A comparison of parametrizations of canopy conductance of aspen and Douglas‐fir forests for class
Bibliographic record
Abstract
Canopy conductance (gc) of an old boreal aspen forest and a west coast Douglas‐fir forest was calculated from the inversion of the Penman‐Monteith (PM) equation with above‐canopy water vapour flux measurements. Values of aspen gc agreed reasonably well with those obtained by scaling up from leafstomatal conductance measurements. Comparison of values of gc obtained from the CLASS (Canadian LAnd Surface Scheme) parametrization with values of Douglas‐fir gc in 1983 and 1984 calculated from the PM equation showed that the CLASS parametrization (based on the Jarvis‐Stewart (JS) model) worked well at high soil water potential (ψ), but underestimated gc at low ψ. In the case of the aspen forest during a wet growing season in 1994, the CLASS parametrization underestimated gc for high values of incident photosynthetic photon flux density. The effectiveness of three parametrizations of gc, developed using linear or non‐linear least squares analysis, was evaluated for the two forests. The first (based on the JS model), related gc to the product of several independent limiting functions, the second (based on the Ball‐Woo‐drow‐Berry (B WB) model related gc to the product of canopy net assimilation rate and canopy surface relative humidity divided by canopy surface CO2 concentration, and the third (based on a modified form of the BWB (MBWB) model) was the same as the second except that the relative humidity was replaced by the reciprocal of air vapour pressure deficit. For both forests, the JS parametrization gave the highest r2 and lowest root mean square (RMS) error. The RMS error of the MBWB parametrization was less than that of the BWB parametrization because the latter underestimated gc during the morning. With the incorporation of the new JS and MBWB parametrizations into CLASS, better estimates of the latent heat flux (QE) from the aspen and Douglas‐fir forests were obtained on half‐hourly and daily bases than with the original CLASS parametrization. The JS parametrization gave better estimates than the MBWB parametrization. Both models parametrized using 1994 data from the aspen forest were successfully applied to the same stand in 1996, which also had a relatively wet growing season. Both models parametrized using data from the Douglas‐fir forest were also applied to four other similar‐aged Douglas‐fir forests but with different values of the leaf area index. Under conditions of minimal water stress, better estimates of QE were obtained for three of the four forests using both parametrizations. In the case of the fourth forest, none of the parametrizations gave satisfactory estimates. This was likely because the initial conditions of soil water content and ψ used in CLASS for the gravelly soil was significantly overestimated as a result of not taking the stone content into account. For conditions of high water stress, which occurred in two of the forests, none of the parametrizations gave satisfactory estimates. However, when the ψ limiting function in the JS parametrization was replaced by that developed from measurements made in the other two forests, the JS parametrization gave reasonable estimates of QE. In the case of the MBWB parametrization, we were unable to adjust the ψ limiting function due to the lack of measurements of canopy net assimilation rate at these two sites.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".