Future shift in winter streamflow modulated by internal variability of climate in southern Ontario
Bibliographic record
Abstract
Abstract. Fluvial systems in southern Ontario are regularly affected by widespread early-spring flood events primarily caused by rain-on-snow events. Recent studies have shown an increase in winter floods in this region due to increasing winter temperature and precipitation. Streamflow simulations are associated with uncertainties tied to the internal variability of climate. These uncertainties can be assessed using hydrological models fed by downscaled Global Climate Model Large Ensemble (GCM-LE) data. The Canadian Regional Climate Model Large Ensemble (CRCM5-LE), a dynamically downscaled version of a GCM-LE, was developed to simulate climate variability over northeastern North America under different future climate scenarios. In this study, CRCM5-LE temperature and precipitation projections under RCP 8.5 scenario were used as input in the Precipitation Runoff Modelling System (PRMS) to simulate near future (2040s) streamflow for four watersheds in southern Ontario. Model simulations show that 14 % of the ensemble project a high (low) increase of streamflow volume in January-February. Streamflow increases may be driven by rain and snowmelt modulation caused by the development of high (low) pressure anomalies in North America’s East Coast. Additionally, the streamflow may be enhanced by high pressure circulation patterns directly over the Great Lakes creating warm conditions and increasing snowmelt and rainfall/snowfall ratio (16 %). These results are important to assess the internal variability of the hydrological projections and to inform society of increased winter streamflow.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".