Interrelated Coastal Flooding, Erosion, and Groundwater Salinization on a Barrier Island During Hurricane Fiona
Bibliographic record
Abstract
Abstract Coastal flooding transforms barrier island morphology and rapidly salinizes freshwater lenses that support island populations and ecosystems. Climate change is expected to increase coastal flood risks, and understanding future island vulnerability requires understanding erosion and salinization processes and their feedbacks. This study investigates how island morphology and groundwater salinity distributions on Hog Island, Prince Edward Island, Canada, responded to high water levels during post‐tropical storm Fiona (24 September 2022), the costliest hurricane to make landfall in Canadian history. Island morphology was monitored with drone‐based LiDAR, and beach groundwater dynamics were investigated with frequency‐domain electromagnetic geophysics surveys and monitoring wells. Comparing pre‐storm and post‐disturbance data revealed high dune scarping that thinned the ocean‐side foredune by 12.3 m on average and reduced the total island volume by 12%. Beach groundwater levels and electrical conductivity increased by up to 2 m and 19 mS cm −1 , respectively, and the freshwater lens was lost under the eroded foredune. Measurements 9 months after the storm revealed early‐stage recovery of the foredune; however, high dune scarping prolonged recovery, and the island volume only increased by 1%. Without a stable ocean‐side foredune, the landward extent of recurring coastal flooding increased and limited freshwater flushing and aquifer recovery. Results indicate that rapid erosion from extreme coastal storms shifts coastal boundaries, salinizes formerly freshwater resources, and limits freshwater recovery. These findings emphasize the importance of understanding the often‐overlooked interconnections between coastal flooding, erosion, and groundwater salinization to effectively manage coastal resources in an age of environmental change.
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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 | 0.001 | 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 | 0.001 |
| 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 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".