Spatial and Temporal Variation in Wave Overtopping Across a Coastal Structure Based on One Year of Field Observations
Bibliographic record
Abstract
Coastal managers worldwide must prepare for changes in annual wave overtopping events due to climate change and sea-level rise. Research often assesses overtopping discharges by extreme events at a sea wall crest, typically using data from physical models or empirical rules based on scaled experiments. Here, we analyse a unique 1-year field dataset of coastal wave overtopping, from SW England, to determine the number of individual waves, regardless of their size, overtopping two locations across a coastal structure. The coastal conditions causing the most frequent overtopping differ from those driving it landward, complicating hazard communications for multiuse infrastructure. These data are the first field observations covering a year of tide, wave and wind conditions that cause overtopping of a vertical sea wall. Storms have a minimal (<2%) contribution to the number of tides associated with overtopping and the prevailing wave direction was not that associated with most overtopping events. Overtopping histograms identify the variability in the most likely time of overtopping relative to high tide for different wave categories across the structure. Sea-level rise, beach lowering and climate change will influence the annual number of waves overtopping in future. Change will be a complex balance between overtopping by different wave categories due to their likelihood of coincidence with water levels that do not cause depth-limitation over the foreshore or (partial-)reflection off the structure. It is possible the number of waves overtopping will reduce at the crest of a sea wall, while more of those overtopping waves will travel further inland.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".