Water temperature modelling in a small forested stream: implication of forest canopy and soil temperature
Bibliographic record
Abstract
The demand for comprehensive environmental assessment of river ecosystem has increased for engineers and scientists. Accurate and versatile water temperature models are required to meet this demand. A number of hydrological models take vegetation and soil characteristics into account, but very few temperature models do. The objective of this paper is to incorporate soil temperature and vegetation as input variables in a deterministic heat budget model. The CEQUEAU hydrological and water temperature model was used to simulate water temperature in Catamaran Brook, a small catchment located in central New Brunswick. The model was modified by incorporating soil temperature as a parameter influencing the temperature of interflow, using the so-called force-restore method. Crown closure was also incorporated in the model as a factor influencing locally advected water using a negative exponential function. The modified model simulated daily water temperatures better than the original model. Root-mean-square error for a period of 5 years decreased from 2.10°C with the original model to 1.77°C with the modified model. Nash coefficient increased from 0.78 with the original model to 0.82 with the modified model. An analysis of residuals showed that the modified model is sensitive to additional parameters such as crown closure, especially for short time scales during periods of higher discharge and during extreme meteorological and hydrological events such as tropical storms.Key words: stream temperature, hydrology, deterministic model, CEQUEAU, forestry.
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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 | 0.000 | 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".