## 3 Results  
### Sensitivity Analysis of Ecophysiological Parameters  
In this study, we conducted a sensitivity analysis of the ecophysiological parameters of the Biome-BGC model across 10 sites using the EFAST method. We identified the parameters with the greatest impact on the simulation results of GPP and NEP by evaluating the sensitivity indices, including the first-order sensitivity index (S\({}_{i}\)) and the total sensitivity index (S\({}_{iT}\)). The sensitivity analysis results for the 10 sites are presented in Figure 3, and the complete sensitivity ranking of all parameters is provided in Supplementary Materials S3.  
Figure 2: The technical route of this study.  
\(S_{1}\) (first-order sensitivity index) represents the direct contribution of a parameter to the model's simulation results, while \(S_{\rm{T}}\) (total sensitivity index) reflects the overall contribution of a parameter, including its interactions with other parameters [54]. The sensitivity analysis of the Biome-BGC model for GPP simulation reveals that the \(S_{1}\) and \(S_{\rm{T}}\) results were largely consistent. This suggests that the ecophysiological parameters of the Biome-BGC model primarily influence the simulation results directly, with minimal interaction among them. Additionally, in the GPP simulations, the parameters with the highest sensitivity index values included FLNR (fraction of leaf N in Rubisco), C:N\({}_{\rm{leaf}}\) (C:N of leaves), k (canopy light extinction coefficient), \(G_{\rm{smax}}\) (maximum stomatal conductance), and C:N\({}_{\rm{fr}}\) (C:N of fine roots). This indicates that these parameters are frequently the most sensitive, significantly impacting the Biome-BGC simulation results. Notably, FLNR exhibited the highest sensitivity index value during GPP simulation at the HBSL site.  
The results of the parameter sensitivity analysis of the Biome-BGC model in simulating NEP show that the parameters with larger values of \(S_{1}\) were FLNR, k, and C:N\({}_{\rm{leaf}}\). The parameters with larger values of \(S_{\rm{T}}\) were FLNR, C:N\({}_{\rm{fr}}\), \({\rm{LWP}}_{\rm{f}}\) (leaf water potential), and \({\rm{DW}}_{\rm{cell}}\) (dead wood cellulose proportion). Additionally, the \(S_{\rm{T}}\) values for nearly all parameters are greater than their \(S_{1}\) values, and the parameters with high \(S_{\rm{T}}\) values differ from those with high \(S_{\rm{t}}\) values. This suggests that during NEP simulation, the ecophysiological parameters in the Biome-BGC model interact extensively with one another, collectively influencing the simulation results. The reason for this phenomenon is that the ecophysiological processes carried out by the Biome-BGC model when simulating NEP are more complex than when simulating GPP, and more mathematical formulas are applied to the model's calculations at the same time that more ecophysiological parameters interact with each other. For example, simulating NEP requires additional calculations of plant autotrophic respiration and soil respiration compared to simulating GPP.  
Overall, factors such as spatial distribution, plant functional types, and the model output metrics (GPP or NEP) contribute to changes in the sensitivity of the ecophysiological  
Figure 3: The first-order sensitivity index (\(S_{1}\)) and total sensitivity index (\(S_{\rm{T}}\)) for sensitive parameters of GPP and NEP at the observation sites.  
parameters within the Biome-BGC model. These changes are irregular and complex, making them difficult to predict without conducting parameter sensitivity analyses.