## 4 Discussion  
This study showed that the incorporation strategy of the MOD_17 model and the Biome-BGCMuSo model can effectively reduce the uncertainty during the simulation of carbon fluxes in different forest types at the Greater Khingan Mountains, Inner Mongolia, in the cold temperate zone. By calibrating the MOD_17 model with the optimized Biome-BGCMuSo model, the correlation coefficient between the simulated forest NPP and the measured forest NPP was 0.64. Although the model was not validated with observational data such as flux sites in the study area, the simulation results were validated with two sample plot data points and tree dendrochronological measurements. Compared with the observation data such as flux sites, the sample plot data and dendrochronological measurement data had the characteristics of various disturbance scenarios and multihabitat conditions, strong spatial continuity, long-term scales, and many samples; the phenomenon revealed the effectiveness of the simulation through the incorporation model strategy and confirmed the reliability and global representation of forest carbon fluxes simulation. The MOD_17 model has strong practicability and has been widely used at regional, national and even global scales, but the parameters of the Biome Properties Look-Up Table have significant uncertainties, which depend on the quality of remote sensing data, model parameterization schemes and empirical knowledge [44; 45]. Therefore, we optimized each parameter. Among them, \(\varepsilon_{max}\) had not been recalibrated but the \(\varepsilon_{max}\) consistent with the cold temperate forest type had been collected through the literature data. The \(f(T_{\text{min}}),f(VPD)\), and PAR were calculated from CMFD data with a time-resolve of 3 h [31], providing a basis for obtaining carbon fluxes with temporal and spatial continuity. The \(f\)PAR was derived from the GLASS LAI/\(f\)PAR product, which had been proven to be reliable [35]. Biome-BGCMuSo is a newly developed process model of Biome-BGC that is widely used to simulate forest NPP, but the sensitivity analysis of many parameters and model calibration are the key steps in forest carbon flux simulation. The results of our sensitivity analysis of the Biome-BGCMuSo model showed that there were five sensitive parameters that had important effects on different forest types. However, many parameters in the Biome-BGCMuSo model were extremely insensitive to GPP, such as TGP (transfer growth period as a fraction of growing season), LGS (litterfall as a fraction of growing season), DMC\({}_{\text{leaf}}\) (dry matter carbon content of leaves), DMC\({}_{\text{f}}\) (dry matter carbon content of fine roots), etc. The parameters that controlled the carboxylation rate of photosynthesis, such as C:Nleaf, SLA1 and FLNR, had the greatest impact on the GPP of different forest types, and the results were consistent with [PERSON] et al. [43] and [PERSON] et al. [46]. These three parameters determined the size of the maximum carboxylation rate, and the Rubisco enzyme directly affected the carboxylation of vegetation to fix atmospheric CO\({}_{2}\). If the nitrogen content of Rubisco enzyme increased its activity would be promoted, thereby enhancing photosynthesis and promoting organic substance accumulation [47; 48]. A strategy based on the incorporation of parametric models and ecological process models to simulate carbon fluxes with temporal and spatial continuity had been proven to be reliable and effective for Mediterranean forests and single dominant tree species in cold temperate zones [23; 24; 25; 26], but feasibility experiments had not been carried out on different forest types in the cold temperate zone. Due to the heterogeneity of the growth environment, different forest types and the same forest type under different habitats had different physiological and ecological adaptation mechanisms, so it was difficult to calibrate the parameters of the Biome-BGCMuSo model on a regional basis, and it would result in the model distortion simulation [23].