Snow depth and streamflow relationships in large North American watersheds
Bibliographic record
Abstract
Snowmelt runoff in the spring is an important component in regional hydrologic systems in the northern United States and Canada, having a vital influence on water resources. In northern latitude rivers, snowmelt runoff provides a considerable volume of freshwater to drive circulation in the Arctic Ocean. This project defines and analyzes patterns of snow volume and discharge in major North American watersheds and determines the strength and form of the associated relationships. The results are used to develop statistical models applicable to individual watersheds, including the Yukon and Mackenzie basins in northern Canada and Alaska, the Saskatchewan basin in southern Canada, and the Missouri and upper Mississippi watersheds in the northern United States. It is shown that snow volume can predict winter and early spring discharge in all watersheds in the study region, with the best model performance in the higher‐latitude Yukon and Mackenzie basins during late fall and winter accumulation. In the lower‐latitude Missouri and upper Mississippi basins, despite additional influences of rain on discharge patterns, the statistical models based on snow volume were still able to estimate streamflow with percent relative error around 50%. To improve modeled discharge estimates during peak spring runoff, additional snow cover variables, including the value and timing of peak snow volume and the duration of snowmelt, were compared with peak annual discharge during periods of intense snowmelt. Significant results were found to occur in the Yukon and Saskatchewan basins owing to the extreme sensitivity to snowmelt runoff and fast river response times.
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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 | 0.000 | 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) | 0.001 | 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".