Reservoir Ice Conditions from Multi-Sensor Remote Sensing and ERA5-Land: The Manicouagan Hydroelectric Reservoir Case Study
Bibliographic record
Abstract
Reservoir ice can have an important impact on the watershed scale and influence hydraulic operations. On the other hand, hydropower generation can also impact the ice regime. In this study, multi-source satellite and ERA5-land data are used to evaluate ice conditions. Specifically, ice-controlling variables (temperature, water levels), ice regime (cover/deformation, thickness) and their interrelations are assessed for a 5-year period from 2017 to 2021. The methodology is applied to the Manicouagan reservoir, one of the largest hydropower reservoirs in Quebec, Canada. The satellite-based land surface temperatures (LSTs) suggest that winter 2021 was the hottest one. Overall, MODIS and Landsat LSTs agree with the ERA5-land temperatures. Ice backscatter from Sentinel-1 indicates that, in general, the reservoir is completely covered by ice from January to March. A correlation of 0.6 and 0.8 is observed between C- and Ku-band Synthetic Aperture Radar (SAR) signal and ice thickness, respectively. Important ice changes inferred from Differential Interferometric SAR (D-InSAR) occur approximately at the position where the largest ERA5-land ice thickness differences are observed. Winter water levels are also evaluated using satellite altimetric data to verify their influence on the ice dynamics. They show a decreasing tendency as the winter advances.
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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 | 0.001 | 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 | 0.001 | 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".