Characteristic Time Scales of Reservoir Evaporation in a Subarctic Climate
Bibliographic record
Abstract
Water bodies such as lakes and reservoirs affect the regional climate by acting as heat sinks and sources through the evaporation of substantial quantities of water over several months of the year. Unfortunately, energy exchange observations between inland water bodies and the atmosphere remain rare in northeastern North America, which has one of the highest densities of lakes in the world. This study helps to fill this gap by analyzing field observations collected from a subarctic hydropower reservoir (50.69°N, 63.24°W) characterized by a mean depth of 44 m and a surface area of 85 km 2 . Two eddy covariance (EC) systems, one on a raft and one onshore, were deployed from 27 June 2018 to 12 June 2022. The thermal regime of the reservoir was monitored using vertical chains of thermistors. Results indicate a mean annual evaporation rate of 590 ± 66 mm (~70% of the annual precipitation), with 84% of the evaporation occurring at a high rate from August to freeze-up in late December through episodic pulses. It was difficult to close the energy balance because of the magnitude and the large time lag of the heat storage term. In order to circumvent this problem, we opted to perform calculations for a year that started from the first of March, as the heat storage in the water column was at its lowest at that point and could thus be ignored. From June to December, monthly Bowen ratios increased from near-zero negative values to about 1.5. After September, due to smaller vapor pressure deficits, latent heat fluxes steadily declined until an ice cover sealed up the reservoir. Two opposite diurnal cycles of sensible and latent heat fluxes were revealed during the open water period, with sensible heat fluxes peaking at night and latent heat fluxes peaking in the afternoon.
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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 | 0.000 |
| 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".