## 4 Discussion
### Applicability of the Biome-BGC Model to the Rubber Plantation in the Study Area  
The Biome-BGC model is suitable for carbon flux simulation of the rubber plantation in the study area. In all experimental years, the model showed a good fit and could also accurately describe the carbon flux differences in different growth periods within a year. Unlike deciduous broad-leaved forests in temperate regions, the rubber plantations in the study area have special phenological characteristics. The general deciduous broad-leaved forests begin to grow new leaves in spring and lose them in autumn, while the rubber plantations generally drop leaves from February to March every year, and new leaves grow almost simultaneously as the previous ones drop [35]. Therefore, there is an obvious carbon flux change scenario in February and March every year. The phenological setting of Biome-BGC was determined by two parameters: the time to start growing new leaves, and the time to complete defoliation. It was found that this setting can accurately characterize carbon flux changes in the deciduous broad-leaved forests in temperate regions by adjusting parameters. However, for rubber plantations, using such a parameter setting for simulation will cause the carbon pool of the model to enter the deciduous stage without accumulating enough organic matter, leading to the model being unable to be initialized normally. Similarly, treating rubber plantations as evergreen forests makes the model unable to capture differences in carbon flux across phenological periods. In response to the above problems, our approach was to adjust the start time of the simulation; that is, by modifying the meteorological data, the time when the vegetation began to grow new leaves was used as the start time of the simulation process so that the time of the growing season was long enough for the model to initialize normally (Figure 6). This adjustment is applicable to forests whose phenological cycle cannot be described by the phenological module of Biome-BGC, so the above research helps with the application of the model in other biomes.  
Parameter optimization based on observational data can effectively improve the accuracy of the model. Although the phenologically adjusted model can accurately simulate  
Figure 6: Phenology of rubber plantations and temperate deciduous broad-leaved forests, and the adjustments to simulation periods.  
the trend of carbon flux, it is still inaccurate in describing the magnitude of carbon flux in each period, and the uncertainty of the simulation results is high (Figure 3a). The principle of the PEST method is to traverse multiple parameter groups until the parameter group with the smallest residual between the simulated and actual value is found [28], which is advantageous for parameter groups with less overall variability, so that the optimized simulation results are more stable in time. Uncertainty in model simulations arises from many aspects, including the model structure and the driving data, with model parameters being an important source of uncertainty [36]. The optimization method used in this study was based on accurate observation data. Parameter optimization targeting observation data can more effectively optimize model parameters, thereby eliminating parameter uncertainty and making the simulation results closer to reality. Furthermore, the observation data used for parameter optimization can include not just the target variable of the simulation, but also other outputs or intermediate variables, such as leaf area index [19]. The above results validate the applicability of this method in the study area and help with the application and promotion of the method in other scenarios.