The study site is located at the southeastern edge of the Tengger Desert in the Shapotou region (37°27′ N, 105°00′E, 1288 m AMSL), northwest China (Figure S1). The dominant species of the natural vegetation are Agriophyllum squarrosum and Hedysarum scoparium and with a coverage rate of approximately 1% (Li et al., 2004a). The soil is wind-borne sand soil (FAO-ISRIC-ISSS, 1998) (Li et al., 2022). The water content of the sand layer is 2–3% (Chen, Li, & Di, 1998). The groundwater level is more than 60 m, which is not available for vegetation. Precipitation is the only source of soil moisture (Li et al., 2004b). The average wind speed is 2.8 m s-1 with a predominant direction (Northwestern), and annual dust-storm days are 59 d (Liu et al., 2006).
The mean annual temperature is 9.6 °C, with a minimum of -25.1 °C in January and a maximum of 38.1 °C in July (Li et al., 2014). The mean annual precipitation was 186 mm, approximately 80% of which occurred between May and September based on the Shapotou Weather Station from 1955 to 2016 (Li et al., 2018). The variation in annual precipitation ranges from 52.9 mm in 2005 (Zhang et al., 2021) to 283.4 mm in 2002 (Li et al., 2014). The annual potential evaporation is approximately 2800 mm (Li, 2012).
The Tengger Desert in the Shapotou area is surrounded by moving sand dunes (Figure S1). A sand-binding vegetation protective system by planting xerophytic shrubs was established in the 1950s to guarantee the running of the Baotou-Lanzhou railway through sand dunes (Li, 2005). Specific information on the vegetation restoration is available in Li et al. (2004a). The same method was adopted to expand the protection system in 1964, 1973, 1982, 1987, and 1989. The length of the vegetation protection zone is 16 km, which is 500 m and 200 m wide on the north and south sides of the railway, respectively (Figure S1). Within the last 50 years of development, the mobile dune-dominated desert landscape has transformed into a complex artificial-natural desert ecosystem (Li, 2005) (Figure S1).
The eddy flux was located in a revegetated area, which was planted in 1989. The dominant xerophytic shrubs included A. ordosica (average height of 0.6 m) and C. korshinskii (average height of 1.2 m), with a coverage of approximately 25%. It is worth highlighting that the vegetation coverage has reached a relatively stable status since the start of the measurements (2009). The enhanced vegetation index (EVI) (see supplementary material for data sources) during the growing season showed an increasing trend from 2000 to 2008 but also reached a steady state from 2009 (Figure 1). This reveals that the restored vegetation needs about 20 years (1989–2009) to reach a stable status. This is consistent with previous studies showing that the coverage of planted shrubs significantly increased for approximately 10–20 years of vegetation establishment (Li et al., 2014).
2.2 Eddy covariance and micrometeorological measurements
An open-path eddy covariance (EC) system was installed in a revegetated area in 2008. We measured the three-dimensional wind vector (CSAT3, Campbell Scientific, Logan, UT, USA) and the concentrations of CO2 and H2O (LI-7500, LiCor, Lincoln, NE, USA) at a height of 4 m above the soil surface. The calibration gases and a dew point generator were used to calibrate the open-path infrared gas analyser in early April of each year. The 10 Hz raw data (concentrations of CO2 and H2O, three-dimensional wind vector, and sonic virtual temperature) were collected with a data logger (CR3000, Campbell Scientific, Logan). The energy closure ratio of the EC system was above 79% (Gao et al., 2016), which indicated flux measurements reached a moderate level of robustness (Wilson et al., 2002).
Air temperature, relative humidity (HMP45C, Vaisala Inc., Helsinki, Finland), and net radiation (CNR-1, Kipp&Zonen, the Netherlands) were measured at 4, 4, and 3 m above the soil surface, respectively. Two self-calibrating soil heat flux sensors (HFP01) were installed 0.05 m below the soil surface. Soil temperature (109-L, Campbell Scientific Ltd., Edmonton, Alberta, Canada) and soil water content (EnviroSMART, Campbell Scientific Ltd., Edmonton) were measured at depths of 5, 10, 15, 20, and 40 cm and 20, 40, 60, 100, and 200 cm, respectively. Precipitation was measured by a rain gauge (TE525MM, Texas Electronics Inc., Dallas, TX, USA) at a height of 0.5 m above the soil surface. The 30-min meteorological data were saved in a data logger (CR3000, Campbell Scientific, Logan).
2.3 Data processing
The EddyPro 6.2 software (LiCor, Lincoln, NE, USA) was used to compute the 10-Hz raw data. The Express Mode of EddyPro was used to calculate the 30-min fluxes, such as net ecosystem productivity (NEP), latent heat and sensible heat. The process includes several corrections such as spike removal, tilt correction (secondary coordinate rotation), corrections for air density fluctuation (Webb, Pearman, & Leuning, 1980), and sonic virtual temperature correction. Spectral corrections for flux losses were also applied following the method of Moncrieff et al. (1997).
The 30-min flux data were processed using the methodologies recommended by ChinaFLUX (Zhang, 2006; Yu et al., 2008). The flux data points were removed during sensor malfunction and maintenance, rain, and snow events. The night-time flux data were filtered under low atmospheric turbulence conditions when the thresholds of friction velocity (u*) were < 0.10 m s-1 (Gao et al., 2012). Spurious ecosystem CO2 uptake took taken place during the non-growing season due to the self-heating of the open-path CO2/H2O infrared sensor (Burba et al., 2008). Hence, negative CO2 flux data (representing the ecosystem uptake of CO2 from the atmosphere) were removed before further analysis.
The linear regression model was used to fill in missing data for short periods (< 2 h), and longer periods of missing data were replaced with mean diurnal variation with a 7-d window (Falge et al., 2001). The Michaelis–Menten equation with a 10-day moving window (Michaelis & Menten, 1913) was used to estimate the day-time NEP (NEPday) for larger gaps. The night-time NEP (NEPnight) is the ecosystem respiration (Re) at night (Renight). The enhanced Van’t Hoff equation (Yu et al., 2008) was used to estimate the missing Renight and day-time Re (Reday), which includes soil moisture at 20 cm and soil temperature at 5 cm. Details of the equation were described by Yu et al. (2008). The daily Re was the sum of Renight and Reday. The daily gross ecosystem productivity (GEP) was the sum of the daily NEP and daily Re (GEP = NEP + Re).
2.4 Definition and analysis of net carbon uptake period
The annual NEP is strongly correlated with the net carbon uptake period (Fu et al., 2017). We used a seven-day moving average to determine the spring start day of the net carbon uptake (SCUP) and autumn end day (ECUP) of the net carbon uptake. The SCUP is the first day, and ECUP is the last when the seven-day moving average of NEP is above 0. The duration of the net carbon uptake period (DCUP) is defined by the number of days between SCUP and ECUP (Fu et al., 2017).
2.5 Statistical analysis
We used the function ‘
mkttest’ in ‘
modifiedmk’ package of RStudio 4.0.3 to estimate the long-term trend (low-frequency signal) of annual carbon fluxes (i.e., GEP, Re, and NEP), environmental variables (i.e., air temperature, precipitation), and net carbon uptake period (i.e., DCUP, SCUP, ECUP). The linear regression was used to model the relationships between carbon fluxes and other variables (i.e., air temperature, precipitation, DCUP, SCUP, ECUP) on annual and seasonal scales,with the seasons being defined as spring (March–May), summer (June–August), and autumn (September–November). The outliers were detected by Cook’s distance, and the autocorrelation between regression variables were tested by Durbin-Watson test before the linear regression model was established. There were no outliers during the observed dates, and no autocorrelation among the residuals of the linear model. Linear regression, Cook’s distance, and Durbin-Watson test were performed using IBM SPSS software (version 26.0; IBM Corp., USA).
We used the Pearson correlation coefficients to determine the relationship between carbon flux, environmental variables, and net carbon uptake period (i.e., SCUP, ECUP), at the seasonal scale. The significance threshold was set at a p-value of 0.05. The plot was generated using OriginPro 2020 software (OriginPro Lab Corp., Northampton, MA, USA). The plot was generated using OriginPro 2020 software (OriginPro Lab Corp., Northampton, MA, USA).