Relationship between passive microwave‐derived snowmelt and surface‐measured discharge, Wheaton River, Yukon Territory, Canada
Bibliographic record
Abstract
Abstract Snow volume and melt timing are major factors influencing the water cycle at northern high altitudes and latitudes, yet both are hard to quantify or monitor in remote mountainous regions. Twice‐daily special sensor microwave imager (SSM/I) passive microwave observations of seasonal snow melt onset in the Wheaton River basin, Yukon Territory, Canada (∼60 ° 08′05″N, ∼134 ° 53′45″W), are used to test the idea that melt onset date and duration of snowpack melt–refreeze fluctuations control the timing of the early hydrograph peaks with predictable lags. This work uses the SSM/I satellite data from 1988 to 2002 to evaluate the chronology of melt and runoff patterns in the upper Yukon River basin. The Wheaton River is a small (875 km 2 ) tributary to the Yukon, and is a subarctic, partly glacierized heterogeneous basin with near‐continuous hydrographic records dating back to 1966. SSM/I pixels are sensitive to melt onset due to the strong increase in snow emissivity, and have a robust signal, in spite of coarse (>25 × 25 km 2 ) pixel resolution and varied terrain. Results show that Wheaton River peak flows closely follow the end of large daily variations in brightness temperature of pixels covering the Wheaton River, but the magnitude of flow is highly variable, as might be expected from interannual snow mass variability. Spring rise in the hydrograph follows the end of high diurnal brightness temperature ( T b ) amplitude variations (DAV) by 0 to 5 days approximately 90% of the time for this basin. Subsequent work will compare these findings for a larger (7250 km 2 ), unglacierized tributary, the Ross River, which is farther northeast (∼61 ° 59″40″N, ∼132 ° 22″40″W) in the Yukon Territory. These techniques will also be used to try to determine the improvement in melt detection and runoff prediction from the higher resolution (∼15 × 15 km 2 ) advanced microwave scanning radiometer for EOS (AMSR‐E) sensor. Copyright © 2006 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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 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".