Applicability of the Bulk-Transfer Approach to Estimate Evapotranspiration from Boreal Peatlands
Bibliographic record
Abstract
Abstract In northern landscapes, peatlands are widespread and their hydrological processes are complex. Furthermore, they are typically remote, limiting the amount and accuracy of in situ measurements. This is especially the case for evapotranspiration ET, which strongly influences watershed hydrology. The objective of this paper is to demonstrate the validity of the bulk-transfer approach to estimate ET over boreal peatlands. The simplicity of the model relies on four assumptions: (i) near-neutral atmospheric conditions; (ii) wet surface; (iii) constant momentum roughness length depending on vegetation height; and (iv) constant water vapor roughness length, with the last two assumptions implying a constant water vapor transfer coefficient CE. Using eddy covariance data from three Canadian peatlands—Necopastic (James Bay, Québec), Mer Bleue (Ottawa, Ontario), and Western Peatland (Athabasca, Alberta)—this paper shows that these sites are characterized by frequent occurrences of near-neutral atmospheric conditions, especially the Necopastic site, with nearly 76% of the 30-min data segments occurring under near-neutral stratification. The analysis suggests these near-neutral conditions occur as a result of strong mechanical turbulence and weak buoyancy effects. The bulk-transfer approach gives promising results for 30-min and daily ET in terms of mean error and correlation, with performances similar to the Penman equation, without requiring net radiation data. The accuracy of the approach is likely related to the number of near-neutral periods and the elevated position of the water table, which backs up the wet surface assumption.
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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 | 0.000 |
| 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) | 0.001 | 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".