Nature-Based Solutions for Coastal Protection: A Multi-Scale Investigation of Wave-Vegetation Interactions
Bibliographic record
Abstract
Nature-based solutions (NBS) are promising strategies for protecting vulnerable coasts in the context of climate change, utilizing the coastal protection capabilities of natural ecosystems for engineering applications. The ability of coastal marsh vegetation to attenuate wave energy and prevent coastal erosion has been acknowledged for decades, however, consideration for their use in coastal protection strategies is presently limited, particularly in Canada due to a lack of engineering guidelines and limited available research considering region-specific variables. Physical modelling presents a useful tool for investigating the coastal protection function provided by marsh vegetation in a controlled, repeatable environment, which can ultimately inform the design of nature-based coastal protection strategies. To date, such studies have investigated the influence of plant biophysical parameters (stem flexibility, width, and height) and hydrodynamic conditions (wave height, wave period, and plant submergence) on wave attenuation. These studies have used either live vegetation, requiring full-scale wave testing, or surrogate vegetation, which allows simplified testing at either full- or reduced-scale. Overall, live vegetation studies have been limited in the variety of saltmarsh plants considered, with few studies considering plant species native to the Canadian coastline. Several physical modelling studies have been performed using surrogate plants, however, methods of surrogate development for flexible vegetation or reduced-scale testing are not yet well developed. This thesis aims to address knowledge gaps pertaining to the use of marsh vegetation in coastal protection strategies, particularly through the development of experimental methods with both live and surrogate plants. A full-scale flume study with live vegetation was performed to develop fundamental knowledge of wave-vegetation interactions for Spartina alterniflora and Spartina patens, two salt marsh species native to Canada’s Atlantic coast. S. alterniflora was observed to demonstrate a resistance strategy in response to hydrodynamic forcing, versus the avoidance strategy of S. patens, supporting complementary functioning of the two species if utilized together in coastal protection schemes. Observations of plant properties and stem bending from live plant tests were subsequently applied in the development of a small-scale flume study, which examined wave attenuation associated with a downscaled S. alterniflora meadow in the configuration of a “living dyke” structure. Wave damping induced by surrogate vegetation was observed to be minimal for the tested wave conditions (0.073 m < Hm0 < 0.225 m, 2.0 < Tp < 3.2 s, 1:4 scale) and beach slope (1V:20H), with wave height evolution dominated by wave shoaling and breaking. Several methods were considered for modelling the S. alterniflora meadow at reduced scale, and results demonstrated a sensitivity to surrogate diameter but not flexibility. The development of robust experimental methods for investigating the performance of nature-based coastal infrastructure is essential for the establishment of appropriate design conditions. The scale series approach of this thesis supports such methodological advancements and is expected to make preliminary contributions to design guidance on coastal marsh-based NBS and provide critical direction for future studies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".