Estimating the Impact of Impounding an 85‐km <sup>2</sup> Hydropower Reservoir in a Subarctic Environment on the Local Radiation Balance
Bibliographic record
Abstract
Abstract This study focuses on the net radiative forcing caused by the impoundment of an 85‐km 2 hydroelectric reservoir in the subarctic Côte‐Nord, Quebec, Canada (50.69°N, 63.24°W, mean depth of 44 m). Using spectral bands from the Landsat 7 ETM+ and Landsat 8 OLI/TIRS satellites, we observed spatial and temporal variations in albedo and surface temperature on 104 dates between 2000 and 2023. By integrating these data with ERA5‐Land meteorological reanalyses and in situ measurements, we investigated seasonal and interannual variations in shortwave and longwave radiative fluxes and net radiation before (2000–2013) and after (2015–2023) reservoir impoundment. The flooded area initially consisted of 79% coniferous forest (mainly black spruce), 15% water, 4% dry soil, and 2% wet soil. Before the impoundment, the average winter albedo was 0.25 ± 0.02 and the summer albedo was 0.08 ± 0.002. After the impoundment, the winter albedo increased significantly, reaching between 0.70 and 0.90 in the presence of fresh snow on the ice (January to May), while in summer it decreased to about 0.05 ± 0.003. Surface temperatures exhibited pronounced pre‐ to postimpoundment contrasts, with the reservoir consistently 2–7°C warmer in autumn and winter but 1–5°C cooler in spring and summer. The reservoir also reaches its maximum temperature 3–5 weeks later. The impoundment induced a net radiative cooling of −21 W m −2 compared to forest, featuring a pronounced winter deficit (−60 to −140 W m −2 ) due to snow cover albedo effects, partially compensated by summer surpluses (+5 to +50 W m −2 ).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".