Modelling Climate Change and Water Quality in the Canadian Prairies Using Loosely Coupled WASP and CE-QUAL-W2
Bibliographic record
Abstract
The prairie waterbodies face a future of warming temperatures and growing water demands. There are increasing concerns about how water quality will be affected. Water quality models are an effective tool for examining scenarios of future conditions that cannot be measured directly. This study combined WASP and CE-QUAL-W2 to investigate the potential impacts of changing flow management and climate change in the Canadian Prairies. The two models were loosely coupled to simulate a strategically managed river-reservoir network. Climate data from the Coupled Model Intercomparison Project Phase 6 (CMIP6) model ensemble were used to create future climate scenarios. Interbasin water transfers were then simulated through the coupled models to determine if any negative impacts from climate change on water quality could be offset through flow management. Climate change impacts in the river stretch were minimised due to the rapid flow travel time along the channel. The interbasin water transfers had a greater influence on water quality concentrations in the river. This result was limited by the uncertain hydro-climatic future of the contributing watershed. Climate change impacts in the downstream reservoir were far more apparent. Evaporative losses increased approximately 150% from the base model by the 2080–2100 period. Chlorophyll-a concentrations increased an average of 53% in this same period based on monthly mean percentage change. Reservoir water quality was improved after adding the interbasin water transfers. Results indicated that flow management would have a positive impact on water quality in the reservoir in the face of future climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".