Wave dynamics and potential longshore sediment transport at Shippagan, Gulf of St Lawrence: insight into seasonal variability and extreme weather events
Bibliographic record
Abstract
This study examines the wave climate and its influence on potential Longshore Sediment Transport (LST) along the Shippagan coast of the Gulf of St. Lawrence, eastern Canada. Using a combination of in-situ observations and ERA5 reanalysis data, the research evaluates both seasonal and annual variations in wave dynamics and sediment transport, with particular attention to the role of extreme weather events. The wave climate shows pronounced seasonality, with higher wave activity in winter and lower activity in summer, while wave direction remains consistently from the southeast. Estimated potential LST rates, derived from renowned three empirical formulae, i.e., CERC, Kaczmarek and Komar show variability in both magnitude and direction over the three-year study period (2021-2023), reflecting the complex sediment dynamics of the region. According to CERC formula, the estimated net annual rates were -6.0 to 6.4 x10⁵ m3, while estimates from the Kaczmarek formula ranged from -6.1 to -1.8 x10⁵ m3 over the same period. The Komar formula provided net annual LST estimates ranging from -6.2 to 6.5 × 10⁵ m³, which are in close agreement with the CERC results. The predominant direction of net potential LST rates is towards southeast direction. Seasonal analysis highlights winter season waves are the dominant contributor to sediment transport, followed by spring and fall. Although, post-tropical storms contributed 5% to the total gross potential LST rates. Additionally, long-term analysis (1992–2023) reveals increasing trends in wave heights (0.4 cm year-1) and potential LST rates. These findings provide valuable insights into sediment dynamics, supporting improved modeling and prediction of the fate of nourished sediments, and contributing to the optimization of nature-based solutions like the Sand Engine for sustainable coastal management.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".