppler current profiler, and an acoustic backscattering system. The aim was to investigate the mechanisms of formation and disappearance of the FML as driven by wave–current interactions on silty seabeds The findings revealed a thin and stable FML within 4–12 cm of the seabed in shallow waters. Strong waves contributed more significantly to sediment resuspension than strong currents. Moreover, the SSC near the seabed was generally governed by the currents, while waves were predominant in this regard in stormy conditions. The index of intensity of the bottom shear β—defined as the ratio of the wave–current-induced shear stress to the critical shear stress—was identified as a sensitive indicator of variations in the SSC. Significant sediment resuspension occurred in case of the coupling of large wave and current events on the fine-grained seabed, when the value of β exceeded 10 for more than 20 h and the Rouse number persisted below 0.01 for over 30 h. Following the storm (β < 10), the sediment gradually accumulated, and this led to the formation of the FML. Prolonged periods in which the value of β surpassed 10 for over 10 h while that of the Rouse number remained below 0.01 for more than 30 h resulted in considerable sediment resuspension and the destruction of the FML. Our results highlight the profound impacts of wave–current interactions on the formation and disappearance of the FML within the BBL in silty, shallow marine environments. The work here offers critical insights into the dynamics of fine-particle sediment, and provides suggestions for mitigating the negative effects associated with the FML.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".