Insights on stream temperature processes through development of a coupled hydrologic and stream temperature model for forested coastal headwater catchments
Bibliographic record
Abstract
Abstract Stream temperature controls a number of biological, chemical, and physical processes occurring in aquatic environments. Transient snow cover and advection associated with lateral throughflow inputs can have a dominant influence on stream thermal regimes for headwater catchments in the rain‐on‐snow zone. Most existing stream temperature models lack the ability to properly simulate these processes. We developed and evaluated a conceptual‐parametric catchment‐scale stream temperature model that includes the role of transient snow cover and lateral advection associated with throughflow. The model consists of routines for simulating canopy interception, snow accumulation and melt, hillslope throughflow runoff and temperature, and stream channel energy exchange processes. The model was used to predict discharge and stream temperature for a small forested headwater catchment near Vancouver, Canada, using long‐term (1963–2013) weather data to compute model forcing variables. The model was evaluated against 4 years of observed stream temperature. The model generally predicted daily mean stream temperature accurately (annual RMSE between 0.57 and 1.24 °C) although it overpredicted daily summer stream temperatures by up to 3 °C during extended low streamflow conditions. Model development and testing provided insights on the roles of advection associated with lateral throughflow, channel interception of snow, and surface–subsurface water interactions on stream thermal regimes. This study shows that a relatively simple but process‐based model can provide reasonable stream temperature predictions for forested headwater catchments located in the rain‐on‐snow zone.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".