Role of Future Reef Growth on Morphological Response of Coral Reef Islands to Sea-Level Rise
Bibliographic record
Abstract
Coral reefs are widely recognised for providing a natural breakwater effect that modulates erosion and flooding hazards on low-lying sedimentary reef islands. Increased water depth across reef platforms due sea-level rise (SLR) can compromise this breakwater effect and enhance island exposure to these hazards, but reef accretion in response to SLR may positively contribute to island resilience. Morphodynamic studies suggest that reef islands can adjust to SLR by maintaining freeboard through overwash deposition and island accretion, but the impact of different future reef accretion trajectories on the morphological response of islands remain unknown. Here we show, using a process-based morphodynamic model, that, although reef growth significantly affects wave transformation processes and island morphology, it does not lead to decreased coastal flooding and island inundation. According to the model, reef islands evolve during SLR by attuning their elevation to the maximum wave runup and islands fronted by a growing reef platform attain lower elevations than those without reef growth, but have similar overwash regimes. The mean overwash discharge across the island crest plays a key role in the ability of islands to keep up with SLR and maintain freeboard, with a value of (10 l m s) separating island construction from destruction. Islands, therefore, can grow vertically to keep up with SLR via flooding and overwash if specific forcing and sediment supply conditions are met, offering hope for uninhabited and sparely populated islands. However, this physical island response will negatively impact infrastructure and assets on developed islands.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".