## 2 Materials and Methods
### Data Processing  
#### 2.5.1 Flux Data Processing  
To reduce the uncertainty caused by the observations, quality control and processing were performed on the data output from the EasyFlux software. Screening and quality control excluded approximately 25% of the data. The exclusion criteria included (1) when the absolute value of the CO\({}_{2}\) flux was greater than 30 g CO\({}_{2}\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) (which is not biologically consistent); (2) when the endflow was weak, especially when the nighttime frictional wind speed (U\({}_{\rm star}\)) was less than 0.15 m/s (below which the nighttime CO\({}_{2}\) flux data would show a significant discrete distribution); and (3) when the PAR was <5 umol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\) and the FCO\({}_{2}\) was <0 [12]. In this study, the following methods were used to interpolate the missing CO\({}_{2}\) data:  
For missing data shorter than 2 h, linear interpolation was used; for missing data lasting from 2 h to 7 days, the average daily variation was used, i.e., the average of the corresponding data points from the neighboring days was used for interpolation; and for missing data lasting for more than 7 days, a nonlinear regression was used for interpolation by building a correlation model.  
Daytime missing NEE data were obtained by interpolating using the Michaelis-Menten equation [13]:  
\[\mathrm{NEE}=\frac{\alpha\times PAR\times P_{\mathrm{max}}}{\alpha\times PAR+P _{\mathrm{max}}}-R_{eco,d}, \tag{1}\]  
where \(\alpha\) (mg CO\({}_{2}\) umol photons\({}^{-1}\)) is the apparent light quantum efficiency of the ecosystem, \(P_{\mathrm{max}}\) is the potential maximum CO\({}_{2}\) assimilation rate, \(R_{eco,d}\) (mg CO\({}_{2}\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\)) is the daytime ecosystem respiration rate, and \(PAR\) is given in umol\(\cdot\)m\({}^{-2}\cdot\)s\({}^{-1}\).  
Due to insufficient nighttime turbulence, NEE data were presented as u\({}^{*}\) (friction wind speed) < 0.15 m/s. Nighttime missing data were obtained via respiration modeling [14]:  
\[R_{eco,n}=\mathrm{a}\times\mathrm{exp}(\mathrm{b}\times\mathrm{T}_{\mathrm{s}}) \tag{2}\]  
where a and b are constants, \(\mathrm{T}_{\mathrm{s}}\) is the temperature at a soil depth of 5 cm, and \(R_{eco,n}\) (mg CO\({}_{2}\) m\({}^{-2}\cdot\)s\({}^{-1}\)) is the nighttime ecosystem respiration rate.  
Directly observed CO\({}_{2}\) fluxes from flux towers represent the NEE. Positive values represent net CO\({}_{2}\) emissions from the system, and negative values represent net CO\({}_{2}\) uptake by the system. This produces the difference between ecosystem respiration (\(\mathrm{R}_{\mathrm{e}}\)) and gross primary productivity (GPP). Therefore, GPP can be defined as [13]  
\[\mathrm{GPP}=\mathrm{NEE}-\mathrm{R}_{\mathrm{e}}. \tag{3}\]Daily ecosystem respiration is the sum of daytime ecosystem respiration (\(\mathrm{R_{e,day}}\)) and nighttime ecosystem respiration (\(\mathrm{R_{e,night}}\)) [14]:  
\[\mathrm{Re}=\mathrm{R_{e,day}}+\mathrm{R_{e,night}}. \tag{4}\]  
Nighttime ecosystem respiration was obtained from the observed nighttime net \(\mathrm{CO_{2}}\) flux. Equation (2) was used to extrapolate daytime ecosystem respiration.  
The interpolated data were divided into daytime (total radiation \(\geq\) 1 W\(\cdot\)m) and nighttime (total radiation \(<\) 1 W\(\cdot\)m). Data from clear and cloudless days each month were selected as representative data. The daily NEE dynamics and other variables throughout the growing season were analyzed. Additionally, the daytime and nighttime fluxes were integrated and summed at 30 min intervals to represent the cumulative \(\mathrm{CO_{2}}\) uptake and release of the ecosystem during the daytime and nighttime.  
After undergoing data quality control and selection, the observed \(\mathrm{CO_{2}}\) flux results from the Momoge wetlands can accurately represent the actual process of \(\mathrm{CO_{2}}\) exchange between the wetland and the atmosphere.