Impacts of precipitation on the cryologic regime of stream channels
Bibliographic record
Abstract
Abstract This paper examines the controlling influence of snow and rain on river ice processes in creeks and streams. Winter precipitation (in the form of rain and snow) has been observed to affect river ice processes and channel parameters of low and high gradient channels in unsuspected ways that can have significant impacts on channel hydraulics, hydrology and habitat. On a low gradient stream, a snowfall event initiated the development of an ice cover by creating unconsolidated snow slush bridges that eventually froze in place. Afterward, both snowfalls and rainfalls in alternation with cold spells dramatically increased the thickening rate of the ice cover well beyond that predicted by classic equations. In a smaller low‐gradient agricultural creek, wind‐blown snow impeded the formation of an ice cover by insulating the flow from cold atmospheric conditions. On steep channels (of different sizes and morphologies), anchor snow slush has been seen to accumulate on the bed substrate. As opposed to anchor ice, anchor snow slush is not believed to require supercooling water conditions to form nor to stay in place. Finally, in a steep headwater creek, a rain‐on‐snow event generated a snow slush flow and multiple snow slush jams. This phenomenon was seen to divert most of the water out of the channel into another watershed and concomitantly signalled a mid‐winter breakup in the greater watershed downstream. These observations suggest that the role of precipitation on small channel winter ice morphology and water flows, levels and currents has been severely underestimated and that any ecological winter studies, hydraulic structure designs and river modelling efforts need to include processes that are sometimes dominated by rain, slush and snow. Copyright © 2012 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 | 0.000 |
| 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) | 0.004 | 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 teacher head, 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".