Freezing Temperature Controls Winter Water Discharge for Cold Region Watershed
Bibliographic record
Abstract
Abstract Pronounced climate warming over the arctic‐subarctic regions has lead to profound hydrological changes including intensified river flow, but how soil frost controls aquifer discharge remains poorly understood. This study quantifies the relationship between freezing temperature and baseflow in winter. Analyses show that the traditional reservoir models are unable to reproduce the observed baseflow variations. By incorporating a freezing temperature function in the reservoir models, the model performances are largely improved. It indicates the dominant role of freezing temperature in controlling the aquifer discharge through reducing the watershed conductivity and liquid (active) water content. The results for the Albany watershed in Canada show that the watershed lump conductivity decreases by half when air temperature accumulates to −172 °C·day from winter start and in extremely cold years, it could decrease by more than 85%. With this relationship, a climate warming of +1, +2, and +4 °C would suggest an increase of 7.7%, 16.7%, and 41.0% in conductivity or 6.8%, 14.7%, and 35.0% in winter discharge, respectively. The study provides an important link between climate warming and aquifer discharge in cold regions. The results could be particularly useful for developing process‐based models, estimating baseflow variations, and assessing climate change impact on cold region hydrology.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".