Snowmelt characterization from optical and synthetic-aperture radar observations in the La Joie Basin, British Columbia
Bibliographic record
Abstract
Abstract. Snowmelt runoff serves both human needs and ecosystem services and is an important parameter in operational forecasting systems. Sentinel-1 synthetic-aperture-radar (SAR) observations can estimate the timing of melt within a snowpack; however, these estimates have not been applied on large spatial scales. Here we present a workflow to combine Sentinel-1 SAR and optical data from Landsat-8 and Sentinel-2 to estimate the onset and duration of snowmelt in the La Joie Basin, a 985 km2 watershed in the southern Coast Mountains of British Columbia. A backscatter threshold is used to infer the point at which snowpack saturation occurs and the snowpack begins to produce runoff. Multispectral imagery is used to estimate snow-free dates across the basin to define the end of the snowmelt period. SAR estimates of snowmelt onset form consistent trends in terms of elevation and aspect on the watershed scale and reflect snowmelt records from continuous snow water equivalence observations. SAR estimates of snowpack saturation are most effective on moderate to low slopes (< 30∘) in open areas. The accuracy of snowmelt duration is reduced due to persistent cloud cover in optical imagery. Despite these challenges, snowmelt duration agrees with trends in snow depths observed in the La Joie Basin. This approach has high potential for adaptability to other alpine regions and can provide estimates of snowmelt timing in ungauged basins.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".