Exploring Climate Change Impacts on Temperature Extremes in the South Saskatchewan River Watershed, Alberta, Canada
Bibliographic record
Abstract
In recent years, the frequency and intensity of extreme temperature events have escalated, posing unprecedented challenges to ecosystems, economies, and human health. As global temperatures rise, these events are emerging as critical threats; therefore, understanding their changes is essential for developing strategies to mitigate their growing risks under global warming. In this study, we used ETCCDI (Expert Team on Climate Change Detection, Monitoring, and Indices) temperature indices to analyze future changes in extreme temperature events in the South Saskatchewan River Watershed (SSRW) in Southern Alberta, Canada, a critical area for irrigation, agriculture, and food production. This analysis is based on an ensemble of 26 Global Circulation Models (GCMs) and three Shared Socio-economic Pathway (SSP) scenarios, in three periods (2015–2030, 2041–2060, 2071–2100), compared against the base period of 1951–1990. The results suggest substantial changes in most of the studied indices towards higher temperatures, with a significant rise in both the numbers of days with extreme temperatures and the magnitude of the temperature itself, as well as a notable drop in the number of cold days. As a result of warming, the growing season length is 16 days longer in 2015–2030 compared to the base period and is projected to increase substantially by the end of the century. A longer growing season might seem favourable for agriculture, but it can disrupt water availability and cause unpleasant environmental consequences. Overall, the scenarios considered in this research suggest that the SSRW could experience warming at a significant rate. This finding underscores the urgent need for adaptation and mitigation actions to enhance resilience and sustainability.
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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 | 0.000 | 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 | 0.000 | 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) | 0.003 | 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".