Towards the End of the Century: Hydrodynamic and Water Quality Modeling for Lake Diefenbaker in the Presence of Climate Change
Bibliographic record
Abstract
This study examines how climate change affects the water quality of Lake Diefenbaker, a water reservoir in Saskatchewan that supplies water to more than a million people. The focus is on key water quality parameters, including temperature, Dissolved Oxygen (DO), Total Nitrogen (TN), and Total Phosphorus (TP). For this purpose, a robust framework for modeling water quality using the CE-QUAL-W2 model is established to simulate conditions up to the late 21st century and analyze potential climatic effects on the lake's water quality. The model was calibrated and validated using observed data. The MESH (Modélisation Environnementale communautaire – Surface Hydrology) hydrological model provided inflow and outflow data from 2011 to 2100 as inputs for the CE-QUAL-W2 model. Future scenarios for 2041-2050 and 2081-2090 are projected using the SSP5-8.5 climate scenario and nutrient concentrations from the SPARROW (SPAtially Referenced Regression On Watershed Attributes) model. Two strategic locations near Gardiner and Qu’Appelle Dams were selected for detailed analysis, focusing on the epilimnion, metalimnion, and hypolimnion layers. The findings suggest alterations in water quality towards the end of the century. Water temperatures are predicted to increase by up to 4°C during the summer and fall seasons, with a corresponding 20% decrease expected for DO levels – particularly evident towards late summer – posing a greater threat for hypoxia. TN and TP concentrations are projected to nearly double, increasing the risk of eutrophication. The findings of this study highlight the risk of water quality degradation in the presence of future climate conditions. These findings emphasize the necessity to develop adaptive water management strategies to help Lake Diefenbaker continue to be a vital water resource for various uses. The model’s results will also provide insights for studies on other connected water systems downstream of Gardiner Dam and Qu’Appelle Dams.
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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 | 0.001 | 0.001 |
| 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 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".