# Simulation of Forest Carbon Fluxes Using Model Incorporation and Data Assimilation
); [EMAIL_ADDRESS] (Z-H.); [EMAIL_ADDRESS] (H.S.) Zongtao Han  
1 Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; [EMAIL_ADDRESS] (M.Y. ); [EMAIL_ADDRESS] (Z-H.); [EMAIL_ADDRESS] (E.C. ); [EMAIL_ADDRESS] (Z-H.); [EMAIL_ADDRESS] (H.S.) Zongtao Han  
1 Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; [EMAIL_ADDRESS] (M.Y. ); [EMAIL_ADDRESS] (Z.L. ); [EMAIL_ADDRESS] (E.C. ); [PERSON]AIL_ADDRESS]om (Z-H.); [EMAIL_ADDRESS] (H.S.) [PERSON]  
1 Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China; [EMAIL_ADDRESS] (M.Y. ); [EMAIL_ADDRESS] (Z.L. fluxes between ecosystem and atmosphere directly [4]. This method has been widely used for the observation of CO\({}_{2}\) exchange within the FLUXNET [5; 6]. The EC measurements are temporally continuous (e.g., half-hourly), and the technique is only considered reliable for a limited area of a few square kilometers [7]. An alternative approach was developed for large-scale simulating and forecasting carbon fluxes through models. Simulation of carbon dynamics still experiences major uncertainty, because of the large variation between different models [8] and their often uncertain parameterization [9]. A remote sensing-based model (i.e., MODIS MOD_17 GPP (MOD_17)) is exploited to acquire spatially distributed simulation, but relies on the applicability and explicability of remote sensing data, and cannot depict the intrinsic characterizations of forest processes (e.g., evapotranspiration, photosynthesis, respiration, allocation). The process-based model Biome-BGC can explain the detailed forest processes or the response of these processes to climate variation temporally on a continuous basis. However, the simulations of the model rely on abundant ground information, input data, ecophysiological parameters, and the accurate calibration of the parameters. Some of the parameters may be hard to acquire in the field and the number of samples provided may be limited [10]. Therefore, the incorporation of MOD_17 with the Biome-BGC model was chosen in this study in order to calibrate the Biome-BGC model and satisfy the requirement of spatial and temporal continuity of the carbon fluxes. Errors and uncertainties, which form an inherent problem in models, are mainly caused by input variables, model structure, and model parameters [11; 12]. Data assimilation provides an effective way to integrate information from observations and a model system, taking into account errors both in the observations and the models to realize a best estimate of the true state of the model system [9; 13]. Sequential data assimilation is designed to update the model system in a sequential manner by separately weighting the observational and modeling errors. The Ensemble Kalman filter (EnKF), an extended Kalman filter, has been a popular data fusion algorithm and its formulation is mainly been contributed to by [PERSON] [14; 15]. This method is Monte Carlo-based and uses recursive data processing. It tracks the model error statistics using an ensemble of model state variables. Both field measurements and remote sensing observations have been successfully assimilated into a process-based model, for either updating the relevant variables in the model, or adjusting the initialization and parameterization of the model [16; 17]. [PERSON] et al. [18] employed a strategy of assimilating observed soil moisture into the LPJ-DGVM model with an EnKF, and thus improved the performance of carbon and water flux predictions. [PERSON] et al. [19] optimized the parameters of the BEPS model through assimilating the flux data into the model using the EnKF, and the results indicated that this strategy could faithfully retrieve the seasonal the inter-annual variations in parameters. When remote sensing data are used to update the state of simulated variables, the data error is assumed to be acceptable for being propagated within the simulated system. [PERSON] et al.