Spatial and temporal variability of the solar radiation heat flux in streams of a forested catchment
Bibliographic record
Abstract
Solar radiation is generally the largest contributing flux to the heat budget of streams and its estimation is crucial to predict stream water temperature with process-based models. The objective of this research is to quantify the spatial (between-site comparison of different stream sizes, within-site comparison at the reach scale) and temporal (seasonal, daily and hourly scales) variability in the transmission coefficient, which represents the proportion of incoming solar radiation reaching streams. We measured solar radiation at an open site with a meteorological station and at microclimate sites located in three streams of various sizes in the Miramichi River basin (Canada). During the summer, the percentage of incoming daily solar radiation reaching a stream varied from 8% in a small headwater stream (Trib) to 43% in a medium-sized stream (CatBk) and was close to 100% in a wide river (LSWM). We observed the largest variability between transmission coefficients for different stream sizes (range of variation = 92%) due to very different canopy closures, followed by variability at the reach scale between lateral positions (range = 21% between left and right banks) and between longitudinal positions (range = 11% between upstream and downstream sites), as measured at the medium-sized stream. Temporal variability was greatest at the seasonal scale where the transmission coefficient varied by 23% between May and September at the small headwater stream. The hourly variability of the transmission coefficient (i.e. associated with different solar angles) surpassed daily variability (i.e. associated with different cloud cover conditions), with coefficients of variation computed at the hourly time scale three to five times greater than at the daily time scale. Overall, this research offers insight regarding the handling of spatial and temporal variability of solar radiation which should provide further insight to improve process-based stream temperature models.
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".