Most published process models of the growth of forest stands are concerned predominantly with either
tree physiology or nutrient cycling, concentrating respectively on photosynthetic carbon gain and
allocation, or on decomposition and nutrient uptake processes. Mechanistic formulations of direct CO2
effects on photosynthesis have been incorporated in some physiology-based models, whereas
modifications incorporating direct CO2 effects in nutrient-driven models have usually been more
empirical. Physiology-based models predict considerable CO2-fertiliser effects, while nutrient driven
models tend to be less sensitive to elevated ambient CO2 concentration (Ca). This paper describes how
effects of elevated Ca can be incorporated in these various types of forest growth models.
The magnitude of the simulated response to elevated Ca varies markedly depending on a particular
model's spatial and temporal resolution and on which processes are incorporated. Two physiology-
based models of forest canopy processes (MAESTRO and BIOMASS) and a plant-soil model (G'DAY)
are considered here. MAESTRO and BIOMASS incorporate mechanistic descriptions of the
biochemical basis of photosynthesis by C3 plants, while G'DAY contains a simplified formulation but
includes soil processes. All three models are used to simulate the response to an instantaneous doubling
of Ca. Simulations of MAESTRO and BIOMASS show that on a clear day total canopy photsynthesis
is temperature-dependent with increases of approximately 10, 45 and 70% at 10. 25 and 40°C
respectively. A simulation for a stand of Pinus radiata growing with abundant water and nutrients and
mean annual day-time temperature of 14.8°C shows an increase of 25% in annual canopy
photosynthesis. On nutrient-limited sites plant responses to elevated Ca are constrained by feedbacks
associated with rates of decomposition and nutrient cycling. According to the G'DAY model, which
incorporates these feedbacks, an instantaneous doubling of Ca leads to a 27% initial productivity
increase lasting less than a decade and a more modest increase of 8% sustained in the long term.
Predicting long-term streamflow variability in moist eucalypt forests using forest growth models and a sapwood area index
