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Worldwide expansion of agriculture is impacting the earth's climate byaltering carbon, water, and energy fluxes, but the climate in turn isimpacting crop production. To study this two-way interaction and its impacton seasonal dynamics of carbon, water, and energy fluxes, we implementeddynamic crop growth processes into a land surface model, the IntegratedScience Assessment Model (ISAM). In particular, we implemented crop-specificphenology schemes and dynamic carbon allocation schemes. These schemesaccount for light, water, and nutrient stresses while allocating theassimilated carbon to leaf, root, stem, and grain pools. The dynamicvegetation structure simulation better captured the seasonal variability inleaf area index (LAI), canopy height, and root depth. We further implementeddynamic root distribution processes in soil layers, which better simulatedthe root response of soil water uptake and transpiration. Observational datafor LAI, above- and belowground biomass, and carbon, water, and energy fluxeswere compiled from two AmeriFlux sites, Mead, NE, and Bondville, IL, USA, tocalibrate and evaluate the model performance. For the purposes of calibrationand evaluation, we use a corn–soybean (C4–C3) rotation system over theperiod 2001–2004. The calibrated model was able to capture the diurnal andseasonal patterns of carbon assimilation and water and energy fluxes for thecorn–soybean rotation system at these two sites. Specifically, thecalculated gross primary production (GPP), net radiation fluxes at the top ofthe canopy, and latent heat fluxes compared well with observations. Thelargest bias in model results was in sensible heat flux (SH) for corn andsoybean at both sites. The dynamic crop growth simulation better captured theseasonal variability in carbon and energy fluxes relative to the staticsimulation implemented in the original version of ISAM. Especially, withdynamic carbon allocation and root distribution processes, the model'ssimulated GPP and latent heat flux (LH) were in much better agreement withobservational data than for the static root distribution simulation. Modeledlatent heat based on dynamic growth processes increased by 12–27% duringthe growing season at both sites, leading to an improvement in modeled GPP by13–61% compared to the estimates based on the original version of theISAM

biogeosciences

Implementation of dynamic crop growth processes into a land surface model: evaluation of energy, water and carbon fluxes under corn and soybean rotation