A detailed energy budget analysis of river supercooling and the importance of accurately quantifying net radiation to predict ice formation
Bibliographic record
Abstract
Abstract River supercooling and ice formation is a regular occurrence throughout the winter in northern countries. The resulting frazil ice production can obstruct the flow through intakes along the river, causing major problems for hydropower and water treatment facilities, among others. Therefore, river ice modellers attempt to calculate the river energy budget and predict when supercooling will occur in order to anticipate and mitigate the effects of potential intake blockages. Despite this, very few energy budget studies have taken place during freeze‐up, and none have specifically analysed individual supercooling events. To improve our understanding of the freeze‐up energy budget detailed measurements of air temperature, relative humidity, barometric pressure, wind speed and direction, short‐ and longwave radiation, and water temperature were made on the Dauphin River in Manitoba. During the river freeze‐up period of late October to early November 2019, a total of six supercooling events were recorded. Analysis of the energy budget throughout the supercooling period revealed that the most significant heat source was net shortwave radiation, reaching up to 298 W/m 2 , while the most significant heat loss was net longwave radiation, accounting for losses of up to 135 W/m 2 . Longwave radiation was also the most significant heat flux overall during the individual supercooling events, accounting for up to 84% of the total heat flux irrespective of flux direction, highlighting the importance of properly quantifying this flux during energy budget calculations. Five different sensible ( Q h ) and latent ( Q e ) heat flux calculations were also compared, using the bulk aerodynamic method as the baseline. It was found that the Priestley and Taylor method most‐closely matched the bulk aerodynamic method on a daily timescale with an average offset of 8.5 W/m 2 for Q h and 10.1 W/m 2 for Q e , while a Dalton‐type equation provided by Webb and Zhang was the most similar on a sub‐daily timescale with average offsets of 20.0 and 14.7 W/m 2 for Q h and Q e , respectively.
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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 | 0.001 |
| 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) | 0.000 | 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".