Hydrological Extremes in the Canadian Prairies in the Last Decade due to the ENSO Teleconnection—A Comparative Case Study Using WRF
Bibliographic record
Abstract
In the Prairie provinces of Alberta, Saskatchewan, and Manitoba, agricultural production depends on winter and spring precipitation. There is large interannual variability related to the teleconnection between the regional hydroclimate and El Niño and La Niña in the Tropical Pacific. A modeling experiment was conducted to simulate climatic and hydrological parameters in the Canadian Prairie region during strong El Niño and La Niña events of the last decade in 2015–2016 and 2010–2011, respectively. The National Center for Atmospheric Research (NCAR) Weather Research and Forecasting (WRF) model was employed to perform two sets of sensitivity experiments with a nested domain at 10 km resolution using the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA) interim data as the lateral boundary forcing. Analysis of the hourly model output provides a detailed simulation of the drier winter, with less soil moisture in the following spring, during the 2015–2016 El Niño and a wet winter during the La Niña of 2010–2011. The high-resolution WRF simulation of these recent weather events agrees well with observations from weather stations and water gauges. Therefore, we were able to take advantage of the WRF model to simulate recent weather with high spatial and temporal resolution and thus study the changes in hydrometeorological parameters across the Prairie during the two extreme hydrological events of the last decade.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".