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TitreIPR 1.0: An efficient method for calculating solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities
AuteurZhang, Y; Chen, W; Li, J
SourceGeoscientific Model Development vol. 7, issue 4, 2014 p. 1357-1376, https://doi.org/10.5194/gmd-7-1357-2014
Année2014
Séries alt.Secteur des sciences de la Terre, Contribution externe 20100075
ÉditeurElsevier
Documentpublication en série
Lang.anglais
DOIhttps://doi.org/10.5194/gmd-7-1357-2014
Mediapapier; numérique; en ligne
Formatspdf
Sujetstélédétection; établissement de modèles; effets climatiques; végétation; écosystèmes; écologie
Illustrationsdiagrams; graphs
ProgrammeImpacts des changements climatiques et adaptation dans le secteur des ressources naturelles et d'autres secteurs clés de l'économie, Géosciences de changements climatiques
Résumé(disponible en anglais seulement)
Climate change may alter the spatial distribution, composition, structure and functions of plant communities. Transitional zones between biomes, or ecotones, are particularly sensitive to climate change. Ecotones are usually heterogeneous with sparse trees. The dynamics of ecotones are mainly determined by the growth and competition of individual plants in the communities. Therefore it is necessary to calculate the solar radiation absorbed by individual plants in order to understand and predict their responses to climate change. In this study, we developed an individual plant radiation model, IPR (version 1.0), to calculate solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities. The model is developed based on geometrical optical relationships assuming that crowns of woody plants are rectangular boxes with uniform leaf area density. The model calculates the fractions of sunlit and shaded leaf
classes and the solar radiation absorbed by each class, including direct radiation from the sun, diffuse radiation from the sky, and scattered radiation from the plant community. The solar radiation received on the ground is also calculated. We tested the model by comparing with the results of random distribution of plants. The tests show that the model results are very close to the averages of the random distributions. This model is efficient in computation, and can be included in vegetation models to simulate long-term transient
responses of plant communities to climate change. The code and a user's manual are provided as Supplement of the paper.
GEOSCAN ID285517