Predicting Latent and Sensible Heat Fluxes in Stream Temperature Models: Current Challenges and Potential Solutions
Bibliographic record
Abstract
Abstract Stream temperature studies typically use either the Penman combination equation (PCE) or empirical wind functions to compute the latent heat flux, which can be an important control on daily maximum water temperature. The sensible heat flux is usually computed from the latent heat flux via the Bowen ratio or a bulk transfer model. Unfortunately, both the PCE and empirical wind functions involve challenges. The PCE is inappropriate for application to subdaily stream evaporation because, as implemented in many stream temperature studies, it does not account for energy associated with warming or cooling of the water column or bed heat fluxes. The PCE thus tends to overestimate evaporation under conditions typical of warm summer days during the diurnal warming phase, which is when evaporation is likely to be an important heat loss mechanism. Empirical wind functions involve substantial uncertainty given the broad range of coefficient values that have been reported in the literature and the lack of above‐stream meteorological data to use in their application. We provide suggestions for improving current practice and also identify research directions to support development of robust approaches to computing the latent and sensible heat fluxes.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".