Spatial variations and long-term trends of potential evaporation in Canada
Bibliographic record
Abstract
Abstract Assessing the status and trend of potential evaporation (PE) is essential for investigating the climate change impact on the terrestrial water cycle. Despite recent advances, evaluating climate change impacts on PE using pan evaporation (Epan) data in cold regions is hindered by the unavailability of Epanmeasurements in cold seasons due to the freezing of water and sparse spatial distribution of sites. This study generated long-term PE datasets in Canada for 1979–2016 by integrating the dynamic evolutions of water–ice–snow processes into estimation in the Ecological Assimilation of Land and Climate Observations (EALCO) model. The datasets were compared with Epanbefore the spatial variations and trends were analyzed. Results show that EALCO PE and Epanmeasurements demonstrate similar seasonal variations and trends in warm seasons in most areas. Annual PE in Canada varied from 100 mm in the Northern Arctic to approximately 1000 mm in southern Canadian Prairies, southern Ontario, and East Coast, with about 600 mm for the entire landmass. Annual PE shows an increasing trend at a rate of 1.5–4 mm/year in the Northern Arctic, East, and West Canada. The increase is primarily associated with the elevated air temperature and downward longwave and shortwave radiation, with some regions contributed by augmented wind speed. The increase of annual PE is mainly attributed to the augmentation of PE in warm seasons.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 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".