Parameterization of Blowing-Snow Sublimation in a Macroscale Hydrology Model
Bibliographic record
Abstract
An algorithm that parameterizes the topographically induced subgrid variability in wind speed, snow transport, and blowing-snow sublimation was designed for use within macroscale hydrology models and other large-scale land surface schemes (LSSs).The algorithm is intended to provide consistent estimates of the relative influence of sublimation from blowing snow for continental-scale river basins, while balancing the land surface water and energy budgets.In addition to the standard LSS inputs, the model requires specification of the standard deviation of terrain slope, the mean fetch, and the lag-1 autocorrelation of terrain gradients.Sublimation fluxes are solved for each vegetation class, for each model grid cell.Model results are compared to observed snow water equivalent (SWE) and simulated estimates of sublimation from blowing snow for two small tundra watersheds: Imnavait Creek, Alaska, and Trail Valley Creek, Northwest Territories, Canada, produced by two different small-scale distributed blowing-snow algorithms.The macroscale algorithm reproduced most aspects of the variability between years and between vegetation types predicted by the more detailed models.The macroscale model was subsequently used to estimate sublimation from blowing snow and the snowpack for the 8000-km 2 Kuparuk River watershed in northern Alaska.Annual average sublimation from blowing snow predicted by the model for this region varies from 47 mm in the foothills of the Brooks Range to approximately 31 mm on the Arctic coastal plain; sublimation was primarily controlled by topographic limitations on fetch in the foothills and by precipitation and vapor pressure on the coastal plain.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 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".