Impacts of Hurricane Winds and Precipitation on Hydrodynamics in a Back‐Barrier Estuary
Bibliographic record
Abstract
Abstract During extreme storms, both wind‐driven changes in water levels and intense precipitation can contribute to flooding. Particularly on low‐lying coastal plains, storm‐driven flooding can cover large areas, resulting in major damage. To investigate the roles of rainfall and storm surge on coastal flooding, a coupled flow‐wave model (Delft3D‐SWAN) that includes precipitation is used to simulate two major storm events. The modeling system is applied over a domain covering coastal North Carolina, USA, including the large Albemarle‐Pamlico estuarine system, and a long and narrow back‐barrier estuary (Currituck Sound [CS]) that experiences major water level variations is investigated in detail. A high‐resolution (50 m) grid with eight vertical layers is used to simulate the conditions during Tropical Storm Hermine and Hurricane Matthew in 2016. Hindcasts (winds, pressure, and precipitation) from eight different atmospheric models are used as atmospheric input conditions, and the results are compared with detailed observations of surface waves, currents, and water levels from sensors mounted on five monitoring platforms in CS. Results show that major differences exist between wind fields producing variations coastal conditions. Precipitation directly on the water surface had a large effect on water levels and produced a larger inundated area. These results help to understand the important contributions of each physical process (precipitation, wind‐driven surge, and waves) to circulation and water levels, and provide guidance on the impact of atmospheric forcing conditions on back‐barrier environments during hurricanes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".