Snow Persistence Explains Stream High Flow and Low Flow Signatures with Differing Relationships by Aridity and Climatic Seasonality
Bibliographic record
Abstract
Abstract. Snow persistence is a globally available metric of snow cover duration that has, thus far, seen limited application to the field of hydrology. This study attempts to explore the controls that snow persistence exerts on streamflow at low and high flow conditions across a diverse spectrum of climatic aridity and seasonality in the United States and Canada. We statistically analyze how snow persistence, aridity, and seasonality conditions interact to control and explain streamflow shape and flashiness at low and high flows. For low flow condition, regardless of climatic aridity and seasonality, a larger snow persistence increases baseflow, reduces low flow variability, and increases the magnitude of extreme low flow relative to average flow. Our results further show that snow persistence becomes a stronger factor in controlling baseflow as well as the magnitude of extreme low flow relative to average flow, in regions with a relatively high aridity and/or with summer-dominant precipitation regimes (or in-phase seasonality). On the other hand, in catchments that are moderately wet to very arid with winter-dominant precipitation regimes (or out-of-phase seasonality), a longer snow persistence could typically lead to a more variability at high flow and a larger magnitude of extreme high flow relative to average flow. This study concludes by demonstrating the relevancy of snow persistence as a globally available streamflow behaviour descriptor and by demonstrating the impacts that climate change may have on snow persistence and ultimately on streamflow behaviour at low and high flows.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".