Modeling Thermal and Dynamic River Ice Processes
Bibliographic record
Abstract
River ice jam formation and release both have the potential to cause flooding and present a threat to human safety. From a flood forecasting perspective, it is highly desirable to be able to predict the effects of ice on streamflow hydraulics. Although a number of models have been developed over the years that do consider ice effects, the majority of these are proprietary. This paper reports on recent developments in incorporating ice processes into the public domain River1D hydrodynamic model. The first objective in this effort was to incorporate river ice formation and melting processes and the application of these components is illustrated for the Peace River in western Canada. The model employs an Eulerian frame of reference for both the flow hydrodynamics and the ice processes and uses a Petrov-Galerkin finite element method to solve the primary equations. Model calibration and validation results with historical data are presented; these indicate that the present model adequately simulates water temperature and ice front progression. However, further enhancements are required to include certain dynamic freeze-up processes, in order to refine the ice front results. Ice jam formation and release components have also been added to the model to facilitate the ice jam flood forecasting application and these capabilities are demonstrated by modeling the large ice jam release event that occurred on the Athabasca River, AB, in 2002. Total ice and water mass and momentum equations are solved in an uncoupled sequence with ice mass conservation, with ice momentum effects considered empirically. The model is found to do a good job of modeling release wave speed and peak magnitude. Further enhancements to consider ice momentum effects more explicitly are underway.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".