Hydrodynamic Simulation of an Irregularly Meandering Gravel-Bed River: Comparison of MIKE 21 FM and Delft3D Flow models
Bibliographic record
Abstract
This study aims at hydrodynamic modelling of Bow River, which passes through the City of Calgary, Canada. Bow River has a mobile gravel bed. Erosion and deposition processes were exacerbated by a catastrophic flood in 2013. Channel banks were eroded at various locations, and large gravel bars formed, which could lead to water level changes and accordingly more flooding. This study investigates the performance of Delft3D-Flow and MIKE 21 FM to simulate the hydrodynamics of the river during the 2013 flood. MIKE 21FM employs unstructured triangular mesh while Delft3D-Flow model uses curvilinear structured grids. Performance of each model was evaluated by the available historical water levels. The results of this study demonstrated that, with approximately the same averaged grid resolution, MIKE 21 FM resulted in more accurate results with a higher computational cost compared to the Delft3DFlow model. It was shown that Delft3D-Flow model may require higher grid cell resolution to result in comparably same depth-averaged velocities throughout the study area. However, considering the balance between the computational cost and the accuracy of the results, both models were capable to adequately replicate the hydrodynamics of the river during the 2013 flood. Results of statistical KS and ANOVA test analysis showed that the model predictions were sensitive to the horizontal eddy viscosity and the Manning roughness. This confirms the necessity of an appropriate calibration of the generated numerical models. The findings of this study shed light on the Bow River flood modelling, which can guide flood management.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".