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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 4
Geosci. Model Dev., 7, 1357–1376, 2014
https://doi.org/10.5194/gmd-7-1357-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 7, 1357–1376, 2014
https://doi.org/10.5194/gmd-7-1357-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Model description paper 10 Jul 2014

Model description paper | 10 Jul 2014

IPR 1.0: an efficient method for calculating solar radiation absorbed by individual plants in sparse heterogeneous woody plant communities

Y. Zhang, W. Chen, and J. Li Y. Zhang et al.
  • Canada Centre for Mapping and Earth Observation, Natural Resources Canada, Ottawa, Canada

Abstract. 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.

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