Carbon and Water Flux Responses to Physiology by Environment Interactions: A Sensitivity Analysis of Climate Impacts on Biophysical Model Parameters

Tuesday, July 23, 2013: 9:00 AM
Desert Salon 13-14 (Desert Springs J.W Marriott Resort )
William L. Bauerle , Department of Horticulture and Landscape Architecture, Colorado State University, Fort Collins
Alex B. Daniels , Horticulture and Landscape Architecture, Colorado State University, Fort Collins
Dave M. Barnard , Horticulture and Landscape Architecture, Colorado State University, Fort Collins
Leaf physiological traits are key factors in carbon and water exchange, providing important vegetation constraints on crop production. Sensitivity of carbon uptake and water use estimates to changes in physiology was determined with a coupled photosynthesis and stomatal conductance (gs) model, linked to canopy microclimate with a spatially explicit scheme (MAESTRA). The sensitivity analyses were conducted over the range of physiology parameter variation observed for Acer rubrum L. (intraspecific) and woody deciduous C3 (C3) vegetation under different climate conditions. Five key physiological inputs [quantum yield of electron transport (α), minimum stomatal conductance (g0), stomatal sensitivity to the marginal water cost of carbon gain (g1), maximum rate of electron transport (Jmax), and maximum carboxylation rate of Rubisco (Vcmax)] changed carbon and water flux estimates ≥ 15% in response to climate gradients; variation in α, Jmax, and Vcmax input resulted in up to ~50% and 82% intraspecific and C3 photosynthesis estimate output differences, respectively. Transpiration estimates were affected up to ~46% and 147% by differences in intraspecific and C3 g1 and g0 values—two parameters previously overlooked in modeling carbon and water exchange. We show that a variable environment, within a canopy or along a climate gradient, changes the spatial parameter effects of g0, g1, α, Jmax, and Vcmax in photosynthesis-gs models. Since variation in physiology parameter input effects are dependent on climate, this approach can be used to assess the spatial importance of key physiology model inputs when estimating carbon and water exchange.