## 2 Materials and Methods
### Data Processing  
#### 2.3.1 Measured Data Processing  
The EC system continuously observes CO\({}_{2}\) flux, and the 30 min data observed via the EC system is processed using the EddyPro 7.1 software to ensure the accuracy of the observations. The original data underwent several processes to ensure accuracy. Initially, any outliers were removed. Subsequently, trend correction [34], the double rotation method [35], frequency response correction [36], sonic temperature correction [37], and Webb-Pearman-Leuning correction [38] were applied. Additionally, a turbulence stationarity test and an overall turbulence characteristic test were conducted to assess the quality of the data. Any data marked with flag 2 were excluded, and data with a frictional wind speed below the nighttime threshold were also eliminated.  
Data imputation is particularly important due to various reasons, such as weather conditions, instrument failure, and quality control, which led to 22% of the CO\({}_{2}\) flux data being missing from April to September of 2018. This issue causes inconvenience in the application of flux tower data. In the current investigation, an online data interpolation tool developed by the Biochemistry Department of the Max Planck Institute ([[https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProCWeb](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProCWeb)]([https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProCWeb](https://www.bgc-jena.mpg.de/bgi/index.php/Services/REddyProCWeb)), (accessed on 15 April 2023)) was used to interpolate the CO\({}_{2}\) flux data. The method employs REddyProc in the R language package for data interpolation, which relies on meteorological data: total radiation, air temperature, and saturated water vapor pressure deficit (VPD) [39]. The data interpolation process consists of the following four steps: (I) Detection and rejection of (NEE) outliers. (II) Estimation of the night-time friction wind speed u\({}^{\star}\) threshold. When the atmospheric turbulent motion is weak, the friction wind speed decreases, and the NEE measured via the eddy covariance system underestimates; therefore, the NEE data below the night-time friction wind speed u\({}^{\star}\) threshold needs to be removed [40; 41]. (III) Interpolation of NEE data by screening out data with time gaps and interpolating NEE using relevant meteorological and flux data [39]. (IV) The night-time data splitting method assumes that Reco is only related to temperature changes and that night-time vegetation only undergoes respiration. Therefore, the night-time NEE response curve to temperature can be used to infer the changes in vegetation _Reco_ during the daytime. Finally, GPP can be calculated using Formula (1). The daytime data splitting method for _NEE_ assumes that the relationship between daytime NEE and total radiation (_Rg_) and vapor pressure deficit (VPD) affects _GPP_, as well as the effect of temperature on Reco.  
\[NEE=Reco-GPP \tag{1}\]  
Due to the lack of instrumentation to observe saturated water vapor pressure, the empirical Tetens Equation (2) was used to indirectly estimate the saturated water vapor pressure from the air temperature using the relationship between the saturated water vapor pressure and the air temperature, as follows [42],  
\[e^{0}(T)=0.6108\exp\left[\frac{17.27T}{T+237.3}\right] \tag{2}\]  
where \(T\) (\({}^{\circ}\)C) is the air temperature and \(e^{0}(T)\) (ka) is the saturated water vapor pressure at temperature T.