Attributes of Lake Okanagan evaporation and development of a mass transfer model for water management purposes
Bibliographic record
Abstract
Understanding the water budget of the valley lakes in the water-stressed Okanagan region of British Columbia is important for allocating resources to maximize social well-being, environmental quality and the economy. However, high uncertainty in existing estimates of lake evaporation prevents sound water resource decision making. To address this uncertainty, buoy- and shore-based eddy covariance and meteorological instrumentation were deployed on the largest of the valley lakes, Lake Okanagan, for approximately 3 years. The objectives were to address the uncertainty in existing Lake Okanagan evaporation estimates by describing seasonal cycles and annual rates and the meteorological attributes controlling evaporation, and developing an accurate and useful model suitable for water managers and policy makers. Results indicate that two sites on Lake Okanagan experienced average annual evaporation of 725 and 835 mm over the study period. The difference can be attributed to spatial differences in surface water temperatures, vapour pressure gradients and atmospheric stability across the lake. Good relationships were found between evaporation rates measured with the eddy covariance systems and meteorological conditions at the offshore buoys, specifically between wind speed and the surface–atmospheric vapour pressure differences. From these relationships, a mass transfer model was developed. Accounting for the seasonal cycle in atmospheric stability increased the accuracy of monthly and annual evaporation estimates from this mass transfer model, but it remains inappropriate to predict daily or hourly evaporation. The study period included years that were climatically typical, so the evaporation observations could represent values close to the long-term mean, but this is unknown. The findings of this study highlight that long-term observations of the atmosphere consistently conditioned to the lake surface are needed for water managers and decision makers to have sound data and information on lake evaporation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".