Yukon River streamflow response to seasonal snow cover changes
Bibliographic record
Abstract
Abstract We used remotely sensed weekly snow water equivalent (SWE) and snow cover extent (SCE) data to investigate streamflow response to seasonal snowcover change over the Yukon watershed. We quantified the seasonal cycles and variations of snowcover (both SWE and SCE) and river streamflow, and identified a clear correspondence of river discharge to seasonal snowcover change. We also examined and compared the weekly mean streamflow with the weekly basin SWE and SCE. The results revealed a strong relation between the streamflow and snowcover change during the spring melt season. This relationship provides a practical procedure of using remotely sensed snowcover information for snowmelt runoff estimation over the large northern watersheds. Analyses of extreme (high/low) streamflow cases (years) and basin snowcover conditions indicate an association of high (low) flood peak with high (low) maximum SWE. Comparative analyses of weekly basin SWE versus SCE, peak snowmelt floods, and climatic variables (temperature and winter precipitation) indicate consistency among basin SWE, SCE, and temperature, but there is some incompatibility between basin SWE and winter precipitation. The inconsistency suggests uncertainties in determination of basin winter snowfall amounts and limitations in applications of the SWE retrieval algorithm over large watersheds/regions with different physical characteristics. Overall, the results of this study demonstrate that the weekly SWE and SCE data/products derived from remote sensing technology are useful in understanding seasonal streamflow changes in the northern regions. Copyright © 2008 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; both teacher heads agree on what is shown here.
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".