Effect of Climate Change on Low-Flow Conditions in the Ruscom River Watershed, Ontario
Bibliographic record
Abstract
The objective of the present study is to explore and project the effect of climate change on the low flows from the Ruscom River watershed in Ontario, Canada. The watershed is one of the subwatersheds draining into Lake St. Clair on the Canadian side of the Great Lakes system. The Soil and Water Assessment Tool (SWAT) model was implemented to simulate the hydrologic regime in the watershed. SWAT was calibrated and validated for the streamflow from the Ruscom River watershed using the observed monthly flow data. The LARS-WG weather generator was used for the generation of daily future weather data at local scale using the Canadian Regional Climate Model (CRCM) outputs under the SRES A2 scenario for the period 2041-2070. The Nash-Sutcliffe efficiency (NSE) and coefficient of determination (r2) for streamflow predictions using SWAT were found to be greater than 0.74. Under the projected climate scenario, the future mean monthly minimum and maximum temperatures by the year 2070 may be increased by 3.2C and 3.6C, respectively, compared to the temperatures in the base period (1961-1990). The average annual precipitation would also increase by 8%. SWAT-simulated flow duration curves indicated that low flows in the Ruscom River would be increased in spring but decreased in summer and fall due to the possible climate change conditions. Based on the frequency analysis, the annual minimum monthly flow of five-year return period could be reduced by as much as 50%.
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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 | 0.001 | 0.001 |
| 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".