Estimating carbon fixation of plant organs for afforestation monitoring using a process-based ecosystem model and ecophysiological parameter optimization.

Afforestation projects for mitigating CO emissions require to monitor the carbon fixation and plant growth as key indicators. We proposed a monitoring method for predicting carbon fixation in afforestation projects, combining a process-based ecosystem model and field data and addressed the uncertainty of predicted carbon fixation and ecophysiological characteristics with plant growth. Carbon pools were simulated using the Biome-BGC model tuned by parameter optimization using measured carbon density of biomass pools on an 11-year-old plantation on Loess Plateau, China. The allocation parameters fine root carbon to leaf carbon (FRC:LC) and stem carbon to leaf carbon (SC:LC), along with specific leaf area (SLA) and maximum stomatal conductance ( ) strongly affected aboveground woody (AC) and leaf carbon (LC) density in sensitivity analysis and were selected as adjusting parameters. We assessed the uncertainty of carbon fixation and plant growth predictions by modeling three growth phases with corresponding parameters: (i) before afforestation using default parameters, (ii) early monitoring using parameters optimized with data from years 1 to 5, and (iii) updated monitoring at year 11 using parameters optimized with 11-year data. The predicted carbon fixation and optimized parameters differed in the three phases. Overall, 30-year average carbon fixation rate in plantation (AC, LC, belowground woody parts and soil pools) was ranged 0.14-0.35 kg-C m y in simulations using parameters of phases (i)-(iii). Updating parameters by periodic field surveys reduced the uncertainty and revealed changes in ecophysiological characteristics with plant growth. This monitoring method should support management of afforestation projects by carbon fixation estimation adapting to observation gap, noncommon species and variable growing conditions such as climate change, land use change.