Impacts of Hydrological Processes on Stream Temperature in a Cold Region Watershed Based on the SWAT Equilibrium Temperature Model
Bibliographic record
Abstract
Variance in stream temperature from historical norms, which reflects the impacts from both hydrological and meteorological factors, is a significant indicator of the stream ecosystem health. Therefore, it is imperative to study the hydrological processes controlling stream temperature in the watershed. The impacts of hydrological processes on stream temperature in the cold region of Western Canada were investigated based on the previously developed Soil and Water Assessment Tool (SWAT) equilibrium temperature model. The model was calibrated and validated for streamflow and stream temperature based on the observations and a global parameter sensitivity analysis conducted to identify the most important hydrological process governing the stream temperature dynamics. The precipitation and air temperature lapse rates were found to be the most sensitive parameters controlling the stream temperature, followed by the parameters regulating the processes of soil water dynamics, surface runoff, and channel routing. Our analysis showed an inverse relationship between streamflow volume and stream temperature, and different runoff components have different impacts on temporal regimes of stream temperatures. This study elaborates on the response of the stream temperature to changes in hydrological processes at the watershed scale and indicates that hydrological processes should be taken into account for prediction of stream temperatures.
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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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".