Assessing the resiliency of a coastal foredune in a changing climate
Bibliographic record
Abstract
The north shore of Prince Edward Island (PEI) is one of the most vulnerable coastlines in Canada to climate change impacts due to tectonic subsidence exacerbating effects of global mean sea level rise (GMSLR), increase in the frequency and intensity of extreme storms including remnants of hurricanes tracking from the south-western Atlantic, and a reduction in protective winter sea ice, which typically fastens to the beach and buffers high energy winter waves. The barrier islands along the north shore serve as a protective buffer to coastal infrastructure and critical ecosystems from storm surge and wave impacts. The resiliency of a coastal barrier depends on the severity of impact from major storms, and the ability for the barrier to recover between subsequent storms with sediment from offshore, nearshore and alongshore sources. Following storm impact, sediment is transported from the nearshore to the beach face via low energy waves, and subsequently towards the foredune during episodic events of high wind energy. Quantification of post-storm recovery gives insight into the past, and future resiliency of a foredune system with regards to the prospect of a changing climate, including the increase in frequency of extreme storms. Lack of consistent, and morphologically accurate boundaries can render quantifications of sediment exchange inaccurate, and unreliable for projecting future changes to the system. There is a need to identify an unbiased, process-based dune toe, which delineates the end of the backshore and beginning of the foredune. This position is especially important when considering exchanges in sediment, including the changes in foredune and beach volume preceding and following a storm event. In this Dissertation, the impact of storms at Brackley Beach (BB) in Prince Edward Island (PEI) are quantified (Chapters 2 & 3), new techniques for characterizing beach-dune boundaries and sediment exchanges are introduced (Chapters 3 & 4), all known boundary delineation methods are critically analysed (Chapter 4), and decadal-scale evolution of the barrier is characterized (Chapter 5). In Chapters 2 and 3, volumetric loss following major storms Post-Tropical Storm Dorian (SD) in 2019 and Hurricane Fiona (HF) in 2022 are quantified with average alongshore losses of 1.7 and 42 m3 m-1 to the foredune. Additionally, Chapter 3 introduces a new method for remotely monitoring sediment exchanges using citizen science imagery. In Chapter 4, well-recognized dune toe delineation methods are compared throughout the SD and HF impact and recovery time series. An adaptation to the Relative Relief (RR) method, the modified RR method (MRR) proved to both accurately quantify sediment exchanges between the beach and dune, and outlined a process transition zone from the beach to the backshore. Finally, in Chapter 5, historical and present resiliency of the BB foredune system is modelled, finding that in the future BB will be unlikely to make a full recovery following HF due to climate change altering the equilibrium of storm impact and recovery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".