## 4 Discussion  
The simulated value of the carbon fluxes of bamboo forests had a good correlation with the observed value of the flux observation station (Figure 4), and the R\({}^{2}\) was 0.58. Other than that, in order to further prove the reliability of this study we compared the simulated NPP with a related study (see Table 5). It can be seen from Table 5 that our simulated mean value of NPP was slightly lower than that of related studies. Due to the fact that structures, mechanisms, and input parameters varied for different models, there are variances in the simulation results of different models. Additionally, there may be differences due to different study areas and periods. Of course, this study also has some shortcomings, the following aspects of which can be analyzed. Firstly, the simulated results of the BEPS model largely depend on the quality of the input data; deficiencies in the input data will affect the accuracy of the simulation results. The resolution of the data in this study is low, so there may be limitations in simulating the carbon flux of bamboo forests in China. Secondly, in this study, the bamboo forest abundance data were used to drive the BEPS model, which solved the influence of mixed pixels on the carbon flux simulation to some extent. However, the phenomenon of \"different objects with same spectrums\" in remote sensing images will affect the result of bamboo forest extraction. Thirdly, we only used the observed data of two carbon flux observation stations to verify the simulated results of bamboo forest carbon fluxes in China. Therefore, there are limitations on the spatial scale. Finally, the carbon fluxes of bamboo forests were not only affected by climate factors but also by human activities and geographic factors (such as slope, aspect, and elevation). This study only considered the impact of climate factors, so there may still be a certain gap between the simulated results and the real situation. Due to the rapid growth of bamboo forests and their high ecological, economic, and social value, some areas promoted the reclamation of wasteland and the plantation of bamboo forests [79], which increased the total area of bamboo forests in China. Therefore, the GPP and NPP of bamboo forests also increased. As shown in Figure 5, certain fluctuations occurred in the annual average GPP and NPP values of bamboo forests, which might be related to climate change. For example, in 2003 there was less precipitation and large-scale drought occurred in the summer (Figure 10), which was not conducive to the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Climate** & **Correlation** & **Direct Path** & \multicolumn{2}{c}{**Indirect Path Coefficient**} & **Partial Correlation** \\  & **Factors** & **Coefficient** & **Coefficient** & **Temperature** & **Precipitation** & **Coefficient** \\ \hline \multirow{2}{*}{GPP} & Temperature & 0.649 ** & 0.387 ** & - & 0.26 & 0.399 \\ \cline{2-7}  & Precipitation & 0.659 ** & 0.415 ** & 0.24 & - & 0.423 \\ \hline \multirow{2}{*}{NPP} & Temperature & 0.562 ** & 0.301 ** & - & 0.15 & 0.293 \\ \cline{2-7}  & Precipitation & 0.602 ** & 0.412 ** & 0.11 & - & 0.386 \\ \hline \hline \end{tabular} Note: **, \(p<0.01\).  
\end{table} 
Table 4: Path analysis of temperature and precipitation to GPP and NPP. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Site** & **Model** & **Mean NPP (g C m\({}^{-2}\) y\({}^{-1}\))** & **Reference** \\ \hline China & BEPS & 716.88 & This study \\ Tianmu Mountain, Zhejiang & CASA & 740 & [77] \\ Anji, Zhejiang & Triplex-Flux & 835.58 & [45] \\ Fujian & BEPS & 788.6 & [78] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of the simulated NPP results in this study with the simulated results of other studies. growth of bamboo forests, leading to low GPP and NPP values in that year. In 2009 and 2010 the values of GPP and NPP were low, which may be related to the natural large-scale low-temperature, snow, and ice disaster in South China in 2008 [80].