Gravel-bed river morphodynamic processes throughout a very large flood event from numerical modeling
Bibliographic record
Abstract
Due to the lack of numerical modeling and continuous in-situ monitoring of flow and sediment transport data, the mechanism of local-scale morphodynamic processes during very large floods are not fully understood. Thus, this study employs Delft3D to develop a two-dimensional (2-D) morphodynamic model to simulate and analyze the morphodynamic process of a gravel-bed river during an 80-year flood event in Calgary, Canada. The model was calibrated using velocimetry data and validated against the measured post-flood bed elevation data. The coefficient of determination (R2) and ratio of the root-mean-square error to the standard deviation (RSR) between the modeled and measured bed elevation was 90% and 0.33, respectively, which demonstrates the reliability of the model. The modeled flow velocity, bedload transport rate, surface sediment sizes, and corresponding morphological changes at different flood stages are presented and analyzed. Results show that bed incision and the mid-channel bar continuously developed throughout the flood while bank erosion and the growth of bank-attached bar mainly happened during the rising and peak periods. We found that the timing and duration of major morphological changes during a flood event varies from site to site within a reach, but is similar for similar morphological units. We also found that the spatial variation of channel planform is the dominant determinant of major morphological changes during floods, while flood events trigger the sediment motion and result in actual deposition or erosion. Improvements are needed in terms of the modeling of bedload transport and bed stratigraphy.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| 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".