Suitability of Dynamic Modeling for Flood Forecasting during Ice Jam Release Surge Events
Bibliographic record
Abstract
Ice jam release surges present a unique challenge to the flood forecaster, since the surge released when an ice jam fails is highly dynamic in nature and, therefore, traditional hydrologic flood routing techniques are inapplicable. The problem is analogous to the classic dam break scenario and should be amenable to analysis by hydraulic flood routing techniques. However, previous investigations suggest that the influence of ice on the wave propagation and attenuation must also be considered to achieve accurate results. This study explores the applicability of dynamic hydraulic flow modeling techniques to the ice jam surge propagation problem, presenting the results of numerical simulations of the ice jam release event which occurred on the Saint John River upstream of Grand Falls, N.B., in April 1993. The surge propagation analysis was conducted using a one-dimensional finite element implementation of the Saint Venant equations adapted for natural channel geometries. Even neglecting ice effects, the resulting model is successful in terms of reproducing the observed peak stage and the surge propagation speed. Based on these results, it is concluded that accurate channel geometry is a key factor in effectively modeling ice jam release surge events.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".