Parameterizing redistribution and sublimation of blowing snow for hydrological models: tests in a mountainous subarctic catchment
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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 ( S R ). Three S R 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 S R 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 ( u 4 ), S R values can be (1) established according to the predominant u 4 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 S R 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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it