Optimization Research Of Forest Carbon Cycle Model Parameters

Terrestrial ecosystem model has been widely used to simulate the interaction between the carbon cycle and ecosystems. Some physiological and biochemical parameters of the model often change seasonally. In this study, based on CO2 flux observation data (gross primary productivity (GPP) and sensible heat flux (LE)) from Maoershan Ecosystem Station flux tower in 2011, using sequential data assimilation with ensemble kalman filter technique to optimize some of the key parameters of the Boreal Ecosystem Productivity Simulator (BEPS) model, taking into account the errors in inputs, parameters, and observations. Optimizing parameters by data assimilation include maximum photosynthetic carboxylation rate (Vcmax), and the slope of stomatal conductance and net photosynthetic rate (M). Parameters were optimized in daily steps. The results shows that the parameters varied significantly at seasonal scales, usually rapid increase in leaf expansion period, in summer reach a steady and decline in senescence of leaves. According to optimized parameters, model simulated value of GPP and RE flux are significantly increased. GPP and RE simulated accuracy can reach 91% and 96% measured value than precision simulated values before optimization increased about 8% and 11%, respectively. This shows EnKF parameter optimization can significantly improve capacity of model to simulate the carbon and water fluxes.