# Characteristics of Carbon Fluxes and Their Environmental Control in Chenhu Wetland, China
## 3 Results and Discussion
### Relationships Between NEE and Some Environmental Parameters
This analytical method represents an advanced form of multiple regression analysis, enabling the examination of the relationships among different environmental variables and their direct and indirect effects on carbon flux. Based on the aforementioned analytical findings, the path analysis was employed to investigate both the direct and indirect influences of key environmental parameters on NEE within the Chenhu wetland during the growing season (Figure 6). The model exhibited a satisfactory fit, as evidenced by a normed fit index (NFI) and comparative fit index (CFI) exceeding 0.9, along with a p-value greater than 0.05, thereby confirming the model's robustness. The path coefficients for VPD and PAR were recorded at 1.03 and -0.89, respectively, while RH and \(\text{T}_{\text{a}}\) displayed coefficients of 0.66 and -0.33, respectively. In general, the diurnal CO\({}_{2}\) flux within the wetland was predominantly influenced by VPD and PAR during the growing season, a conclusion that is consistent with the research conducted by [PERSON] et al. [2] in the Qinghai Lake alpine wetland. Throughout the diurnal cycle, fluctuations in NEE were primarily influenced by leaf assimilation, with PAR serving as the principal determinant of plant photosynthesis [48]. During the growing season of 2022, the capacity for CO\({}_{2}\) absorption in the Chenhu wetland during daylight hours exhibited a positive correlation with increasing solar radiation. The relationship between NEE and PAR during daytime adhered to the Michaelis-Menten light response model, which can be mathematically represented by a rectangular hyperbolic \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Month** & \(\mathbf{T_{a}}\) & \(\mathbf{T_{s}}\) & **RH** & **VPD** & **SWC** & **P** \\ \hline
1 & \(-0.222\) & \(0.285\) & \(0.229\) & \(-0.235\) & \(-0.148\) & \(0.317\) \\
2 & \(0.127\) & \(-0.170\) & \(-0.183\) & \(0.177\) & \(0.191\) & \(0.066\) \\
3 & \(-0.143\) & \(0.838\)** & \(0.150\) & \(-0.207\) & \(0.570\)** & \(0.081\) \\
4 & \(-0.352\) & \(-0.389\) & \(0.322\) & \(-0.324\) & \(-0.339\) & \(-0.131\) \\
5 & \(-0.773\)** & \(-0.650\)** & \(0.743\)** & \(-0.756\)** & \(-0.020\) & \(-0.201\) \\
6 & \(-0.413\) & \(-0.414\) & \(0.504\)* & \(-0.511\)* & \(0.156\) & \(0.094\) \\
7 & \(-0.715\)** & \(-0.466\)** & \(0.702\)** & \(-0.711\)** & \(0.526\)* & \(0.389\) \\
8 & \(-0.611\)** & \(-0.493\)** & \(0.614\)** & \(-0.625\)** & \(0.625\)** & \(-0.520\)** \\
9 & \(-0.001\) & \(-0.054\) & \(0.026\) & \(-0.036\) & \(-0.049\) & - \\
10 & \(-0.594\)** & \(0.175\) & \(0.601\)** & \(-0.590\)** & \(0.241\) & \(0.336\) \\
11 & \(-0.548\)** & \(0.110\) & \(0.501\)** & \(-0.543\)** & \(0.256\) & \(0.059\) \\
12 & \(-0.172\) & \(-0.362\) & \(0.168\) & \(-0.178\) & \(-0.004\) & \(0.016\) \\ \hline \hline \end{tabular} Note. * and ** are significantly correlated at 0.05 (bilateral) and 0.01 (bilateral) levels, respectively.  
\end{table} 
Table 3: Pearson correlation coefficients of the monthly mean daily variation of NEE and environmental parameters at night. Figure 6: Path analysis between main environmental factors and NEE. \(\chi^{2}=0.5\), \(p=0.46>0.05\), Degrees of freedom = 1. NFI = 0.999, CFI = 1.000, root mean square error of approximation (RMSEA) = 0.  
equation [27]. This non-linear relationship demonstrates seasonal variations in accordance with vegetation growth. As illustrated in Figure 7 and Table 4, below the light compensation point, NEE values were positive during daylight hours, indicating that the respiratory intensity of the ecosystem exceeded that of photosynthetic production. As PAR increased, both photosynthesis and net carbon absorption also rose. The carbon absorption capacity of the wetland ecosystem reached a saturation point when PAR levels attained light saturation.