# Interannual Variations of Water and Carbon Dioxide Fluxes over a Semiarid Alpine Steppe on the Tibetan Plateau

Authors: Yang Liu; Huizhi Liu; Fengquan Li; Qun Du; Lujun Xu; Yaohui Li
Journal: Advances in Meteorology
 (2022)

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## Abstract

Water and carbon exchanges between grassland and the atmosphere are important processes for water balance and carbon balance. Based on eddy covariance observations over a semiarid alpine steppe ecosystem in Bange on the Tibetan Plateau during the growing season from 2014 to 2017, the variations in evapotranspiration (ET), net ecosystem exchange (NEE), and their components and the associated driving factors were analyzed. Linear and nonlinear models were applied to investigate the relationships between fluxes and their controlling factors over different timescales. The results show that the average ET for the growing season ranged from 1.1 to 2.4 mm/d with an average of 2.0 mm/d for the four consecutive years. Drought conditions reduced the surface conductance and hence the Priestley–Taylor coefficient. Mean T/ET was low (0.34) due to low vegetation cover. Plant growth increased the T/ET ratio during the growing season, whereas soil water content (SWC) explained most of the variation of ET andE on daily and monthly scales. The Enhanced Vegetation Index (EVI) was the most important controlling factor for temperature. Transpiration increased with SWC in dry conditions. For the growing season in 2014, 2016, and 2017, Bange was a carbon sink, while it was a carbon source in 2015. The largest CO2 flux was higher and the temperature sensitivity coefficient (Q10) was lower for 2015 than for the other three years. SWC affected these photosynthesis and respiration parameters. The ratio of respiration (Re) to gross primary production (GPP) was the highest during the 2015 growing season. Both on daily and monthly scales, Re was positively and linearly correlated with GPP. The most important controlling factor for the CO2 flux was EVI on daily and monthly scales.


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## Body

## 1. Introduction

Evapotranspiration (ET), which consists of transpiration (T) from the plant canopy and evaporation (E) from bare soil and leaf interception [1], is an important component of the terrestrial water balance and a link between carbon and energy cycles [2]. The ratio of T to ET (T/ET) represents the allocation between biological and physical processes for the ecosystem water flux [3]. Furthermore, the component fluxes of ET are used as the input, calibration, and validation of many hydrological and land surface models. The eddy covariance (EC) technique only provides observations of net ET fluxes. Partitioning ET into its component fluxes is important to interpret the water balance and ecological processes [4].The CO2 flux is affected by many environmental factors. Extreme climatic events (e.g., droughts) can lead to anomalies in carbon dioxide fluxes [5]. Both gross primary production (GPP) and ecosystem respiration (Re) can be affected by droughts and hence affect the magnitude of the net ecosystem exchange (NEE).The Tibetan Plateau (TP) is the highest plateau in the world, with an average elevation of over 4000 m [6]. The Tibetan Plateau ecosystem is very sensitive to climate change [7]. From the beginning of this century, a lot of field experiments about the land-atmosphere interaction on the Tibetan Plateau have been carried out [7–11]. Over an alpine meadow on the Qinghai-Tibetan Plateau, soil water content (SWC) and air temperature (Ta) were the most important environmental factors in surface energy partitioning on seasonal scales [12]. On the annual scale, the ratio of annual evapotranspiration (ET) to annual precipitation (PPT) is about 60% in the Haibei meadow [7] while the ratio nearly equals 1 for the alpine steppe in Shuanghu, Tibet, China [9]. The Tibetan Plateau spans nearly 29° of longitude and 15° of altitude. The water and CO2 exchanges over grassland ecosystems vary in different climate zones. Information regarding ET and NEE is necessary to understand the responses of the Tibetan Plateau alpine steppe to climate change. Many studies on the Tibetan Plateau have been focused on the characteristics of ET. There is a need to partition ET over TP and examine how ET, NEE, and their components respond to the environmental and biological factors against the background of climate change.In this context, eddy covariance measurements of water and carbon dioxide fluxes at the Bange site during the growing season (June to September) from 2014 to 2017 were used to analyze the variation of evapotranspiration and its components. The main objectives in this study were to (1) characterize the seasonal and interannual variations of ET and NEE and their partitioning over this semiarid alpine steppe; (2) determine the parameters of photosynthesis and respiration; and (3) investigate the important environmental and meteorological factors that control ET, NEE, and their components on daily and monthly scales.


## 2. Materials and Methods

### 2.1. Site Description

The observation site is located at Bange (31°25′N, 90°2′E elevation 4700 m) in a sub-frigid semiarid climatic zone on the Tibetan Plateau. This site is under the influence of the plateau monsoon, with a 30-year (1980–2010) mean annual air temperature of −2.8°C and an annual total precipitation of 308.3 mm, most of which falls from June to October. The soil is classified as light-color sandy loam. The land cover type of this site is alpine steppe, and the dominant species isStipa purpurea (C3 grass), with a mean plant height of less than 3 cm throughout the growing season. In this study the growing season is defined as the period from June to September.


### 2.2. EC Measurements and EVI

Based on the “Third Tibetan Plateau Scientific Experiment,” the site was set up on 15 July 2014 with measurements taken until 6 September 2017. Water vapor and carbon dioxide concentrations were measured using the EC system, which consisted of a three-dimensional sonic anemometer (CSAT-3, Campbell, USA) and an open-path CO2/H2O infrared gas analyzer (LI-7500A, LI-COR, USA). Continuous auxiliary measurements of meteorological and environmental conditions were also performed (Table 1). More details of this site and measurements are described in Wang et al. [8].Table 1
Variables, instruments, and measurement heights.
Variables
Instruments
Measurement heights [m]
Temperature
HMP45C, Campbell
2, 4, 10
Relative humidity
HMP45C, Campbell
2, 4, 10
Wind speed
010C, Campbell
2, 4, 10
Wind direction
020C, Campbell
10
Net radiation
CNR4, Kipp & Zonen
1.5
PAR
LI190SB, LI-COR
1.5
Soil heat flux
HFP01, Hukseflux
0.05, 0.1 (depth)
Soil temperature
109-L, Campbell
0.05, 0.1, 0.2, 0.5, 1 (depth)
Soil water content
CS616, Campbell
0.05, 0.1, 0.2, 0.5, 1 (depth)
Precipitation
52202, Young
—As a proxy for grass phenology, the Enhanced Vegetation Index (EVI) with a spatial and temporal resolution of 250 m and 16 days, respectively, was retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS, MOD13Q1). To transform biweekly EVI data into daily intervals, the data were linearly interpolated to fill gaps.


### 2.3. Flux Calculation and Post-processing

The EddyPro 6.2.0 package was used to process the 10 Hz raw data and calculate 30 min-averaged fluxes. Post-processing of the raw data mainly included double rotation [13] for tilt correction, spectral loss correction [14], and WPL density fluctuation correction [15].In addition, the test of the stationary conditions for LE and the integral turbulence characteristics was performed as described by [16]. To produce a continuous dataset, data gaps were filled with look-up tables and mean diurnal course methods.


### 2.4. Partitioning of ET and Treatments of NEE

The ET partitioning method employed in this study, known as FVS method, is based on the analysis of the correlation structure of CO2 and H2O. It assumes that the flux variance similarity theory applies separately to CO2 and H2O stomatal (photosynthesis, transpiration) and non-stomatal (respiration, evaporation) components. Using the estimated leaf-level water use efficiency (WUE) as input, the FVS method calculates the contribution of each component to the total fluxes of ET, and thus the magnitude of each component flux can be determined. WUE is the only input parameter needed in the FVS method. WUE was estimated as follows:(1)WUE=0.7cs¯−ci¯qs¯−qi¯=0.7cs¯1−kqs¯−qi¯,where cs and qs are the concentrations of carbon dioxide and water vapor at the leaf surface, respectively, which were inferred from the EC-measurement level value by considering the mean profile; ci and qi represent the inter-stomatal concentrations of carbon dioxide and water vapor, respectively. qi was assumed to be saturated at the leaf surface temperature (estimated as air temperature), and ci was calculated from cs¯ by parameterizing the ci¯/cs¯ratio,k [17]. For C3 species, k was parameterized as follows:(2)k=−0.084qi¯−qs¯+0.8793.The partitioning of ET fluxes was applied to half-hourly measurements during the daytime.The van’t Hoff equation was applied to partition NEE into Re and GPP [18]. Specifically, the nighttime half-hourly NEE data (assumed to be equal to nighttime Re) were fitted against the soil temperature at a depth of 5 cm (Ts) using the following formula:(3)Re=aebTs,where a and b are the regression parameters. Then, the daytime estimates of Re were derived using daytime Ts and (3). GPP was calculated as the difference between NEE and Re:(4)GPP=Re−NEE.The Michaelis–Menten model was used to describe the relationship between daytime NEE (Fc) and PAR:(5)Fc=FmaxαPARFmax+αPAR+Rebulk,where PAR is the photosynthetic active radiation (μmolm−2s−1), Fmax is the NEE at a saturating light level (μmolm−2s−1), α is the apparent quantum yield (μmolCO2μmol−1photons), and Rebulk is the bulk estimated Re.


### 2.5. Calculation of Bulk Surface Parameters

The bulk parameter of surface conductance (Gs) was employed to represent the surface-atmosphere exchange efficiency [19]. By inverting the Penman–Monteith equation, Gsms−1 was derived as follows [20]:(6)Gs=GaγLEρaCpGaVPD+∆H−γLE,where Ga is the aerodynamic conductance (ms−1), and Ga=1/u/u∗2+6.2u∗−0.67 in which u is the wind speed (ms−1); γ is the psychrometric constant (kPaK−1); ρa is the air density (kgm−3); Cp is the specific heat of air at constant pressure (Jkg−1K−1); VPD is the vapor pressure deficit (kPa); ∆ is the slope of the vapor pressure curve at the mean air temperature (kPaK−1); and H is the sensible heat flux (Wm−2).The Priestley–Taylor coefficientα was calculated as the ratio of the actual evapotranspiration rate (ET) to the equilibrium evapotranspiration rate ETeq:(7)α=ETETeq=∆+γ∆LERn−G,where Rn is the net radiation (Wm−2) and G is the soil heat flux (Wm−2).


## 2.1. Site Description

The observation site is located at Bange (31°25′N, 90°2′E elevation 4700 m) in a sub-frigid semiarid climatic zone on the Tibetan Plateau. This site is under the influence of the plateau monsoon, with a 30-year (1980–2010) mean annual air temperature of −2.8°C and an annual total precipitation of 308.3 mm, most of which falls from June to October. The soil is classified as light-color sandy loam. The land cover type of this site is alpine steppe, and the dominant species isStipa purpurea (C3 grass), with a mean plant height of less than 3 cm throughout the growing season. In this study the growing season is defined as the period from June to September.


## 2.2. EC Measurements and EVI

Based on the “Third Tibetan Plateau Scientific Experiment,” the site was set up on 15 July 2014 with measurements taken until 6 September 2017. Water vapor and carbon dioxide concentrations were measured using the EC system, which consisted of a three-dimensional sonic anemometer (CSAT-3, Campbell, USA) and an open-path CO2/H2O infrared gas analyzer (LI-7500A, LI-COR, USA). Continuous auxiliary measurements of meteorological and environmental conditions were also performed (Table 1). More details of this site and measurements are described in Wang et al. [8].Table 1
Variables, instruments, and measurement heights.
Variables
Instruments
Measurement heights [m]
Temperature
HMP45C, Campbell
2, 4, 10
Relative humidity
HMP45C, Campbell
2, 4, 10
Wind speed
010C, Campbell
2, 4, 10
Wind direction
020C, Campbell
10
Net radiation
CNR4, Kipp & Zonen
1.5
PAR
LI190SB, LI-COR
1.5
Soil heat flux
HFP01, Hukseflux
0.05, 0.1 (depth)
Soil temperature
109-L, Campbell
0.05, 0.1, 0.2, 0.5, 1 (depth)
Soil water content
CS616, Campbell
0.05, 0.1, 0.2, 0.5, 1 (depth)
Precipitation
52202, Young
—As a proxy for grass phenology, the Enhanced Vegetation Index (EVI) with a spatial and temporal resolution of 250 m and 16 days, respectively, was retrieved from the Moderate-Resolution Imaging Spectroradiometer (MODIS, MOD13Q1). To transform biweekly EVI data into daily intervals, the data were linearly interpolated to fill gaps.


## 2.3. Flux Calculation and Post-processing

The EddyPro 6.2.0 package was used to process the 10 Hz raw data and calculate 30 min-averaged fluxes. Post-processing of the raw data mainly included double rotation [13] for tilt correction, spectral loss correction [14], and WPL density fluctuation correction [15].In addition, the test of the stationary conditions for LE and the integral turbulence characteristics was performed as described by [16]. To produce a continuous dataset, data gaps were filled with look-up tables and mean diurnal course methods.


## 2.4. Partitioning of ET and Treatments of NEE

The ET partitioning method employed in this study, known as FVS method, is based on the analysis of the correlation structure of CO2 and H2O. It assumes that the flux variance similarity theory applies separately to CO2 and H2O stomatal (photosynthesis, transpiration) and non-stomatal (respiration, evaporation) components. Using the estimated leaf-level water use efficiency (WUE) as input, the FVS method calculates the contribution of each component to the total fluxes of ET, and thus the magnitude of each component flux can be determined. WUE is the only input parameter needed in the FVS method. WUE was estimated as follows:(1)WUE=0.7cs¯−ci¯qs¯−qi¯=0.7cs¯1−kqs¯−qi¯,where cs and qs are the concentrations of carbon dioxide and water vapor at the leaf surface, respectively, which were inferred from the EC-measurement level value by considering the mean profile; ci and qi represent the inter-stomatal concentrations of carbon dioxide and water vapor, respectively. qi was assumed to be saturated at the leaf surface temperature (estimated as air temperature), and ci was calculated from cs¯ by parameterizing the ci¯/cs¯ratio,k [17]. For C3 species, k was parameterized as follows:(2)k=−0.084qi¯−qs¯+0.8793.The partitioning of ET fluxes was applied to half-hourly measurements during the daytime.The van’t Hoff equation was applied to partition NEE into Re and GPP [18]. Specifically, the nighttime half-hourly NEE data (assumed to be equal to nighttime Re) were fitted against the soil temperature at a depth of 5 cm (Ts) using the following formula:(3)Re=aebTs,where a and b are the regression parameters. Then, the daytime estimates of Re were derived using daytime Ts and (3). GPP was calculated as the difference between NEE and Re:(4)GPP=Re−NEE.The Michaelis–Menten model was used to describe the relationship between daytime NEE (Fc) and PAR:(5)Fc=FmaxαPARFmax+αPAR+Rebulk,where PAR is the photosynthetic active radiation (μmolm−2s−1), Fmax is the NEE at a saturating light level (μmolm−2s−1), α is the apparent quantum yield (μmolCO2μmol−1photons), and Rebulk is the bulk estimated Re.


## 2.5. Calculation of Bulk Surface Parameters

The bulk parameter of surface conductance (Gs) was employed to represent the surface-atmosphere exchange efficiency [19]. By inverting the Penman–Monteith equation, Gsms−1 was derived as follows [20]:(6)Gs=GaγLEρaCpGaVPD+∆H−γLE,where Ga is the aerodynamic conductance (ms−1), and Ga=1/u/u∗2+6.2u∗−0.67 in which u is the wind speed (ms−1); γ is the psychrometric constant (kPaK−1); ρa is the air density (kgm−3); Cp is the specific heat of air at constant pressure (Jkg−1K−1); VPD is the vapor pressure deficit (kPa); ∆ is the slope of the vapor pressure curve at the mean air temperature (kPaK−1); and H is the sensible heat flux (Wm−2).The Priestley–Taylor coefficientα was calculated as the ratio of the actual evapotranspiration rate (ET) to the equilibrium evapotranspiration rate ETeq:(7)α=ETETeq=∆+γ∆LERn−G,where Rn is the net radiation (Wm−2) and G is the soil heat flux (Wm−2).


## 3. Results

### 3.1. Meteorological Conditions

The daytime average downward solar radiation (Rs) ranged from 205.7 to 689.3 wm−2, with a mean value of 464.9 wm−2 (Figure 1(a)). The air temperature (Ta) and soil temperature at 5 cm depth (Ts) corresponded well with Rs. Following the variability of Rs, Ta and Ts increased at the beginning of the growing season, peaked in mid-growing season, and then decreased (Figure 1(b)). The daytime-mean Ta and Ts were generally higher than 5°C except at the end of the 2014 growing season. During the growing season of 2016, Ta was lower than that in 2015 or 2017 (Table 2). The lower temperature in the early growing season may have caused the delayed onset of the plant green-up day.Figure 1
Seasonal variations of daytime average downward solar radiation (Rs), air temperature (Ta), wind speed (WS), vapor pressure deficit (VPD), precipitation, and soil water content at 5 cm depth below ground (SWC) during the growing season from 2014 to 2017. The daytime average of the variable was calculated with half-hourly data at conditions of Rs > 100 wm−2.Table 2
Periodical average of the daytime-mean shortwave radiation (Rs), air temperature (Ta), soil temperature at 5 cm depth (Ts), vapor pressure deficit (VPD), wind speed (WS), soil water content at 5 cm depth (SWC), and periodical sum of precipitation (PPT) for the growing season from 2014 to 2017.
RsWm−2
Ta [°C]
Ts [°C]
WS [ms−1]
VPD [kPa]
SWC [m3m−3]
PPT [mm]
2014
218.7
9.4
12.5
3.37
0.53
0.15
209.6
2015
278.1
11.5
15.1
4.40
0.91
0.08
121.3
2016
240.6
10.1
13.8
4.15
0.61
0.13
392.1
2017
247.2
10.8
14.9
4.21
0.64
0.13
373.5The daytime-mean wind speed (WS) at Bange was about 4 m/s during the growing season. WS in 2014 was clearly lower than that during the other three years (Figure1(c)). During the growing season in 2015, the vapor pressure deficit (VPD) was the largest (Figure 1(d)), and the soil water content at 5 cm depth (SWC) was the lowest (Figure 1(e)). The fluctuations of VPD and SWC were mainly affected by the precipitation. Bange received the lowest amount of precipitation in 2015, indicating that severe drought and water deficit may have limited ET and its component fluxes that year.The timing when EVI reached its peak value was different for each year (Figure2). EVI peaked in late August, early September, late August, and late July from 2014 to 2017, respectively (Figure 2). The mean value of EVI during the growing season for the consecutive four years was 0.21, 0.13, 0.17, and 0.20, respectively. Although the steppe received more precipitation in June 2016 than in June 2017, the lower temperature may have hindered plant growth, and thus EVI in 2016 was lower.Figure 2
Variations of EVI during the growing season from 2014 to 2017.


### 3.2. Variations of ET,Gs, and ET/ETeq

The temporal variation pattern of ET was highly related to the variations of Rs and SWC. The intra-seasonal trends of ET in 2014 and 2017 were not significant due to the even distributions of precipitation. By contrast, the inter-seasonal trend of ET in 2015 and 2016 was obvious due to the uneven distribution of precipitation (Figure3(a)). ET in 2015 showed clear seasonal variation corresponding to rainfall events, which recharged the soil water during this drought year. ET in 2016 peaked in July. The average ET values were 2.2 mm/d and 1.1 mm/d for the growing season in 2014 and 2015, respectively, while the mean values of ET were larger for 2016 and 2017 with a value of 2.4 mm/d for both years. The mean ET value for the whole study period was 2.0 mm/d, and the maximum daily ET was 3.9 mm/d, which occurred both in 2016 and 2017. This is consistent with the mean daily ET over another alpine steppe at 4947 m a.s.l. [9] with a mean value of 2.1 mm/d and a maxima of 4.2 mm/d.Figure 3
Variations of ET,Gs, and ET/ETeq throughout the growing season from 2014 to 2017.The variation of dailyGs corresponded well with that of ET (Figure 3(b)). Gs increased gradually, peaked during the mid-growing season, and then decreased. The variation pattern of Gs was clearly modified by the precipitation events. It was less than 10.0 mms−1 when no precipitation occurred, whereas Gs reached a relatively high value following precipitation, particularly in 2016 and 2017. The mean value of Gs at this site was 10.0 mms−1 (±7.0mms−1) and the maxima was 32.6 mms−1, which occurred in 2017. The mean value of Gs during the growing season was lower than that of alpine meadows on the Qinghai-Tibetan Plateau (11.0 mms−1 [12]), but it was greater than that of another semiarid alpine steppe on TP (7.5 mms−1, [9]).The Priestley–Taylor coefficientα (=ET/ETeq) was significantly lower than the suggested value of 1.26 for humid climates (Figure 3(c)), indicating that the surface was far from saturated. The α ranged between 0.09 and 1.02 with a mean value of 0.47 for the whole study period. The averageα during the growing season for the four consecutive years was 0.61, 0.31, 0.50, and 0.51, respectively. The small magnitude of α in 2015 represents the limitation of the water supply contributing to the low ET, which may also be reflected in the magnitude of SWC. Therefore, the low ET rate for the growing season in 2015 was attributed to the water deficit rather than the low atmospheric evaporative demand or available energy. Zhang et al. [21] measured a value of α in the growing season at Haibei over QTP of 1.37, which is higher than 1.26, and they concluded that net radiation limits ET over the alpine meadow.


### 3.3. Variation of ET Partitioning

The variation of direct evaporation (E) and plant transpiration (T) is shown in Figure 4(a). E was overall much greater than plant T, except during dry conditions. The mean E for each growing season was 1.55, 0.78, 1.59, and 1.43 mm/d, respectively, from 2014 to 2017 with an ensemble mean of 1.33 mm/d (Table 3). As for T, the mean value was 0.62, 0.43, 0.73, and 0.94 mm/d for the four years, respectively, with an ensemble mean of 0.68 mm/d.Figure 4
Variations of mean daily ET (mm/d), E (mm/d), T (mm/d), and T/ET for the growing season from 2014 to 2017.Table 3
Mean ET,E, T, and T/ET for the growing season from 2014 to 2017 (mm/d).
Year
ET
E
T
T/ET
2014
2.17
1.55
0.62
0.29
2015
1.21
0.78
0.43
0.36
2016
2.32
1.59
0.73
0.31
2017
2.37
1.43
0.94
0.40The ratio of transpiration to evapotranspiration (T/ET) ranged from 0.03 to 0.84 throughout the measurement period, with a mean value of 0.34 (Figure4(b)). For each year, mean T/ET was 0.29, 0.36, 0.31, and 0.40, respectively. Compared with other grasslands over QTP, the Bange site had a much lower T/ET. The maximum T/ET for 2016 was 0.72 when EVI reached its peak. Hu et al. [22] reported a maximum T/ET value of ca. 0.8 during conditions of peak LAI at an alpine shrub-meadow. Similarly [23], reported a maximum T/ET that reached 0.93–0.98 (mean = 0.96) over alpine grassland during the peak growing season. Our result is consistent with previous studies and shows that the contribution of evaporation to ET reduced with plant growth. However, the maximum T/ET was significantly lower than 1 due to the low vegetation cover at this site.The seasonal pattern of T/ET was related to plant growth but strongly modified by SWC (Figure5). During drought conditions in 2015, the maximum T/ET reached 0.84, which is greater than the maximum value of 2016 (0.72). Fewer rainfall events resulted in less wetting of the surface for evaporation, which therefore reduced E, whereas grass could use available root-zone soil water for transpiration, which was less available for E [24]. This effect of SWC on T/ET has been previously reported over different ecosystems including desert shrubland, savanna, and a grassland site in Arizona, USA [25].Figure 5
The relationship between 16 d averaged T/ET and MODIS EVI. The color shading represents the value of SWC. The gray ellipse highlights the extremely dry conditions during 2015. The gray rectangle indicates wet conditions.


### 3.4. Variations of NEE, GPP, and Re

The seasonal and interannual variation of daily gap-filled and partitioned NEE, GPP, and Re are displayed in Figure6 and Table 4. For substantial parts of the wet years (2014, 2016, and 2017), the steppe ecosystem was a net sink of CO2 characterized by the negative value of NEE. However, for late June and August in 2015 and for the early growing season in June 2016, this ecosystem was a strong source of CO2, and NEE reached a maximum of 1.25 g Cm−2d−2 on 14 August 2015.Figure 6
Variations of NEE, Re, and GPP throughout the growing season from 2014 to 2017.Table 4
Total NEE, Re, and GPP for the growing season from 2014 to 2017 (gCm−2mon−1).
Year
NEE
Re
GPP
Re/GPP
2014
−17.77
85.13
102.90
0.83
2015
16.83
60.02
43.19
1.39
2016
−21.96
108.89
130.85
0.83
2017
−29.39
107.65
137.04
0.79The maximum daily GPP during the growing season was 2.00, 0.70, 1.97, and 2.24g Cm−2d−2 for the four years, respectively. The GPP in the drought year of 2015 was clearly lower than that during the other three years, consistent with the lower EVI. During this drought year, Re was also lower than that for the wet years.


## 3.1. Meteorological Conditions

The daytime average downward solar radiation (Rs) ranged from 205.7 to 689.3 wm−2, with a mean value of 464.9 wm−2 (Figure 1(a)). The air temperature (Ta) and soil temperature at 5 cm depth (Ts) corresponded well with Rs. Following the variability of Rs, Ta and Ts increased at the beginning of the growing season, peaked in mid-growing season, and then decreased (Figure 1(b)). The daytime-mean Ta and Ts were generally higher than 5°C except at the end of the 2014 growing season. During the growing season of 2016, Ta was lower than that in 2015 or 2017 (Table 2). The lower temperature in the early growing season may have caused the delayed onset of the plant green-up day.Figure 1
Seasonal variations of daytime average downward solar radiation (Rs), air temperature (Ta), wind speed (WS), vapor pressure deficit (VPD), precipitation, and soil water content at 5 cm depth below ground (SWC) during the growing season from 2014 to 2017. The daytime average of the variable was calculated with half-hourly data at conditions of Rs > 100 wm−2.Table 2
Periodical average of the daytime-mean shortwave radiation (Rs), air temperature (Ta), soil temperature at 5 cm depth (Ts), vapor pressure deficit (VPD), wind speed (WS), soil water content at 5 cm depth (SWC), and periodical sum of precipitation (PPT) for the growing season from 2014 to 2017.
RsWm−2
Ta [°C]
Ts [°C]
WS [ms−1]
VPD [kPa]
SWC [m3m−3]
PPT [mm]
2014
218.7
9.4
12.5
3.37
0.53
0.15
209.6
2015
278.1
11.5
15.1
4.40
0.91
0.08
121.3
2016
240.6
10.1
13.8
4.15
0.61
0.13
392.1
2017
247.2
10.8
14.9
4.21
0.64
0.13
373.5The daytime-mean wind speed (WS) at Bange was about 4 m/s during the growing season. WS in 2014 was clearly lower than that during the other three years (Figure1(c)). During the growing season in 2015, the vapor pressure deficit (VPD) was the largest (Figure 1(d)), and the soil water content at 5 cm depth (SWC) was the lowest (Figure 1(e)). The fluctuations of VPD and SWC were mainly affected by the precipitation. Bange received the lowest amount of precipitation in 2015, indicating that severe drought and water deficit may have limited ET and its component fluxes that year.The timing when EVI reached its peak value was different for each year (Figure2). EVI peaked in late August, early September, late August, and late July from 2014 to 2017, respectively (Figure 2). The mean value of EVI during the growing season for the consecutive four years was 0.21, 0.13, 0.17, and 0.20, respectively. Although the steppe received more precipitation in June 2016 than in June 2017, the lower temperature may have hindered plant growth, and thus EVI in 2016 was lower.Figure 2
Variations of EVI during the growing season from 2014 to 2017.


## 3.2. Variations of ET,Gs, and ET/ETeq

The temporal variation pattern of ET was highly related to the variations of Rs and SWC. The intra-seasonal trends of ET in 2014 and 2017 were not significant due to the even distributions of precipitation. By contrast, the inter-seasonal trend of ET in 2015 and 2016 was obvious due to the uneven distribution of precipitation (Figure3(a)). ET in 2015 showed clear seasonal variation corresponding to rainfall events, which recharged the soil water during this drought year. ET in 2016 peaked in July. The average ET values were 2.2 mm/d and 1.1 mm/d for the growing season in 2014 and 2015, respectively, while the mean values of ET were larger for 2016 and 2017 with a value of 2.4 mm/d for both years. The mean ET value for the whole study period was 2.0 mm/d, and the maximum daily ET was 3.9 mm/d, which occurred both in 2016 and 2017. This is consistent with the mean daily ET over another alpine steppe at 4947 m a.s.l. [9] with a mean value of 2.1 mm/d and a maxima of 4.2 mm/d.Figure 3
Variations of ET,Gs, and ET/ETeq throughout the growing season from 2014 to 2017.The variation of dailyGs corresponded well with that of ET (Figure 3(b)). Gs increased gradually, peaked during the mid-growing season, and then decreased. The variation pattern of Gs was clearly modified by the precipitation events. It was less than 10.0 mms−1 when no precipitation occurred, whereas Gs reached a relatively high value following precipitation, particularly in 2016 and 2017. The mean value of Gs at this site was 10.0 mms−1 (±7.0mms−1) and the maxima was 32.6 mms−1, which occurred in 2017. The mean value of Gs during the growing season was lower than that of alpine meadows on the Qinghai-Tibetan Plateau (11.0 mms−1 [12]), but it was greater than that of another semiarid alpine steppe on TP (7.5 mms−1, [9]).The Priestley–Taylor coefficientα (=ET/ETeq) was significantly lower than the suggested value of 1.26 for humid climates (Figure 3(c)), indicating that the surface was far from saturated. The α ranged between 0.09 and 1.02 with a mean value of 0.47 for the whole study period. The averageα during the growing season for the four consecutive years was 0.61, 0.31, 0.50, and 0.51, respectively. The small magnitude of α in 2015 represents the limitation of the water supply contributing to the low ET, which may also be reflected in the magnitude of SWC. Therefore, the low ET rate for the growing season in 2015 was attributed to the water deficit rather than the low atmospheric evaporative demand or available energy. Zhang et al. [21] measured a value of α in the growing season at Haibei over QTP of 1.37, which is higher than 1.26, and they concluded that net radiation limits ET over the alpine meadow.


## 3.3. Variation of ET Partitioning

The variation of direct evaporation (E) and plant transpiration (T) is shown in Figure 4(a). E was overall much greater than plant T, except during dry conditions. The mean E for each growing season was 1.55, 0.78, 1.59, and 1.43 mm/d, respectively, from 2014 to 2017 with an ensemble mean of 1.33 mm/d (Table 3). As for T, the mean value was 0.62, 0.43, 0.73, and 0.94 mm/d for the four years, respectively, with an ensemble mean of 0.68 mm/d.Figure 4
Variations of mean daily ET (mm/d), E (mm/d), T (mm/d), and T/ET for the growing season from 2014 to 2017.Table 3
Mean ET,E, T, and T/ET for the growing season from 2014 to 2017 (mm/d).
Year
ET
E
T
T/ET
2014
2.17
1.55
0.62
0.29
2015
1.21
0.78
0.43
0.36
2016
2.32
1.59
0.73
0.31
2017
2.37
1.43
0.94
0.40The ratio of transpiration to evapotranspiration (T/ET) ranged from 0.03 to 0.84 throughout the measurement period, with a mean value of 0.34 (Figure4(b)). For each year, mean T/ET was 0.29, 0.36, 0.31, and 0.40, respectively. Compared with other grasslands over QTP, the Bange site had a much lower T/ET. The maximum T/ET for 2016 was 0.72 when EVI reached its peak. Hu et al. [22] reported a maximum T/ET value of ca. 0.8 during conditions of peak LAI at an alpine shrub-meadow. Similarly [23], reported a maximum T/ET that reached 0.93–0.98 (mean = 0.96) over alpine grassland during the peak growing season. Our result is consistent with previous studies and shows that the contribution of evaporation to ET reduced with plant growth. However, the maximum T/ET was significantly lower than 1 due to the low vegetation cover at this site.The seasonal pattern of T/ET was related to plant growth but strongly modified by SWC (Figure5). During drought conditions in 2015, the maximum T/ET reached 0.84, which is greater than the maximum value of 2016 (0.72). Fewer rainfall events resulted in less wetting of the surface for evaporation, which therefore reduced E, whereas grass could use available root-zone soil water for transpiration, which was less available for E [24]. This effect of SWC on T/ET has been previously reported over different ecosystems including desert shrubland, savanna, and a grassland site in Arizona, USA [25].Figure 5
The relationship between 16 d averaged T/ET and MODIS EVI. The color shading represents the value of SWC. The gray ellipse highlights the extremely dry conditions during 2015. The gray rectangle indicates wet conditions.


## 3.4. Variations of NEE, GPP, and Re

The seasonal and interannual variation of daily gap-filled and partitioned NEE, GPP, and Re are displayed in Figure6 and Table 4. For substantial parts of the wet years (2014, 2016, and 2017), the steppe ecosystem was a net sink of CO2 characterized by the negative value of NEE. However, for late June and August in 2015 and for the early growing season in June 2016, this ecosystem was a strong source of CO2, and NEE reached a maximum of 1.25 g Cm−2d−2 on 14 August 2015.Figure 6
Variations of NEE, Re, and GPP throughout the growing season from 2014 to 2017.Table 4
Total NEE, Re, and GPP for the growing season from 2014 to 2017 (gCm−2mon−1).
Year
NEE
Re
GPP
Re/GPP
2014
−17.77
85.13
102.90
0.83
2015
16.83
60.02
43.19
1.39
2016
−21.96
108.89
130.85
0.83
2017
−29.39
107.65
137.04
0.79The maximum daily GPP during the growing season was 2.00, 0.70, 1.97, and 2.24g Cm−2d−2 for the four years, respectively. The GPP in the drought year of 2015 was clearly lower than that during the other three years, consistent with the lower EVI. During this drought year, Re was also lower than that for the wet years.


## 4. Discussion

### 4.1. Biological Control of ET via Surface Conductance

The surface conductanceGsmms−1 is the total conductance representing the aggregation of plant transpiration, plant interception evaporation, and bare soil evaporation [26]. The relationship between Gs and environmental factors is investigated in this section. Gs was responsive to available water but insensitive to air temperature and radiation (data not shown). Specifically, Gs decreased with increasing VPD (Figure 7(a)). The Gs-VPD relationship is expressed with an exponential model:(8)Gs=34.62exp−1.90VPD,R2=0.51,indicating that when VPD was at a low level (less than about 0.7 kPa), a minor increase of VPD caused a substantial decrease in Gs. However, as VPD continued to increase and exceeded a critical value of ca. 1.2 kPa, the magnitude of Gs almost became insensitive to the variation of VPD and stabilized at a constant small value. Similar results have been reported in other grassland ecosystems over TP (e.g., [18, 20]) and North America (e.g., [27]), forest ecosystems (e.g., [28, 29]), and desert shrub (e.g., [30]). Since the magnitude of SWC was highly negatively correlated with that of VPD (data not shown), Gs was also controlled by SWC, and the Gs-SWC relationship was more significant than the Gs-VPD one (Figure 7(b)):(9)Gs=0.73exp19.98SWC,R2=0.60.Figure 7
Variations ofGs with VPD (a) and SWC (b).
(a)
(b)This relationship was consistent with that found in the low range of SWC in a semiarid steppe over Inner Mongolia [31]. However, an expected capping of Gs as the SWC exceeded a critical value (0.16 m3m−3 reported by [8]) was not observed in this study. Gs is expected to remain constant as the SWC continues to increase. However, a positive linear relationship between Gs and SWC was reported at another alpine meadow on the Qinghai-Tibetan Plateau [26] with SWC ranging between ∼0.05 and 0.35 m3m−3. It is expected that Gs will reach a maximum value and then stabilize. The critical value of SWC above which Gs stabilizes for the QTP region remains to be further investigated.G
s represents the combined effect of VPD, Rn, SWC, and physiological rhythm of plants [23]. The relationship between Gsandα (ET/ ETeq) reflects the interactive effects of SWC and LAI with the photosynthesis capacity [32, 33, 34]. Therefore, although individual factors (e.g., precipitation and SWC) varied considerably throughout the study period, ET/ ETeq was scaled with Gs as follows:(10)ETETeq=0.60−0.52exp−0.24Gs,R2=0.78.In 2015Gs was relatively small and the Gs-α relationship fitted (8) well, while in 2016 and 2017 when Gs was relatively large, the Gs-α plot displays some scatter to the fitted line (Figure 8). The magnitude of α increased with Gs in a low Gs section (less than about 12 mms−1), but when Gs exceeded this threshold, α became less sensitive to Gs. This threshold was consistent with the theoretical value (16 mms−1, [35]) and values from other alpine steppes (e.g., 13 mms−1, [9]; 15 mms−1, [36]).Figure 8
Relationship between the daily average surface conductance (Gs) and the Priestley–Taylor coefficient (α=ET/ETeq).


### 4.2. Environmental and Meteorological Controls of Water Flux

To better characterize the variation of ET and its components, the relationships betweenE and T and environmental (SWC) and meteorological (Ta and VPD) factors were investigated on the daily scale. Rn was not a major factor (data not shown) indicating that the exchange of water fluxes was not energy-limited at Bange, different from that of a humid alpine meadow [21].E andT responded to SWC differently (Figure 9). Precipitation at this site has large seasonal variation, which can influence the recharge of soil water. Furthermore, during some periods of the study, there was a shortage of soil water. When SWC was lower than about 0.1 m3m−3, both E and T increased with SWC. As SWC continued to increase, E also kept increasing, whereas T became less sensitive to SWC and fluctuated around 0.8 mmd−1, indicating that the optimal SWC for plant transpiration was greater than about 0.1 m3m−3.Figure 9
Relationship ofE (a) and T (b) with SWC.
(a)
(b)BecauseT became insensitive to the variation of SWC, when SWC reached a critical value, the relationship between T and meteorological factors was analyzed to better understand the variation of T under wet conditions. Only data with SWC > 0.1 m3m−3 were used for the analysis. Significant linear T-Ta and T-VPD relationship were observed (Figure 10). At high SWC levels, the increase of both Ta and VPD caused an increase of T.Figure 10
Variations of daily meanT against Ta (a) and VPD (b) at conditions of SWC greater than 0.1 m3m−3.
(a)
(b)On the monthly scale, both ET andE were linearly and positively correlated with SWC (Figure 11(a) and 11(b)). SWC alone explains 73% (78%) of the variation in ET (E), indicating that water availability was the main factor controlling the long-term ET and E. At the monthly scale, no single factor explained the variation of T. Multiple factors were closely related to plant growth rather than the environmental or meteorological variables. Such a positive linear relationship between T and EVI is consistent with previous work at other grassland sites (e.g., [27]). However, some scatter exists in the T-EVI fit due to the influence of VPD, which represents the evaporative demand.Figure 11
Relationship of the monthly total ET (a) andE (b) with SWC.
(a)
(b)


### 4.3. The Relationship between Daytime NEE and PAR under Different SWC

The relationship between the daytime half-hourly NEE and PAR was described with the Michaelis–Menten model. The NEE-PAR relationships showed a similar pattern for 2014, 2016, and 2017 (Figure12(a)). For these three years, the NEE reached its minimum at PAR of 1300 μmolm−2s−1. When PAR was less than 1300 μmolm−2s−1, the daytime NEE decreased with the increasing PAR. For PAR greater than 1300 μmolm−2s−1, the daytime NEE increased with PAR. Fmax ranged from −3.97 to −4.65 μmolm−2s−1 (Table 5). By contrast, the relationship was significantly different for 2015 compared with the other three years. During 2015, the PAR critical value reduced to 600 μmolm−2s−1, and Fmax decreased to −0.99 μmolm−2s−1.Figure 12
Relationship between the daytime half-hourly NEE and PAR for the growing season from 2014 to 2017 (a), and the whole dataset classified by SWC (b). The NEE and PAR data were averaged using PAR bins of 100μmolm−2s−1.
(a)
(b)Table 5
The ecosystem photosynthesis parameters that describe the daytime NEE-PAR relationship using the Michaelis–Menten model.
Treatment
SWC
F
max
α
R
e
bulk
R
2
2014
0.150
−4.65
−0.043
2.31
0.89
2015
0.076
−0.99
−0.009
0.63
0.78
2016
0.133
−3.97
−0.021
1.36
0.93
2017
0.130
−4.10
−0.028
1.78
0.92
SWC > 0.1
−3.75
−0.024
1.52
0.90
SWC < 0.1
−1.10
−0.046
1.45
0.72To further investigate the influence of SWC on the NEE-PAR curve, the NEE-PAR curves classified by wet (SWC > 0.1m3m−3) and dry (SWC <= 0.1 m3m−3) conditions are displayed in Figure 12(b). For the purpose of reducing measurement errors [36, 37], the daytime NEE was further subdivided and then averaged based on each PAR level (100 μmolm−2s−1 per PAR bin) under different conditions of SWC. The results show that α increased, but Fmax decreased under dry conditions (Table 5).


### 4.4. The Relationship between Nighttime NEE andTs

The nighttime NEE increased exponentially with increasingTs, and the Q10 during the growing season ranged from 1.16 to 1.98 from 2014 to 2017 with the minimum value of Q10 in 2015. These values were in the normal but lower range of previous studies [36]. The magnitudes of Q10 were significantly lower than those for QTP pasture (3.0, [37]). During dry conditions, the Q10 value was lower than that during wet conditions (Table 6, Figure 13).Table 6
Parameters describing the relationship between nighttime NEE andTs using (3) and (4).
Treatment
SWC
a
b
R
2
Q
10
2014
0.150
0.483
0.054
0.88
1.72
2015
0.076
0.319
0.015
0.50
1.16
2016
0.133
0.323
0.068
0.94
1.98
2017
0.130
0.402
0.062
0.98
1.86
SWC > 0.1
0.374
0.065
0.96
1.92
SWC < 0.1
0.219
0.029
0.57
1.34Figure 13
Relationship between nighttime half-hourly NEE andTs for the growing season from 2014 to 2017 (a), and the whole dataset classified by SWC (b). The nighttime NEE data were averaged using Ts bins of 1°C.
(a)
(b)


### 4.5. The Relationship between Re and GPP with Different EVI

Re was positively correlated with GPP on both daily and monthly scales (Figure14). There was some scatter from the fitted Re-GPP line, as shown in the gray ellipse, indicating that Re was much greater than GPP. This phenomenon mainly occurred in 2015 and June 2016 when EVI was low. The ratio of total Re to total GPP (Re/GPP) during the growing season was 0.83, 1.39, 0.83, and 0.79 for the four consecutive years, respectively. A ratio of Re/GPP less than 1 indicates that NEE was negative. For the growing season of 2014, 2016, and 2017, this semiarid steppe acted as a carbon sink, while it was a carbon source in 2015.Figure 14
Relationship between daily (a) and monthly (b) Re and GPP during the growing season from 2014 to 2017. Color shading represents EVI values.
(a)
(b)


### 4.6. Modeling the Relationships between the Observed Variables

On the daily scale, multiple stepwise linear regression analysis indicated that both ET andE could be modeled by the combination of four environmental variables, namely, SWC, Rn, Ts, and VPD. According to relaimpo analysis, SWC was the most important factor in these four variables followed by Rn (Table 7). On the monthly scale, ET variability is mainly explained by three variables, namely, SWC, Rn, and Ts, while E is explained by SWC. EVI was not significantly related to ET or E. By contrast, the most important variable to explain the variability of T was EVI on both daily and monthly scales. Because T is a stomatal component whereas E is a non-stomatal component, the EVI control on T was greater than that on E.Table 7
Multiple stepwise linear regression analysis showing the optimum model variables, their parameters, and relative importance on daily and monthly timescales for the water (ET,E, and T) and carbon (NEE, Re, and GPP) fluxes.
Flux (y)
Timescale
R
2
Optimum model∗
Relative importance of the parameters (scaled to 1)
ET (mm/d)
Day
0.71
y = −1.963 + 11.409 SWC + 0.004 Rn + 0.094 Ts − 0.542 VPD
SWC = 0.50; Rn = 0.29; Ts = 0.12; VPD = 0.10
Month
0.91
y = −3.657 + 13.285 SWC + 0.095 Ts + 0.007 Rn
SWC = 0.64; Rn = 0.30; Ts = 0.06
E (mm/d)
Day
0.66
y = −1.471 + 8.549 SWC + 0.005 Rn − 0.84 VPD + 0.05 Ts
SWC = 0.47; Rn = 0.32; VPD = 0.17; Ts = 0.05
Month
0.77
y = −0.32 + 11.978 SWC
SWC = 1.00
T (mm/d)
Day
0.51
y = −0.767 + 5.474 EVI + 0.407 VPD
EVI = 0.84; VPD = 0.16
Month
0.70
y = −2.023 + 6.008 EVI + 0.183 WS + 0.049 Ts
EVI = 0.72; Ts = 0.15; WS = 0.13
NEE (gC m−2s−1)
Day
0.50
y = 0.922–8.980 EVI + 2.008 SWC + 0.002 Rn − 0.001 PAR
EVI = 0.85; SWC = 0.07; Rn = 0.05; PAR = 0.03
Month
0.56
y = 1.076 – 6.927 EVI
EVI = 1
Re (gC m−2s−1)
Day
0.72
y = −1.018 + 2.381 EVI + 6.039 SWC + 0.064 Ts
SWC = 0.46; EVI = 0.38; Ts = 0.16
Month
0.54
y = −0.339 + 6.767 EVI
EVI = 1
GPP (gC m−2s−1)
Day
0.76
y = −1.896 + 10.327 EVI + 0.053 Ts + 4.768 SWC − 0.003 Rn + 0.612 PAR
EVI = 0.65; SWC = 0.21; Ts = 0.08; Rn = 0.02; PAR = 0.04
Month
0.79
y = −1.275 + 12.721 EVI
EVI = 1
∗Units: SWC (m3 m−3); Rn (W m−2); Ts (oC); VPD (kPa); EVI (−); PAR (μmolm−2s−1).The multiple linear model indicates that on both daily and monthly scales, the most important predictor of the carbon flux is EVI. Specifically, on the monthly scale, EVI alone explains 56%, 54%, and 79% of the variability of NEE, Re, and GPP, respectively.


## 4.1. Biological Control of ET via Surface Conductance

The surface conductanceGsmms−1 is the total conductance representing the aggregation of plant transpiration, plant interception evaporation, and bare soil evaporation [26]. The relationship between Gs and environmental factors is investigated in this section. Gs was responsive to available water but insensitive to air temperature and radiation (data not shown). Specifically, Gs decreased with increasing VPD (Figure 7(a)). The Gs-VPD relationship is expressed with an exponential model:(8)Gs=34.62exp−1.90VPD,R2=0.51,indicating that when VPD was at a low level (less than about 0.7 kPa), a minor increase of VPD caused a substantial decrease in Gs. However, as VPD continued to increase and exceeded a critical value of ca. 1.2 kPa, the magnitude of Gs almost became insensitive to the variation of VPD and stabilized at a constant small value. Similar results have been reported in other grassland ecosystems over TP (e.g., [18, 20]) and North America (e.g., [27]), forest ecosystems (e.g., [28, 29]), and desert shrub (e.g., [30]). Since the magnitude of SWC was highly negatively correlated with that of VPD (data not shown), Gs was also controlled by SWC, and the Gs-SWC relationship was more significant than the Gs-VPD one (Figure 7(b)):(9)Gs=0.73exp19.98SWC,R2=0.60.Figure 7
Variations ofGs with VPD (a) and SWC (b).
(a)
(b)This relationship was consistent with that found in the low range of SWC in a semiarid steppe over Inner Mongolia [31]. However, an expected capping of Gs as the SWC exceeded a critical value (0.16 m3m−3 reported by [8]) was not observed in this study. Gs is expected to remain constant as the SWC continues to increase. However, a positive linear relationship between Gs and SWC was reported at another alpine meadow on the Qinghai-Tibetan Plateau [26] with SWC ranging between ∼0.05 and 0.35 m3m−3. It is expected that Gs will reach a maximum value and then stabilize. The critical value of SWC above which Gs stabilizes for the QTP region remains to be further investigated.G
s represents the combined effect of VPD, Rn, SWC, and physiological rhythm of plants [23]. The relationship between Gsandα (ET/ ETeq) reflects the interactive effects of SWC and LAI with the photosynthesis capacity [32, 33, 34]. Therefore, although individual factors (e.g., precipitation and SWC) varied considerably throughout the study period, ET/ ETeq was scaled with Gs as follows:(10)ETETeq=0.60−0.52exp−0.24Gs,R2=0.78.In 2015Gs was relatively small and the Gs-α relationship fitted (8) well, while in 2016 and 2017 when Gs was relatively large, the Gs-α plot displays some scatter to the fitted line (Figure 8). The magnitude of α increased with Gs in a low Gs section (less than about 12 mms−1), but when Gs exceeded this threshold, α became less sensitive to Gs. This threshold was consistent with the theoretical value (16 mms−1, [35]) and values from other alpine steppes (e.g., 13 mms−1, [9]; 15 mms−1, [36]).Figure 8
Relationship between the daily average surface conductance (Gs) and the Priestley–Taylor coefficient (α=ET/ETeq).


## 4.2. Environmental and Meteorological Controls of Water Flux

To better characterize the variation of ET and its components, the relationships betweenE and T and environmental (SWC) and meteorological (Ta and VPD) factors were investigated on the daily scale. Rn was not a major factor (data not shown) indicating that the exchange of water fluxes was not energy-limited at Bange, different from that of a humid alpine meadow [21].E andT responded to SWC differently (Figure 9). Precipitation at this site has large seasonal variation, which can influence the recharge of soil water. Furthermore, during some periods of the study, there was a shortage of soil water. When SWC was lower than about 0.1 m3m−3, both E and T increased with SWC. As SWC continued to increase, E also kept increasing, whereas T became less sensitive to SWC and fluctuated around 0.8 mmd−1, indicating that the optimal SWC for plant transpiration was greater than about 0.1 m3m−3.Figure 9
Relationship ofE (a) and T (b) with SWC.
(a)
(b)BecauseT became insensitive to the variation of SWC, when SWC reached a critical value, the relationship between T and meteorological factors was analyzed to better understand the variation of T under wet conditions. Only data with SWC > 0.1 m3m−3 were used for the analysis. Significant linear T-Ta and T-VPD relationship were observed (Figure 10). At high SWC levels, the increase of both Ta and VPD caused an increase of T.Figure 10
Variations of daily meanT against Ta (a) and VPD (b) at conditions of SWC greater than 0.1 m3m−3.
(a)
(b)On the monthly scale, both ET andE were linearly and positively correlated with SWC (Figure 11(a) and 11(b)). SWC alone explains 73% (78%) of the variation in ET (E), indicating that water availability was the main factor controlling the long-term ET and E. At the monthly scale, no single factor explained the variation of T. Multiple factors were closely related to plant growth rather than the environmental or meteorological variables. Such a positive linear relationship between T and EVI is consistent with previous work at other grassland sites (e.g., [27]). However, some scatter exists in the T-EVI fit due to the influence of VPD, which represents the evaporative demand.Figure 11
Relationship of the monthly total ET (a) andE (b) with SWC.
(a)
(b)


## 4.3. The Relationship between Daytime NEE and PAR under Different SWC

The relationship between the daytime half-hourly NEE and PAR was described with the Michaelis–Menten model. The NEE-PAR relationships showed a similar pattern for 2014, 2016, and 2017 (Figure12(a)). For these three years, the NEE reached its minimum at PAR of 1300 μmolm−2s−1. When PAR was less than 1300 μmolm−2s−1, the daytime NEE decreased with the increasing PAR. For PAR greater than 1300 μmolm−2s−1, the daytime NEE increased with PAR. Fmax ranged from −3.97 to −4.65 μmolm−2s−1 (Table 5). By contrast, the relationship was significantly different for 2015 compared with the other three years. During 2015, the PAR critical value reduced to 600 μmolm−2s−1, and Fmax decreased to −0.99 μmolm−2s−1.Figure 12
Relationship between the daytime half-hourly NEE and PAR for the growing season from 2014 to 2017 (a), and the whole dataset classified by SWC (b). The NEE and PAR data were averaged using PAR bins of 100μmolm−2s−1.
(a)
(b)Table 5
The ecosystem photosynthesis parameters that describe the daytime NEE-PAR relationship using the Michaelis–Menten model.
Treatment
SWC
F
max
α
R
e
bulk
R
2
2014
0.150
−4.65
−0.043
2.31
0.89
2015
0.076
−0.99
−0.009
0.63
0.78
2016
0.133
−3.97
−0.021
1.36
0.93
2017
0.130
−4.10
−0.028
1.78
0.92
SWC > 0.1
−3.75
−0.024
1.52
0.90
SWC < 0.1
−1.10
−0.046
1.45
0.72To further investigate the influence of SWC on the NEE-PAR curve, the NEE-PAR curves classified by wet (SWC > 0.1m3m−3) and dry (SWC <= 0.1 m3m−3) conditions are displayed in Figure 12(b). For the purpose of reducing measurement errors [36, 37], the daytime NEE was further subdivided and then averaged based on each PAR level (100 μmolm−2s−1 per PAR bin) under different conditions of SWC. The results show that α increased, but Fmax decreased under dry conditions (Table 5).


## 4.4. The Relationship between Nighttime NEE andTs

The nighttime NEE increased exponentially with increasingTs, and the Q10 during the growing season ranged from 1.16 to 1.98 from 2014 to 2017 with the minimum value of Q10 in 2015. These values were in the normal but lower range of previous studies [36]. The magnitudes of Q10 were significantly lower than those for QTP pasture (3.0, [37]). During dry conditions, the Q10 value was lower than that during wet conditions (Table 6, Figure 13).Table 6
Parameters describing the relationship between nighttime NEE andTs using (3) and (4).
Treatment
SWC
a
b
R
2
Q
10
2014
0.150
0.483
0.054
0.88
1.72
2015
0.076
0.319
0.015
0.50
1.16
2016
0.133
0.323
0.068
0.94
1.98
2017
0.130
0.402
0.062
0.98
1.86
SWC > 0.1
0.374
0.065
0.96
1.92
SWC < 0.1
0.219
0.029
0.57
1.34Figure 13
Relationship between nighttime half-hourly NEE andTs for the growing season from 2014 to 2017 (a), and the whole dataset classified by SWC (b). The nighttime NEE data were averaged using Ts bins of 1°C.
(a)
(b)


## 4.5. The Relationship between Re and GPP with Different EVI

Re was positively correlated with GPP on both daily and monthly scales (Figure14). There was some scatter from the fitted Re-GPP line, as shown in the gray ellipse, indicating that Re was much greater than GPP. This phenomenon mainly occurred in 2015 and June 2016 when EVI was low. The ratio of total Re to total GPP (Re/GPP) during the growing season was 0.83, 1.39, 0.83, and 0.79 for the four consecutive years, respectively. A ratio of Re/GPP less than 1 indicates that NEE was negative. For the growing season of 2014, 2016, and 2017, this semiarid steppe acted as a carbon sink, while it was a carbon source in 2015.Figure 14
Relationship between daily (a) and monthly (b) Re and GPP during the growing season from 2014 to 2017. Color shading represents EVI values.
(a)
(b)


## 4.6. Modeling the Relationships between the Observed Variables

On the daily scale, multiple stepwise linear regression analysis indicated that both ET andE could be modeled by the combination of four environmental variables, namely, SWC, Rn, Ts, and VPD. According to relaimpo analysis, SWC was the most important factor in these four variables followed by Rn (Table 7). On the monthly scale, ET variability is mainly explained by three variables, namely, SWC, Rn, and Ts, while E is explained by SWC. EVI was not significantly related to ET or E. By contrast, the most important variable to explain the variability of T was EVI on both daily and monthly scales. Because T is a stomatal component whereas E is a non-stomatal component, the EVI control on T was greater than that on E.Table 7
Multiple stepwise linear regression analysis showing the optimum model variables, their parameters, and relative importance on daily and monthly timescales for the water (ET,E, and T) and carbon (NEE, Re, and GPP) fluxes.
Flux (y)
Timescale
R
2
Optimum model∗
Relative importance of the parameters (scaled to 1)
ET (mm/d)
Day
0.71
y = −1.963 + 11.409 SWC + 0.004 Rn + 0.094 Ts − 0.542 VPD
SWC = 0.50; Rn = 0.29; Ts = 0.12; VPD = 0.10
Month
0.91
y = −3.657 + 13.285 SWC + 0.095 Ts + 0.007 Rn
SWC = 0.64; Rn = 0.30; Ts = 0.06
E (mm/d)
Day
0.66
y = −1.471 + 8.549 SWC + 0.005 Rn − 0.84 VPD + 0.05 Ts
SWC = 0.47; Rn = 0.32; VPD = 0.17; Ts = 0.05
Month
0.77
y = −0.32 + 11.978 SWC
SWC = 1.00
T (mm/d)
Day
0.51
y = −0.767 + 5.474 EVI + 0.407 VPD
EVI = 0.84; VPD = 0.16
Month
0.70
y = −2.023 + 6.008 EVI + 0.183 WS + 0.049 Ts
EVI = 0.72; Ts = 0.15; WS = 0.13
NEE (gC m−2s−1)
Day
0.50
y = 0.922–8.980 EVI + 2.008 SWC + 0.002 Rn − 0.001 PAR
EVI = 0.85; SWC = 0.07; Rn = 0.05; PAR = 0.03
Month
0.56
y = 1.076 – 6.927 EVI
EVI = 1
Re (gC m−2s−1)
Day
0.72
y = −1.018 + 2.381 EVI + 6.039 SWC + 0.064 Ts
SWC = 0.46; EVI = 0.38; Ts = 0.16
Month
0.54
y = −0.339 + 6.767 EVI
EVI = 1
GPP (gC m−2s−1)
Day
0.76
y = −1.896 + 10.327 EVI + 0.053 Ts + 4.768 SWC − 0.003 Rn + 0.612 PAR
EVI = 0.65; SWC = 0.21; Ts = 0.08; Rn = 0.02; PAR = 0.04
Month
0.79
y = −1.275 + 12.721 EVI
EVI = 1
∗Units: SWC (m3 m−3); Rn (W m−2); Ts (oC); VPD (kPa); EVI (−); PAR (μmolm−2s−1).The multiple linear model indicates that on both daily and monthly scales, the most important predictor of the carbon flux is EVI. Specifically, on the monthly scale, EVI alone explains 56%, 54%, and 79% of the variability of NEE, Re, and GPP, respectively.


## 5. Conclusion

With four consecutive years of eddy covariance observations of water and carbon dioxide fluxes over an alpine steppe on the Tibetan Plateau, the interannual variations of ET, NEE, and their components were investigated. Using an eddy covariance-based technique, ET was partitioned into its two components. Empirically nonlinear and linear models were used to investigate the observed fluxes and variables.A low value of ET/ETeq and SWC indicated a water deficit condition at this site. Drought conditions can reduce the surface conductance and hence the Priestley–Taylor coefficient. T/ET was closely related to the plant growth stage under good water conditions. During drought conditions, the reduction of E was greater than that of T, and thus T/ET could increase.The ecosystem photosynthesis parameters andQ10 for the growing season in the drought year 2015 were clearly different from those in the other three years, when Fmax was much higher and Q10 was much lower. This difference also existed for dry conditions compared with wet conditions. The daily and monthly average Re values were positively and linearly correlated with GPP.With multiple stepwise linear regression, the water and carbon fluxes can be explained by different values and types of variables on different timescales. For ET andE, SWC was the most important predictor, whereas EVI was the main predictor for T. For the carbon dioxide flux, EVI was the most important variable in explaining the variability on different temporal scales, except for Re where SWC was most significant on the daily scale.



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*Source: 7368882-2022-10-06.xml*