## 4 Results and Analysis
### Parameter Sensitivity Analysis and Calibration  
Based on the Biome-BGC model after a phenology module modification (V1), we used the Morris method and the Sobol' method to identify those parameters that have a significant impact on the GPP simulation for further calibration. The results of Morris sensitivity analysis at Haibei Station and Damxung Station are shown in Figure S1. At Haibei Station, three ecophysiological parameters (L\({}_{\text{cel}}\), VPD\({}_{\text{i}}\), and VPD\({}_{\text{f}}\)) had a Morris index (\(\mu\)) below the preset threshold of 0.05, and six ecophysiological parameters (L\({}_{\text{lig}}\), FR\({}_{\text{cel}}\), SLA\({}_{\text{shasun}}\), G\({}_{\text{bl}}\), VPD\({}_{\text{i}}\), and VPD\({}_{\text{f}}\)) at Damxung Station had a Morris index below the preset threshold. These parameters were considered to have negligible effects on the GPP simulation and were excluded from the next [PERSON]' sensitivity analysis.  
The results of Sobol' sensitivity analysis of the remaining parameters at Haibei Station and Damxung Station are provided in Figure S2. As can be seen, at Haibei Station, the simulation of GPP was most sensitive to C:N\({}_{\text{fr}}\), and its large first order and total order sensitivity index values indicate that this parameter affects the GPP simulation either directly or through interaction. The second most influential parameters were C:N\({}_{\text{leaf}}\) and C:N\({}_{\text{lit}}\). Among them, C:N\({}_{\text{leaf}}\) mainly affects GPP through interaction, while C:N\({}_{\text{lit}}\) directly controls the simulation of GPP. Interestingly, similar sensitivity analysis results also appeared in Damxung Station, which suggests that there is no significant difference between the parameters that have an important impact on the GPP simulation of alpine meadows and alpine steppes. To further determine the influential parameters to be calibrated, we set the threshold of the total order sensitivity index to 0.05. Through screening, Haibei Station retained seven ecophysiological parameters and Damxung Station retained eight ecophysiological parameters, as shown in Table 3.  
Based on these selected influential parameters, we used the simulated annealing optimization algorithm combined with EC-derived GPP data to calibrate these parameters to achieve model parameterization. The calibrated results for Haibei Station and Damxung Station are provided in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Influential Parameters 1**} & \multirow{2}{*}{**Default Value**} & \multicolumn{2}{c}{**Calibrated Value**} \\ \cline{3-4}  & & **Haibei Station** & **Damxung Station** \\ \hline FRC:LC & 2.0 & 2.07 & 1.94 \\ C:N\({}_{\text{leaf}}\) & 24.0 & 32.93 & 35.04 \\ C:N\({}_{\text{lit}}\) & 49.0 & 45.12 & 44.23 \\ C:N\({}_{\text{lrf}}\) & 42.0 & 43.66 & 35.42 \\ SLA & 45.0 & 44.39 & 44.61 \\ PLNR & 0.15 & 0.124 & 0.137 \\ G\({}_{\text{smax}}\) & 0.005 & 0.0025 & 0.0059 \\ LWP\({}_{1}\) & \(-\)0.6 & /2 & \(-\)0.84 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The influential ecophysiological parameters and its calibrated values for Haibei Station and Damxung Station.  
Figure 3: Spatial distribution of phenological indicators of alpine grasslands on the TP. (**a**) The start of growing season (SOS); (**b**) The end of growing season (EOS); (**c**) The length of growing season (LOS).