A three‐dimensional numerical model investigation of the impact of submerged macrophytes on flow dynamics in a large fluvial lake
Bibliographic record
Abstract
Abstract Aquatic plants (macrophytes) are known to affect flow dynamics, contributing to flow resistance. Most studies on flow‐vegetation interactions are performed in laboratory flumes and focus on the flow field around plants, with little research at the level of vegetation patches in large aquatic ecosystems. In most hydrodynamic models, increased drag due to plants is modelled by increasing the Manning's n roughness coefficient. The objectives of this study were to: (1) develop a three‐dimensional hydrodynamic model (Delft3D) applicable to large water bodies including a novel approach to represent macrophyte resistance (modified k‐ ε turbulence closure model); and (2) compare the modelled flow with field measurements for different vegetation configurations and patch arrangements. Work was carried out in Lake Saint‐Pierre, a large fluvial lake of the St Lawrence River in Québec, Canada. Results showed a marked increase in residence time in the zone affected by macrophytes when using the modified k‐ ε turbulence closure model compared to the Manning's n approach, particularly near the bed. An improved agreement with field measured depth‐averaged velocity is obtained with this novel approach (correlation coefficient of 0.80 compared to 0.46 with Manning's n only). In addition, a good fit was obtained between vertical velocity profiles modelled and measured in the macrophyte zone. Sensitivity analysis revealed that the additional drag due to plants was closely associated with plant height, but that plant density played only a minor role in retarding velocities. These findings indicate that it is possible to accurately quantify both the horizontal and vertical flow modulations resulting from submerged vegetation in large fluvial systems. Considering that the Delft3D model is capable of approximating measured velocity magnitude, preserving the logarithmic shape throughout the water column and reaching near‐zero velocities without increasing the roughness coefficient, we recommend this modelling approach for future research on the impact of macrophytes on flow at the scale of vegetation patches in large water bodies comparable to Lake Saint‐Pierre.
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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 | 0.000 | 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 | 0.000 | 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) | 0.002 | 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".