Parameterizing redistribution and sublimation of blowing snow for hydrological models: tests in a mountainous subarctic catchment
Bibliographic record
Abstract
Abstract Model tests of blowing snow redistribution and sublimation by wind were performed for three winters over a small mountainous sub‐Arctic catchment located in the Yukon Territory, Canada, using a physically based blowing snow model. Snow transport fluxes were distributed over multiple hydrological response units (HRUs) using inter‐HRU snow redistribution allocation factors (SR). Three SR schemes of varying complexity were evaluated. Model results show that end‐of‐winter snow accumulation can be most accurately simulated using a physically based blowing snow model when SR values are established when taking into account wind direction and speed and HRU aerodynamic characteristics, along with the spatial arrangement of the HRUs in the catchment. With the knowledge that snow transport scales approximately with the fourth power of wind speed (u4), SR values can be (1) established according to the predominant u4 direction and magnitude over a simulation period or (2) can change at each time step according to a measured wind direction. Unfortunately, wind direction data were available only for one of the three winters, so the latter scheme was tested only once. Although the aforementioned SR schemes produced different results, model efficiency was of similar merit. The independent effects of topography and vegetation were examined to assess their importance on snow redistribution modelling over mountainous terrain. Snow accumulation was best simulated when including explicit representations of both landscape vegetation (i.e. vegetation height and density) and topography (i.e. wind exposure). There may be inter‐basin differences in the relative importance of model representations of topography and vegetation. Copyright © 2009 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".