Impact of snow cover on river low flows in North America
Bibliographic record
Abstract
<!--!introduction!--> Limited information exists on the linkages between snowmelt and summer low flow generation in cold region rivers. This study examined the influence of snowpack conditions on low flow during the warm season for a sample of 260 snow-affected pristine river basins in North America. Correlations between summer low flow magnitude and antecedent winter and summer hydroclimate variables showed that the maximum winter snow water equivalent (SWE) influenced summer low flow, with a decreasing influence of SWE throughout summer and significant inter-basin heterogeneity. The sensitivity of low flow to both SWE and summer rainfall was assessed using multivariate hierarchical models which include the effects of catchment attributes. Expectedly, the rainfall accumulated between the snowmelt onset and the low flow period was the dominant control on low flow volumes, but snow storage also had a significant influence. The sensitivity of low flows to SWE was stronger towards higher elevations and weaker in more forested catchments. While snow accumulation and melt are known to be a prime flood generation mechanism in several cold regions, our results also show that snowmelt is an important contributor to low flow discharge in summer via subsurface and groundwater flows. Future depletion of snowpacks in response to climate change could thus exacerbate hydrological droughts in regions with limited summer rainfall.
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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".