Water Temperature and Hydrological Modelling in the Context of Environmental Flows and Future Climate Change: Case Study of the Wilmot River (Canada)
Bibliographic record
Abstract
Simulation of surface water flow and temperature under a non-stationary, anthropogenically impacted climate is critical for water resource decision makers, especially in the context of environmental flow determination. Two climate change scenarios were employed to predict streamflow and temperature: RCP 8.5, the most pessimistic with regards to climate change, and RCP 4.5, a more optimistic scenario where greenhouse gas emissions peak in 2040. Two periods, 2018–2050 and 2051–2100, were also evaluated. In Canada, a number of modelling studies have shown that many regions will likely be faced with higher winter flow and lower summer flows. The CEQUEAU hydrological and water temperature model was calibrated and validated for the Wilmot River, Canada, using historic data for flow and temperature. Total annual precipitation in the region was found to remain stable under RCP 4.5 and increase over time under RCP 8.5. Median stream flow was expected to increase over present levels in the low flow months of August and September. However, increased climate variability led to higher numbers of periodic extreme low flow events and little change to the frequency of extreme high flow events. The effective increase in water temperature was four-fold greater in winter with an approximate mean difference of 4 °C, while the change was only 1 °C in summer. Overall implications for native coldwater fishes and water abstraction are not severe, except for the potential for more variability, and hence periodic extreme low flow/high temperature events.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".