Effects of spatial aggregation of initial conditions and forcing data on modelling snowmelt using a land surface scheme
Bibliographic record
Abstract
Small-scale topography and snow redistribution have important effects on snow-cover heterogeneity and the timing, rate, and duration of spring snowmelt in mountain tundra environments. However, land surface schemes (LSSs) are usually applied as a means to provide large-scale surface states and vertical fluxes to atmospheric models and do not normally incorporate topographic effects or horizontal fluxes in their calculations A study was conducted in Granger Creek, an 8-km2 catchment within Wolf Creek Research Basin in the Yukon Territory, Canada, to examine whether inclusion of the effects of wind redistribution of snow between landscape units, and slope and aspect in snowmelt calculations for tiles, could improve the simu-lation of snowmelt by an LSS. Measured snow accumulation, reflecting overwinter wind redistribution of snow, was used to provide initial conditions for the melt simulation, and physically based algorithms from a small-scale hydrological model were used to calculate radiation on slopes during melt. Based on consideration of the spatial distribution of snow accumulation, topography, and shrub cover in the basin, it was divided into five landscapes units (tiles) for simulation of mass and energy balance using an LSS during melt. Effects of averaging initial conditions and forcing data on LSS model performance were contrasted against distributed simulations. Results showed that, in most of the cases, simulations using aggregated initial conditions and forcing data gave unsuccessful descriptions of snow ablation whereas the incorporation of both snow-cover redistribution and slope and aspect effects in an LSS improved the prediction of snowmelt rate, timing, and duration. 1.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".