The 2011 ASHS Annual Conference

A spatially explicit mechanistic model, MAESTRA, was used to separate key parameters affecting transpiration to provide insights into the most influential parameters for accurate predictions of within crown and canopy transpiration.  Once validated among Acer rubrum L. genotypes, model responses to different parameterization scenarios were scaled up to stand transpiration (expressed per unit leaf area) to assess how transpiration might be affected by the spatial distribution of foliage properties.  An in silico within canopy sensitivity analysis was conducted over the range of genotype parameter variation observed and under different climate forcing conditions.  The analysis revealed seven of 16 leaf traits had a 5% or greater impact on transpiration predictions.  Under sparse foliage conditions, comparisons of our findings with previous studies were in agreement that parameters such as the maximum Rubisco-limited rate of photosynthesis can explain ≈ 20% of the variability in predicted transpiration.  However, the spatial analysis shows how such parameters can decrease or change in importance below the upper most canopy layer.  Alternatively, model sensitivity to leaf width and minimum stomatal conductance was continuous along a vertical canopy depth profile.  Foremost, we examine transpiration sensitivity to an observed range of morphological and physiological parameters and identify the spatial sensitivity of transpiration model predictions to vertical variations in micro-climate and foliage density to reduce the uncertainty of current transpiration predictions.