Role of Waves in Forecasting Extreme Coastal Flooding under a Warming Climate: Insights from Norfolk, Virginia
Bibliographic record
Abstract
Coastal flooding occurs when the total water level (TWL) exceeds that of the natural or built coastal defense. Operational models used to forecast the TWL typically consider the combined effect of mean sea level (MSL), high tide, and storm surge. However, the extent to which storm waves run up the beach or structure has traditionally been neglected. Several studies argue that excluding wave run-up could lead to a significant underestimation of the resulting coastal flooding. Others, in direct opposition, argue that extreme wave run-up metrics artificially inflate the estimated TWL. Here, these contradictory findings are addressed by quantifying the contribution of wave runup to coastal flooding at Norfolk (VA, USA) during Hurricane Irene (2011) using the Delft3D FM and FUNWAVE numerical models. To assess the impact of a warming climate, the analysis considered a range of sea level rise values and hurricane intensities, including a combined scenario that represents a 3°C increase in sea surface temperature by the year 2100. Wave run-up contributed the most (48% on average) to the TWL at the coast relative to the existing MSL but accounted for <20% of the inundated area, with the remainder flooded by the tide, surge, and SLR. These findings confirm that while wave run-up might play an important role in the TWL and damage along the coast, its contribution to the overall flood extent will be secondary when measured against other factors like storm surge and SLR for low-lying areas with broad continental shelves, like Norfolk. In addition, this study demonstrates the utility of relating the characteristics of each driver of coastal flooding to a target warming level in providing a physical basis for the magnitudes considered when forecasting the compounding consequences of climate change.
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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".