Wave Interactions with <i>Spartina alterniflora</i> on a Living Dyke Model: New Insights into the Use of Scaled Surrogate Meadows
Bibliographic record
Abstract
Physical modeling presents a useful tool for investigating the coastal protection function provided by marsh vegetation in a controlled, repeatable environment to inform the design of nature-based coastal protection strategies or nature-based solutions (NBS). To date, such studies have been used to investigate the influence of plant biophysical parameters and hydrodynamic conditions on wave attenuation, predominantly using surrogate vegetation due to the logistical challenges associated with live plant experiments. Most studies have been performed at or near full scale to avoid uncertainties associated with downscaling vegetation, particularly where Reynolds number similitude cannot be preserved. To address knowledge gaps related to the physical modeling of NBS at the small scale, experiments (1:4 scale) were conducted at the National Research Council of Canada’s Ocean, Coastal and River Engineering Research Centre, Ottawa, in collaboration with the University of Ottawa and the Institut National de la Recherche Scientifique, Quebec, Canada. This study aims to (1) investigate methods for downscaling live vegetation in laboratory settings and (2) compare various surrogate proxies for the semiflexible Spartina alterniflora salt marsh species. A solid volume fraction scaling approach was applied to select multiple stem width and stem density combinations representative of a prototype-scale S. alterniflora field while maintaining stem Reynolds numbers within a range representative of prototype conditions. Arrays of various surrogate elements were subjected to irregular waves (0.073 m < Hm0 < 0.225 m, 2.0 s < Tp < 3.2 s) at two water depths (d = 0.60, 0.75 m) across a fixed beach slope (1:20). Comparison of wave height transformations for the different surrogate array configurations indicated that downscaling of vegetation canopies is sensitive to stem diameter and spacing, even considering equivalent solid volume fractions. Flexible surrogate arrays performed similarly to rigid surrogate arrays in terms of irregular wave attenuation despite measurable deformation of the flexible element stems. This supports that wave transformations across S. alterniflora fields can be reasonably represented in scaled models using rigid cylinders for the range of array stem densities, wave conditions, and scale tested herein. This study presents novel critical guidance on small-scale physical modeling of wave–vegetation interactions to inform the design of coastal marsh–based NBS.
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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) | 0.000 | 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 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".