Development of a Continental Scale Coastal Flood Model Using a Sub-Setting Approach
Bibliographic record
Abstract
Coastal flooding associated with hurricanes and other major storm events along the U.S. Coast results from complex interactions between freshwater flows, tides, storm surge, and wave effects. We have developed a two-way coupled model consisting of the National Water Model (NWM), the Advanced Circulation Ocean Model (ADCIRC), and WAVEWATCH III (WWIII) to quantify these interactions and compute total water levels in the coastal zone after significant riverine and coastal flooding events. This coupled continental coastal model covers the US Gulf and Atlantic Coasts, extending from the US-Canada border to the US-Mexico border. The Delft3D FM, D-Flow Flexible Mesh (D-Flow FM) model simulates coastal flooding on a 2D unstructured mesh within the National Water Model (NWM)/ADCIRC/WWIII coupled system. We developed a high quality 2D unstructured mesh using a sizing function that assigns element sizes based on proximities of coastal features at given spatial locations. Data sources used to identify relevant coastal features included NWM streamlines, the National Hydrography Dataset (NHD), and United States Army Corps of Engineers (USACE) data, allowing integration of D-Flow FM with the NWM and optimization of the number of computational points. The system obtains freshwater inflow boundary conditions to D-Flow FM from the NWM channel network. Offshore water levels boundary conditions for D-Flow FM come from the coupled ADCIRC-WWIII model. Domain sub-setting keeps runtimes within reasonable limits, as it does execution of the detailed hydrodynamic model within a user-defined area enclosing the storm landfall site. The advantage of this approach comes from the fact that the same coupled model setup allows simulation of coastal flooding for different storm events; only the sub-setting enclosure and the atmospheric forcing require updating from case to case. Model validation, consisting of water level comparisons against observations from simulations using the coupled system for historical storm events. The model simulations satisfactorily reproduced observed spatial and temporal variations of total water levels. In conclusion, this study presents performance of the sub-setting approach in reducing runtime considerably without compromising the accuracy of the coupled modeling system solution.
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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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".