An Analysis of the Thermal Regime and Energy Balance of a Subarctic Hydroelectric Reservoir Using Direct Measurements of Surface and Lateral Exchanges
Bibliographic record
Abstract
The thermal regime of hydroelectric reservoirs differs from that of lakes, as it is influenced not only by natural inflows and outflows of energy, but also by management rules through regulated downstream constraints and more importantly the electric demand through turbine flows. These advection terms are rarely assessed for hydroelectric reservoirs particularly in eastern North America, a region where they are abundant. This study contributes, using a series of unique observations, to the assessment of the water and energy balances of the 85-km 2 Romaine-2 northern reservoir (50.69°N; 63.24°W) with an average depth of 44 m. Two thermistor chains were deployed to monitor the dynamics of the vertical temperature profiles from 2018 to 2022. The surface energy balance components were measured using two eddy-covariance stations. Summer stratification occurs from June to November, and winter stratification from December to May. The maximum water temperature gradient of the metalimnion was 1°C m –1 in mid-September, and the maximum depth of the thermocline was 35 m in late October, before the autumn mixing period. We found that the water balance of the reservoir was mainly controlled by turbine operations, with a hydraulic residence time of 5.4 months. Net radiation was found to be the main source of energy to the reservoir (95.6% of the energy input), and the net advection of heat was weak (4.4%) in a steady state reservoir. Latent (58.5%) and sensible (16.5%) heat fluxes dominated the outflow energy balance. In short, this study highlights that the heat advection term represents a small fraction of the annual energy budget for the subarctic reservoir under study, despite being the dominant term in its water budget.
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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) | 0.000 | 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 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".