Assessing the effects of sea level rise on ocean waves and surge events along the victorian coast
Bibliographic record
Abstract
• High-res wave-hydrodynamic hindcast developed for Victoria under SLR scenarios. • Captures cross-scale dynamics from open ocean to enclosed coastal bays. • SLR amplifies water level extremes; wave height response is spatially varied. • Dataset supports erosion, inundation, and shoreline hazard assessments. This study investigates nonlinear surges and extreme wind-wave patterns off the coast of Victoria by simulating sea level rise (SLR) scenarios of 0.5, 0.8, 1.0 and 1.4 meters alongside a 31-year hindcast (1990–2020) using the validated SCHISM-WWMIII coupled wave-circulation model. Model simulations were compared with observational data, confirming the accuracy of the results. Our findings indicate that sea levels along the Victorian coast have been rising at a rate of 1.46 × 10⁻ 2 cm/year, while wave heights in the Southern Ocean have also increased over time. However, the rate of wave height increase is lower along the Victorian coast compared to the Southern Ocean. Due to island blocking, mean wave heights in Bass Strait remain lower than those in the Southern Ocean, yet extreme water levels in the strait exceed those in the open ocean. The impact of SLR is most pronounced in the waters south of Tasmania, where maximum elevations exceed 1.2 meters under the 1.0-meter SLR scenario. SLR contributes to higher mean water levels and increased wave heights off the coast of Victoria, underscoring the complex interactions between rising sea levels and coastal wave dynamics. Wave direction and peak period were also examined, but their changes under SLR scenarios were found to be minimal. These findings highlight the importance of incorporating both SLR and wave dynamics into coastal hazard assessments to better understand future risks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".