Quantifying the Impacts of Climate Change on Streamflow Dynamics of Two Major Rivers of the Northern Lake Erie Basin in Canada
Bibliographic record
Abstract
This paper focuses on understanding the effects of projected climate change on streamflow dynamics of the Grand and Thames rivers of the Northern Lake Erie (NLE) basin. A soil water assessment tool (SWAT) model is developed, calibrated, and validated in a base-period. The model is able to simulate the monthly streamflow dynamics with ‘Good’ to ‘Very Good’ accuracy. The calibrated and validated model is then subjected with daily bias-corrected future climatic data from the Canadian Regional Climate Model (CanRCM4). Five bias-correction methods and their 12 combinations were evaluated using the Climate Model data for hydrologic modeling (CMhyd). Distribution mapping (DM) performed the best and was used for further analysis. Two future time-periods and two IPCC AR5 representative concentration pathways (RCPs) are considered. Results showed marked temporal and spatial variability in precipitation (−37% to +63%) and temperature (−3 °C to +14 °C) changes, which are reflected in evapotranspiration (−52% to +412%) and soil water storage (−60% to +12%) changes, resulting in heterogeneity in streamflow (−77% to +170%) changes. On average, increases in winter (+11%), and decreases in spring (–33%), summer (−23%), and autumn (−15%) streamflow are expected in future. This is the first work of this kind in the NLE and such marked variability in water resources availability poses considerable challenges to water resources planners and managers.
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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 | 0.000 | 0.001 |
| 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) | 0.000 | 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 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".