Simulating of carbon fluxes in bamboo forest ecosystem using BEPS model based on the LAI assimilated with Dual Ensemble Kalman Filter

LAI is one of the most important observation data in the research of carbon cycle of forest ecosystem, and it is also an important parameter to drive process-based ecosystem model. The Moso bamboo forest (MBF) and Lei bamboo forest (LBF) were selected as the study targets. Firstly, the MODIS LAI time series data during 2014-2015 was assimilated with Dual Ensemble Kalman Filter method. Secondly, the high quality assimilated MBF LAI and LBF LAI were used as input dataset to drive BEPS model for simulating the gross primary productivity (GPP), net ecosystem exchange (NEE) and total ecosystem respiration (TER) of the two types of bamboo forest ecosystem, respectively. The modeled carbon fluxes were evaluated by the observed carbon fluxes data, and the effects of different quality LAI inputs on carbon cycle simulation were also studied. The LAI assimilated using Dual Ensemble Kalman Filter of MBF and LBF were significantly correlated with the observed LAI, with high R2 of 0.81 and 0.91 respectively, and lower RMSE and absolute bias, which represented the great improvement of the accuracy of MODIS LAI products. With the driving of assimilated LAI, the modeled GPP, NEE, and TER were also highly correlated with the flux observation data, with the R2 of 0.66, 0.47, and 0.64 for MBF, respectively, and 0.66, 0.45, and 0.73 for LBF, respectively. The accuracy of carbon fluxes modeled with assimilated LAI was higher than that acquired by the locally adjusted cubic-spline capping method, in which, the accuracy of mo-deled NEE for MBF and LBF increased by 11.2% and 11.8% at the most degrees, respectively.