A comparison of parametrizations of canopy conductance of aspen and Douglas‐fir forests for class
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».