A stochastic assessment of climate change impacts on precipitation and potential evaporation in Alberta
Bibliographic record
Abstract
In many climate change investigations, changes in precipitation are projected under various scenarios; however, changes in evaporation have received relatively less attention. For irrigation and water resources management, the difference between potential evaporation and precipitation can provide better quantification of local water availability and drought conditions. Therefore, projecting joint variations in precipitation and potential evaporation can provide better information for climate change adaptation. A stochastic approach based on a Generalised Linear Model (GLM) framework is proposed to study these together at a station scale. Eight stations in Alberta are selected for which historical pan evaporation records and up-to-date meteorological information are available. Results show that potential evaporation estimated from Global Circulation Models directly can be unreliable. The evaporation ensemble simulated by the GLM approach can represent observed evaporation more realistically and provide better uncertainty quantification. If only simulated precipitation is considered, the projected drought conditions in the 2080s are likely to be less severe than that in the 2000s. However, the projected difference between precipitation and evaporation (water deficit) shows that the future drought conditions may be higher or lower, varying between the stations. Implications of the results and further development of the proposed approach to address spatial dependence between stations are also discussed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".