Three dimensional hydrodynamic modelling of the impact of macrophytes in Lake Saint-Pierre
Bibliographic record
Abstract
Aquatic plants (macrophytes) are known to affect flow dynamics by contributing to flow resistance. Most studies on flow-vegetation interactions are performed in laboratory flumes and focus on the flow field around simulated plants. Little research is done at the level of real vegetation patches in water bodies such as Lake Saint-Pierre (LSP), a large fluvial lake of the Saint-Lawrence River in Quebec, Canada. Although some two-dimensional (2D) hydrodynamic models have included additional drag due to macrophytes in natural rivers through an increase in roughness coefficient (Manning’s n), these studies do not well represent the near-zero velocities observed in dense macrophyte zones such as those of LSP. Furthermore, because most submerged plants are flexible and have different growth forms and heights, a three-dimensional (3D) approach may better represent their true impact on the flow field. The objective of this study is to develop a 3D hydrodynamic model (Delft3D) of a large-scale field site with abundant macrophytes (LSP) and investigate to what extent the flow and residence time are affected by macrophytes. Two macrophyte simulation approaches (trachytope and modified k-ε turbulence closure model) were first compared to laboratory experiments from the literature to determine how best to simulate the macrophyte impact on flow dynamics. Results indicated that the modified k-ε turbulence approach better predicted the variability of the flow field. This approach was then used to study the zone at the mouth of the Saint François River in LSP, where an extensive macrophyte zone is present annually. 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 current reduction. These findings indicate that it is possible to accurately quantify both the horizontal and vertical differences in flow resulting from submerged vegetation in large fluvial systems.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".