Complex Numerical Simulation of the U.S. East Coast and Inland Areas using a Coupled Hydrologic, Hydrodynamic and Ocean model: Application to Hurricane Sandy
Bibliographic record
Abstract
Fluctuations of the total water level in the U.S. East Coast depends on the complex interactions of freshwater flow, tide, storm surge and wave actions. In order to include all major forcings of water movement in this area, a coupled modeling system consisting of the National Water Model (NWM), the Advanced Circulation Ocean Model (ADCIRC), and the WAVEWATCH III model has been developed. In this system, a coupled inland hydrologic model is linked to an ocean hydrodynamic and wave model to compute total water levels in the coastal zones. In the freshwater component of the hydrodynamic model, 1D river components were included in the model to capture an accurate representation of tributaries to the 2D model of the estuary and oceans. The model domain included several states of the US East Coast starting from New Jersey to the St. Croix River at the US-Canada border. Model simulations were compared with 2012 superstorm Sandy measured tidal water levels and hurricane surge. Initial simulations reproduced satisfactory spatial and temporal variations of water levels due to riverine discharge and storm surge. The model predictions showed that using 1D component allowed better representations of the inland rivers and produced accurate river water levels. Simulations indicated that water levels in the inland areas depends on both river discharges and backwater effects of the ocean. These results showed the strengths of the coupled modeling system used in this research to compute total water levels during river flooding that coincides with extreme hurricane surge. Initial results showed that the coupled modeling framework used in this study is capable of total water estimation in the coastal zones and the accuracy of the water levels highly depends on the availability of reliable topographic, bathymetric, and bottom roughness data.
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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 | 0.001 | 0.001 |
| 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".