Implementation of a largescale variable velocity river flow routing algorithm
Bibliographic record
Abstract
ABSTRACT Implementation and validation of a flow routing scheme for the North American domain of the Canadian Regional Climate Model (CRCM) is described. A variable velocity flow routing algorithm is used to transport runoff from the land surface to the continental edges and provide freshwater flux forcing for the oceans. The flow routing scheme uses Manning’s equation to estimate flow velocities for river channels whose cross-sec-tions are assumed to be rectangular. Discretization of major North American river basins and their flow direc-tions are obtained at the polar stereographic resolution of the CRCM using 5-minute global river flow direction data as a template. In the absence of observation-based gridded estimates of runoff, model runoff estimates from a global simulation of the Variable Infiltration Capacity (VIC) hydrological model (forced with observation-based meteorological data) are used to validate the flow routing scheme. Model results show that the inclusion of flow routing improves the comparison with observation-based streamflow estimates when compared to the unrouted runoff. Monthly comparison of simulated streamflow with observation-based estimates, and basin-wide averaged flow velocities, suggests that the flow routing scheme performs satisfactorily. RÉSUME [Traduit par la rédaction] L’article décrit la mise en œuvre et la validation d’un schéma de routage du ruissellement pour le domaine nord-américain du Modèle régional canadien du climat (MRCC). Un algorithme de routage du ruissellement à vitesse variable est utilisé pour simuler le transport du ruissellement à la surface
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".