Validation of a 1-D lake model for modeling evaporation from an elongated and deep boreal reservoir
Bibliographic record
Abstract
Freshwater reservoirs modify the regional climate through mass, energy, and momentum exchanges with the atmosphere. Recent studies have shown that hydropower reservoirs tend to evaporate more than the land they have flooded, hence reducing water availability for other uses, at a level that should with local climate conditions, however. Knowing that evaporation is a key component of the water balance and that very few studies have focused on evaporation from northern reservoirs, which are ice covered several months per year, there is a real need for models that can provide reliable estimates of this water vapor flux. This project focuses on the modeling of evaporation from an 85-km2 hydropower reservoir located in the boreal biome of eastern Canada (50.7°N, 63.2°W), with a mean depth of 60 m and an elongated shape. To support this modeling effort, two flux towers (one on the shore and one on a raft) and a vertical chain of thermistors were deployed. Exchanges between the water surface and the atmosphere are simulated with the Canadian Small Lake Model (CSLM), a 1-D physical-based surface scheme designed to be coupled with a numerical weather prediction model. The model also simulates the thermal regime of the water body, including ice formation. Considering the irregular shape of the reservoir as well as its depth, a new model parameterization was adopted that improved simulations (albedo parameterization, leakage parameter, mixed layer maximum depth...). Turbulent fluxes were successfully predicted during the open water period. Comparison between observed and modeled time series showed a good agreement specifically for sensible heat fluxes. Deviations mostly occur before freeze-up (October to November) and around ice off (April to May) with a tendency of overestimating latent heat fluxes when its observed magnitude is small (ice period). Thermal mixing as well as mixed layer deepening were well estimated. Thermal mixing, as well as mixed layer deepening, were well estimated. Near-surface water temperature confirmed the ability of the CSLM to simulate the near-surface seasonal cycle. However, in early fall, an overestimation of the water temperature induced an overestimation of the heat fluxes leading to early depletion of the energy storage that led to an early modeled freeze-up.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".