Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites

Zhou Yanlian, Wu Xiaocui, Ju Weimin, Chen Jiquan, Wang Shaoqiang, Wang Huimin, Yuan Wenping, Black Andrew, Jassal Rachhpal, Ibrom Andreas, Han Shijie, Yan Junhua, Margolis Hank, Roupsard Olivier, Li Yingniam, Zhao Fenghua, Kiely Gérard, Starr Gregory, Pavelka Marian, Montagnani Leonardo, Wohlfahrt Georg, D'Odorico Petra, Cook David, Arain M. Altaf, Bonal Damien, Beringer Jason, Blanken Peter D., Loubet Benjamin, Leclerc Monique Y., Matteucci Giorgio, Nagy Zoltan, Olejnik Janusz, Paw U Kyaw Tha, Varlagin Andrej. 2016. Global parameterization and validation of a two-leaf light use efficiency model for predicting gross primary production across FLUXNET sites. Journal of Geophysical Research. Biogeosciences, 121 (4) : pp. 1045-1072.

Journal article ; Article de revue à facteur d'impact
Published version - Anglais
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Abstract : Light use efficiency (LUE) models are widely used to simulate gross primary production (GPP). However, the treatment of the plant canopy as a big leaf by these models can introduce large uncertainties in simulated GPP. Recently, a two-leaf light use efficiency (TL-LUE) model was developed to simulate GPP separately for sunlit and shaded leaves and has been shown to outperform the big-leaf MOD17 model at six FLUX sites in China. In this study we investigated the performance of the TL-LUE model for a wider range of biomes. For this we optimized the parameters and tested the TL-LUE model using data from 98 FLUXNET sites which are distributed across the globe. The results showed that the TL-LUE model performed in general better than the MOD17 model in simulating 8 day GPP. Optimized maximum light use efficiency of shaded leaves (εmsh) was 2.63 to 4.59 times that of sunlit leaves (εmsu). Generally, the relationships of εmsh and εmsu with εmax were well described by linear equations, indicating the existence of general patterns across biomes. GPP simulated by the TL-LUE model was much less sensitive to biases in the photosynthetically active radiation (PAR) input than the MOD17 model. The results of this study suggest that the proposed TL-LUE model has the potential for simulating regional and global GPP of terrestrial ecosystems, and it is more robust with regard to usual biases in input data than existing approaches which neglect the bimodal within-canopy distribution of PAR.

Mots-clés Agrovoc : forêt tropicale, Couverture végétale, couverture du sol, Cycle du carbone, Photosynthèse, biomasse aérienne des arbres, Biomasse, séquestration du carbone, Écosystème, Lumière, Méthode statistique, Modèle de simulation, cartographie des fonctions de la forêt

Mots-clés géographiques Agrovoc : Chine

Classification Agris : K01 - Forestry - General aspects
U10 - Computer science, mathematics and statistics
F60 - Plant physiology and biochemistry
F40 - Plant ecology

Champ stratégique Cirad : Axe 6 (2014-2018) - Sociétés, natures et territoires

Auteurs et affiliations

  • Zhou Yanlian, Nanjing University of Information Science and Technology (CHN)
  • Wu Xiaocui, Nanjing University of Information Science and Technology (CHN)
  • Ju Weimin, Nanjing University of Information Science and Technology (CHN)
  • Chen Jiquan, Nanjing University of Information Science and Technology (CHN)
  • Wang Shaoqiang, Chinese Academy of Sciences (CHN)
  • Wang Huimin, Chinese Academy of Sciences (CHN)
  • Yuan Wenping, Beijing Normal University (CHN)
  • Black Andrew, University of British Columbia (CAN)
  • Jassal Rachhpal, University of British Columbia (CAN)
  • Ibrom Andreas, Technical University of Denmark (DNK)
  • Han Shijie, Chinese Academy of Sciences (CHN)
  • Yan Junhua, Chinese Academy of Sciences (CHN)
  • Margolis Hank, Université de Laval (CAN)
  • Roupsard Olivier, CIRAD-PERSYST-UMR Eco&Sols (CRI)
  • Li Yingniam, Chinese Academy of Sciences (CHN)
  • Zhao Fenghua, Chinese Academy of Sciences (CHN)
  • Kiely Gérard, UCC (IRL)
  • Starr Gregory, Alabama A and M University (USA)
  • Pavelka Marian, Institute of Systems Biology and Ecology (CZE)
  • Montagnani Leonardo, Forest Services and Agency for the Environment (ITA)
  • Wohlfahrt Georg, Institute of Ecology (AUT)
  • D'Odorico Petra, ETH (CHE)
  • Cook David, Argonne National Laboratory (USA)
  • Arain M. Altaf, McMaster University (CAN)
  • Bonal Damien, INRA (FRA)
  • Beringer Jason, University of Western Australia (AUS)
  • Blanken Peter D., University of Colorado (USA)
  • Loubet Benjamin, INRA (FRA)
  • Leclerc Monique Y., University of Georgia (USA)
  • Matteucci Giorgio, Università degli studi della Tuscia (ITA)
  • Nagy Zoltan, Szent Istvan University (HUN)
  • Olejnik Janusz, Poznan University of Life Sciences (POL)
  • Paw U Kyaw Tha, UC (USA)
  • Varlagin Andrej, Russian Academy of Sciences (RUS)

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