## 4 Results and Analysis
### Site-Level Evaluation  
The effects of phenology on GPP simulation for the calibration year and the validation year are shown in Figure 4 and Figure S3, respectively. Among them, the calibration year represents the first year of the acquired EC-derived GPP data, which is used for model calibration, and the validation year represents the second year of the acquired EC-derived GPP data, which is used for model validation. As can be seen from Figure 4 and Figure S3, V0 severely underestimated the GPP values at both Haibei Station and Damxung Station as compared to the EC-derived GPP (marked with red dots). However, the GPP simulated by V1 effectively overcame this limitation, which indicated that remotely sensed phenology can more realistically reflect the phenological characteristics of alpine grasslands on the TP. The reason for the substantial underestimation of GPP in V0 is mainly attributable to the severe lag in the simulation of SOS, which directly leads to a shortened LOS and further affects the carbon fixation process of vegetation. Nevertheless, it should be noted that there was still a large difference between the GPP simulated by V1 and the GPP derived from EC measurements, so further calibration of V1 is still necessary.  
A comparison of GPP simulations before and after calibration for the calibration year and the validation year were shown in Figure 4 and Figure S3, respectively. As depicted in Figure 4, V2  
Figure 4: Comparisons between GPP derived from EC measurements and GPP simulated by the version of V0, V1, and V2 for the calibration year. (**a**) Haibei Station (alpine meadow); (**b**) Damxung Station (alpine steppe).  
significantly improved the simulation accuracy of GPP and showed the best performance in all versions of the Biome-BGC model. Compared with the initial version V0, the RMSE of the final version V2 was reduced by 1.49 gC/m\({}^{2}\)/day at Haibei Station and 0.27 gC/m\({}^{2}\)/day at Damxung Station, and its coefficient of determination \(R^{2}\) (\(P<0.01\)) increased by 0.08 and 0.03 at Haibei Station and Damxung Station, respectively. However, it should be mentioned that the simulation accuracy of V2 is slightly lower than that of V0 at Damxung Station for the validation year (Figure S3), but it is still much higher than the simulation accuracy of V1. The reason may be attributable primarily to the relatively low carbon fixation rate at Damxung Station, while the GPP simulated by V0 is also underestimated due to the severe lag of the simulated SOS, thus making the GPP simulated by V0 close to the EC-derived GPP.  
On the other hand, we also used EC-measured NEE and total ecosystem respiration (TER) to validate the performance of V2. As can be seen from Figure 5 and Figure S4, compared with V0 and V1, V2 significantly improved the simulation accuracy of NEE during the growing season at both Haibei Station and Damxung Station. However, it should be noted that there were still some discrepancies between the NEE simulated by V2 and the NEE measured by EC technique during the non-growing season, which may be attributed to the limitations in the soil respiration module of the Biome-BGC. In addition, as shown in Figure 6 and Figure S5, V2 also greatly improved the simulation accuracy of TER, which generated a good agreement with the observed TER. Overall, the Biome-BGC model, after a phenology module modification and parameter calibration, effectively improved the simulation accuracy of carbon fluxes and had a good consistency with the observations, which can be used for GPP simulation of alpine grasslands on the TP.  
Figure 5: Comparisons between NEE measured by EC technique and NEE simulated by the version of V0, V1, and V2 for the calibration year. (**a**) Haibei Station (alpine meadow); (**b**) Damxung Station (alpine steppe).  
Figure 6: Comparisons between total ecosystem respiration (TER) derived from EC measurements and TER simulated by the version of V0, V1, and V2 for the calibration year. (**a**) Haibei Station (alpine meadow); (**b**) Damxung Station (alpine steppe).