## 3 Results  
### 3.1 3-PG Model Calibration and Picea asperata Biomass Simulation  
We performed Morris sensitivity analysis using the r3 pg package and identified the three most sensitive parameters of the 3-PG model. They are the ratio of leaf biomass to stem productivity when DBH is 20 cm (PFS20), the minimum fraction of NPP to roots (PRn), and the canopy quantum efficiency (AlphaCx), respectively. Parameters related to biomass allocation and photosynthesis are always sensitive to model performance. The model parameterization process and results are similar. Therefore, this section uses the Solling site with the most measured data and the longest temporal range as an example to show the 3-PG calibration results. First, we calibrated the model using all the measured values at the Solling site from 1967 to 2014, as introduced in Section 2.1. There is a deviation between the estimated value (3-PG simulation) and the measured value (Observation) (Figure 3a). From 1967 to 1970, the stand was not thinned, and the estimated value of 3-PG was close to the measured value. But after the first thinning, the model did not properly capture the immediate impact of thinning. This led to a persistent underestimation of stand biomass. After the second thinning in 1981, the model continued to use the original parameters. This can lead to overly optimistic estimates of the stand's growth potential, resulting in consistently higher estimates. In order to make the estimated value reflect the true situation of the stand as much as possible, we re-optimized the sensitive parameters of the model every time a thinning event occurred. After correcting the parameters in stages, the model simulation results (3-PG staged simulation) were consistent with the observation data. They can meet the data quality requirements of deep learning. Through model simulation, we obtained a total of 3904 augmented data points to support LSTM training. With the decrease in stand density, the optimal values of sensitive parameters in the 3-PG model also change (Figure 3b). As the age of the forest increases and the density of the stand decreases, there is a corresponding increase in the PRn value. In comparison to the initial value, there is a 5.23% increase; PFS20 decreased by 7.09% compared with the initial value, and AlphaCx changed by 2.6% overall from the initial value. The performance of AlphaCx is subject to real-time thinning and exhibits notable fluctuations. A reduction of 100 trees per hectare in stand density resulted in a fluctuation of 13.67% in the parameter value. It is noteworthy that the parameters exhibit a relatively narrow range of variation with respect to their values. Nonetheless, in the 3-PG model, even minor alterations to the aforementioned three critical parameters can significantly impact the model's trajectory.  
Figure 2: Thinning regimes and time. The horizontal axis represents the year. The vertical axis represents different thinning regimes. 10Y, 15Y, 20Y, 25Y, and 30Y represent the intervals of 10, 15, 20, 25, and 30 years with respect to forest stand thinning, respectively. L, M, H represent light (10%), moderate (20%), and heavy (30%) thinning intensity, respectively, which are distinguished by different colors in the figure; 2T/3T represent simulated thinning two times and three times within the set thinning period.