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A simple calibrated model of Amazon rainforest productivity based on leaf biochemical properties

Authors


Jon Lloyd, Environmental Biology Group, Research School of Biological Sciences, Australian National University, GPO Box 475, Canberra, ACT 2601, Australia.

ABSTRACT

A simple ‘big leaf’ ecosystem gas exchange model was developed, using eddy covariance data collected at an undisturbed tropical rainforest in south-western Amazonia (Brazil). The model used mechanistic equations of canopy biochemistry combined with an empirical stomatal model describing responses to light, temperature and humidity. After calibration, the model was driven using hourly data from a weather station at the top of the tower at the measurement site, yielding an estimate of gross primary productivity (annual photosynthesis) in 1992/1993 of about 200 mol C m−2 year . Although incoming photon flux density emerged as the major control on photosynthesis in this forest, at a given PAR CO2 assimilation rates were higher in the mornings than in the afternoons. This was attributable to stomatal closure in the afternoon in response to increasing canopy-to-air vapour pressure differences. Although most morning gas exchange was clearly limited by the rate of electron transport, afternoon gas exchange was generally observed to be very nearly co-limited by both Rubisco activity (Vmax) and electron transport rate. The sensitivity of the model to changes in nitrogen allocation showed that the modelled ratio of Vmax to electron transport (Jmax) served nearly to maximize the annual carbon gain, and indeed, would have resulted in almost maximum annual carbon gain at the pre-industrial revolution atmospheric CO2 concentration of 27 Pa. Modelled gross primary productivity (GPP) was somewhat lower at 27 Pa, being about 160 mol C m−2 year−1. The model suggests that, in the absence of any negative feedbacks on GPP, future higher concentrations of atmospheric CO2 will continue to increase the GPP of this rainforest, up to about 230 mol C m−2 year−1 at 70 Pa.

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