Climate Change Impacts in the Elbow River Watershed
Bibliographic record
Abstract
The Elbow River Watershed originates in the foothills of the Rocky Mountains and is a primary source of water for the City of Calgary. Consequently, the long-term protection and investment of this resource is a primary interest to the City of Calgary. While droughts and water shortages are a serious concern to Albertans, spring freshet flooding may lead to enormous costs virtually overnight. The impacts of climate change on spring flooding in the Elbow River Watershed were determined using both a statistical analysis of historical hydro-climatological data and a modelling analysis using the Canadian Regional Climate Model (CRCM) forcing to the SSARR Watershed model, which is used by Alberta Environment for flood forecasting. Statistical analyses revealed that there were significantly increasing trends in annual mean temperature in the eastern most part of the watershed (+0.007°C/yr) caused by significant trends during February and March only. Significantly increasing trends in annual mean temperature in the western portion of the watershed were also observed (+0.056°C/yr) and were primarily due to increases in January, March, April, July and August. There were no demonstrated trends in total annual precipitation but significant decreases in snowfall were observed in the eastern portion of the watershed. Conversely, increases in snowfall were observed in the western portion near the foothills. No significant trends were observed in discharges within this watershed. Modelling spring freshet flooding using the SSARR and CRCM models showed that spring time flooding due to expected increases in precipitation during the month of May can nearly double flood peaks.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".