Stochastic Generation of Snow Depth in Canada
Bibliographic record
Abstract
Snow depth is a significant component in the hydrological cycle and global energy and water balances, contributing to climate change impacts. Weather stations with gauges for snow depth are scarce, especially in complex terrain regions, and require high accuracy for measurements. Advances in observational systems offer unconventional solutions yet are expensive. To bridge these gaps, stochastic generation methods offer a cost-effective solution to reproduce time series of hydrological variables, preserving their stochastic properties. Stochastic generation methods are well-established for total precipitation but lack snow depth generation. Here, we introduce a stochastic method to exclusively generate snow depth time series that preserve their distinct statistical properties on different time scales. We use 450 observed snow depth time series and 470 CMIP6 simulations to detect Canada's observed and physical statistical properties. The results indicate that snow depth has a light tail, and the distribution might change daily. The probability of zero snow depth shows a clear seasonal pattern. The synthetic snow depth time series can be an alternative to climate models’ outputs, offering a computationally effective solution to investigate the snow depth variability. This method advances the generation of stochastic time series of snow depth and can be applied to investigate catastrophes from snowmelt processes and avalanches that lead to severe damage and fatalities.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".