Articles | Volume 6, issue 7
Biogeosciences, 6, 1247–1272, 2009

Special issue: Biogeochemistry and function of Amazon Forest

Biogeosciences, 6, 1247–1272, 2009

  28 Jul 2009

28 Jul 2009

Modelling basin-wide variations in Amazon forest productivity – Part 1: Model calibration, evaluation and upscaling functions for canopy photosynthesis

L. M. Mercado1,2, J. Lloyd3, A. J. Dolman4, S. Sitch5, and S. Patiño3,6,2 L. M. Mercado et al.
  • 1Centre for Ecology and Hydrology, Wallingford, Oxon, OX10 8BB, UK
  • 2Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
  • 3School of Geography, University of Leeds, LS2 9JT, UK
  • 4Free University of Amsterdam, 1081 HV Amsterdam, The Netherlands
  • 5Met Office Hadley Centre, JCHMR, Wallingford, Oxon, OX10 8BB, UK
  • 6Instituto de Investigación de Recursos Biológicos Alexander von Humboldt, Diagonal 27 No. 15-09, Bogotá D.C, Colombia

Abstract. Given the importance of Amazon rainforest in the global carbon and hydrological cycles, there is a need to parameterize and validate ecosystem gas exchange and vegetation models for this region in order to adequately simulate present and future carbon and water balances. In this study, a sun and shade canopy gas exchange model is calibrated and evaluated at five rainforest sites using eddy correlation measurements of carbon and energy fluxes.

Results from the model-data evaluation suggest that with adequate parameterisation, photosynthesis models taking into account the separation of diffuse and direct irradiance and the dynamics of sunlit and shaded leaves can accurately represent photosynthesis in these forests. Also, stomatal conductance formulations that only take into account atmospheric demand fail to correctly simulate moisture and CO2 fluxes in forests with a pronounced dry season, particularly during afternoon conditions. Nevertheless, it is also the case that large uncertainties are associated not only with the eddy correlation data, but also with the estimates of ecosystem respiration required for model validation. To accurately simulate Gross Primary Productivity (GPP) and energy partitioning the most critical parameters and model processes are the quantum yield of photosynthetic uptake, the maximum carboxylation capacity of Rubisco, and simulation of stomatal conductance.

Using this model-data synergy, we developed scaling functions to provide estimates of canopy photosynthetic parameters for a range of diverse forests across the Amazon region, utilising the best fitted parameter for maximum carboxylation capacity of Rubisco, and foliar nutrients (N and P) for all sites.

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