Modeling evaporation from northern waterbodies, the case of an 85-km2 reservoir
Bibliographic record
Abstract
Freshwater bodies represent 9% of Canada’s total land area, with more than half of these having a surface area smaller than 100 km2. Taking into account the interactions between lakes and the atmosphere in meteorological models is crucial, considering the marked differences with the surrounding land masses (low albedo, unlimited source of water, high thermal capacity, etc.). Open water evaporation, in particular, is often a challenge because of its intangible nature and the scarcity of direct observations. This project focuses on the modeling of the surface energy budget of a reservoir located in the boreal biome of eastern Canada, with an emphasis on the evaporation. Observations are available for the 85-km2 La Romaine 2 hydroelectric reservoir (50.7°N, 63.2°W), where two micrometeorological towers were deployed: one operated yearlong on the shore and one operated on a floating deck during ice-free conditions. Modeling resorts to the Canadian Small Lake Model (CSLM), a one-dimensional land surface model designed to integrate the lake-atmosphere fluxes into meteorological models. The model also simulates the thermal regime of the water body, including ice formation. Lastly, the model can be used for climate and weather prediction, which may be a useful for reservoir management. Comparison of field observations and simulations confirms the CSLM ability to reproduce the turbulent fluxes and the temperature behavior of the reservoir except for some specific periods, in particular the ice breakups and freeze-ups. The model somehow underestimates the water temperature resulting in a premature depletion of the lake heat storage in autumn. It also overreacts to high wind episodes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".