Agriculture, Ecosystems and Environment 142 (2011) 318–328
Contents lists available at ScienceDirect
Agriculture, Ecosystems and Environment
journal homepage: www.elsevier.com/locate/agee
Biophysical regulation of net ecosystem carbon dioxide exchange over
a temperate desert steppe in Inner Mongolia, China
Fulin Yang a,c , Guangsheng Zhou b,a,∗ , John E. Hunt d , Feng Zhang a
a
State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun, Xiangshan, Haidian District, Beijing 100093, China
Chinese Academy of Meteorological Sciences, 46 Nandajie, Zhongguancun, Haidian District, Beijing 100081, China
Graduate University of the Chinese Academy of Sciences, 19A Yuquan Road, Shijingshan District, Beijing 100049, China
d
Landcare Research, P.O. Box 40, Gerald Street, Lincoln 7640, New Zealand
b
c
a r t i c l e
i n f o
Article history:
Received 22 February 2011
Received in revised form 30 May 2011
Accepted 30 May 2011
Available online 28 June 2011
Keywords:
Net ecosystem CO2 exchange
Ecosystem respiration
Temperate desert steppe
Inner Mongolia
Drought
a b s t r a c t
Measurements of net ecosystem carbon dioxide (CO2 ) exchange (NEE) were made, using eddy covariance,
to investigate the biophysical regulation of a temperate desert steppe characterized drought in Inner
Mongolia, China during 2008. The half-hourly maximum and minimum NEE were −3.07 and 0.85 ␮mol
CO2 m−2 s−1 (negative values denoting net carbon uptake). The maximum daily NEE was −6.0 g CO2
m−2 day−1 . On an annual basis, integrated NEE was −7.2 g C m−2 y−1 , indicating a weak carbon sink.
The light response curves of NEE showed a rather low apparent quantum yield (˛) and saturation value
of NEE (NEEsat ). Moreover, ˛ and NEEsat varied with canopy development, soil water content (SWC),
air temperature (Ta ), and vapor pressure deficit (VPD). Piecewise regression results suggested that the
optimal SWC, Ta , and VPD for half-hourly daytime NEE were 12.6%, 24.3 ◦ C, and 1.7 kPa, respectively. The
apparent temperature sensitivity of ecosystem respiration was 1.6 for the entire growing season, and it
was significantly controlled by soil moisture. During the growing season, leaf area index explained about
26% of the variation in daily NEE. Overall, NEE was strongly suppressed by water stress and this was the
dominant biophysical regulator in the desert steppe.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Grasslands store approximately 34% of the global stock of
carbon in terrestrial ecosystems (White et al., 2000), and play
an important role in regional and global carbon storage and
cycling. Accurate measurements and predictions of CO2 exchange
between grassland ecosystems and the atmosphere are particularly
important for global carbon cycle research. Micrometeorological measurements of CO2 flux are commonly used to determine
environmental drivers of carbon cycling in grassland ecosystems
(Baldocchi et al., 2001; Yu et al., 2006), with the aim of developing or improving ecosystem models. These models can then
be used to assess the effects of changing climate on land surface processes (Friend et al., 2007). The response of CO2 exchange
to climatic variation has been found to vary on a temporal
scale and across ecosystems. Consequently, uncertainty in these
∗ Corresponding author at: State Key Laboratory of Vegetation and Environmental
Change, Institute of Botany, Chinese Academy of Sciences, 20 Nanxincun, Xiangshan,
Haidian District, Beijing 100093, China. Tel.: +86 10 62836268;
fax: +86 10 82595962.
E-mail address: [email protected] (G. Zhou).
0167-8809/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.agee.2011.05.032
models limits the ability to accurately predict CO2 exchange
(Polley et al., 2010). Therefore, validation and improved ecosystem models are required to understand the regulation of CO2
exchange at multiple temporal scales and by diverse ecosystems.
Net ecosystem CO2 exchange (NEE) is a result of gross ecosystem
production (GEP), i.e. photosynthetic assimilation, and autotrophic
(Ra ) and heterotrophic (Rh ) respirations. Photosynthesis and respirations may respond at different rates to environmental variabilities. Wang and Zhou (2008) suggested that single-factor response
functions of carbon budget components had limited values in arid
and semiarid ecosystems, due to the combination of drought and
high temperatures. Drought can substantially modify the seasonal
development of leaf area and change plant physiology, and therefore impact on both the timing and magnitude of maximal CO2
uptake (Hunt et al., 2002). Canopy development and other biological processes that regulate photosynthesis and respiration, in turn,
are affected by seasonal or annual amounts of precipitation (PPT)
(Polley et al., 2010). Depending on timing and amount of precipitation, more or less carbon uptake can occur in grassland ecosystems.
At the ecosystem level, grasslands can be either net carbon sources
or sinks (Meyers, 2001; Flanagan et al., 2002; Xu and Baldocchi,
2004). Scott et al. (2009) reported severe cool season drought could
lead to the greatest annual net carbon loss. In one dry tussock
F. Yang et al. / Agriculture, Ecosystems and Environment 142 (2011) 318–328
grassland, the annual net CO2 exchange was determined mostly
by the timing and intensity of spring rainfall (Hunt et al., 2004). In
Inner Mongolian typical steppe, the capacity to fix CO2 was closely
related to both timing and frequency of rainfall events (Hao et al.,
2008).
Soil respiration involves a suite of complex processes contributing to CO2 efflux from the surface of soils (Qi et al., 2002),
and is usually expressed empirically as an exponential function of temperature. The temperature sensitivity of respiration
is often expressed by a Q10 value, the factor by which respiration rate increases with every 10 ◦ C increase in temperature.
Q10 is treated commonly as a constant of 2 in many ecosystem models, such as CASA (Potter et al., 1993), TEM (Tian et al.,
1999), and can be used to calculate soil or ecosystem respiration (Reco ) from local to global scales (Cox et al., 2000; Fang and
Moncrieff, 2001). However, recent studies have shown that the
value of Q10 may vary considerably in space and time across
systems (Raich and Schlesinger, 1992; Kirschbaum, 2000; Aires
et al., 2008), and a small change in Q10 in models can cause a
significant bias in the estimate of soil respiration (Xu and Qi,
2001; Reichstein and Beer, 2008). Kutsch and Kappen (1997) compared a Q10 -adjustment model to a fixed model in agricultural
ecosystems and found that the Q10 -fixed model overestimated
soil respiration in the dry season and underestimated soil respiration in the other seasons. Recent studies showed that Q10
varied over the season with changes in soil moisture, temperature, phenology, and carbon inputs, and should be taken
into account in modeling long-term ecosystem respiration (Xu
and Baldocchi, 2004; Yuste et al., 2007). Davidson and Janssens
(2006) suggested the environmental constraints, such as drought,
that can also temporarily or indefinitely affect apparent temperature sensitivities. However, ecosystem models commonly
do not explicitly consider the varying sensitivity of soil respiration rates to temperature and moisture (Qi et al., 2002).
Therefore, accurately quantifying response of Reco to soil temperature (Ts ) and soil water content (SWC), especially drought,
is critical for obtaining a reliable estimate of ecosystem carbon
budget.
The desert steppes are the most arid grassland ecosystem type,
distributing in the region with annual precipitation between 150
and 250 mm and continental climate (Sun, 2005). The 17.5 million
ha of temperate desert steppe in China provided 0.066 Pg of carbon
storage in the biomass (Fan et al., 2008). Inner Mongolian temperate
desert steppe covering 8.8 million ha (Liao and Jia, 1996) is located
in a transitional zone between steppe and desert, and is vulnerable to desertification due to climate change and increased human
activity (Yang and Zhou, 2011). Severe drought conditions have the
most significant effect on plant biomass in Inner Mongolia grasslands (Xiao et al., 1995). Recent studies have shown that the annual
mean temperature has increased, while precipitation in spring and
winter has decreased over the past 40 years (Li et al., 2002). Warming trend may intensify the hydrological cycle and lead to increased
drought severity and duration. Severe drought could lead to a
change in plant community structure, which, in turn, may alter
the water and carbon dioxide cycling processes (Scott et al., 2010).
Understanding how CO2 exchange responds under current climate
variability and drought is thus of critical importance to predict how
the desert steppe ecosystem will respond to future climate change.
Knowledge of the biophysical regulations of CO2 exchange from this
large area of water-limited grassland ecosystem, however, is still
lacking.
Using eddy covariance measurements, the objectives of this
study were to: (1) investigate the biophysical regulations on CO2
fluxes; (2) quantify the magnitude of CO2 exchange; (3) calculate
the carbon balance in 2008 over the Inner Mongolian temperate
desert steppe.
319
2. Material and methods
2.1. Study site
This study was conducted at the Inner Mongolian Temperate Desert Steppe Ecosystem Research Station (44◦ 05 N, 113◦ 34 E,
970 m a.s.l.), located north of the county of Sunitezuoqi, Inner
Mongolia Autonomous Region, China, during 2008. The study
region has an arid–semiarid, temperate continental climate. The
annual mean air temperature is 3.2 ◦ C, with monthly mean temperature ranging from −18.7 ◦ C in January to 22.1 ◦ C in July. The mean
annual precipitation is 184 mm (40-year period from 1965 to 2004,
Sunitezuoqi weather station), with most of precipitation (85%)
occurring between May and September. The plant community is
dominated by the bunch grass Stipa klemenzii and the herb Allium
polyrrhizum. In mid-summer, the grass canopy is 0.20–0.35 m tall.
The study site was fenced in August 2007 to prevent grazing and
other disturbances. The soils are classified as brown calcic with an
average bulk density of 1630 kg m−3 .
2.2. Eddy flux and micrometeorological measurements
An eddy covariance (EC) system was used to measure the fluxes
of energy, water vapor and CO2 . The EC system was installed
at a height of 2.0 m, and included a 3-D sonic anemometer–
thermometer (CSAT-3, Campbell Scientific Inc., Logan, UT, USA) and
an open path infrared gas (CO2 /H2 O) analyzer (LI-7500, LI-COR Inc.,
Lincoln, NE, USA). The raw signals were recorded at 10 Hz by a data
logger (CR5000, Campbell Scientific Inc., Logan, UT, USA).
A meteorological tower was established near the eddy
covariance tower to measure environmental variables. Photosynthetically active radiation (PAR) and net radiation (Rn ) were
measured at 2.4 m above the ground, using a quantum sensor (LI190SB, LI-COR Inc., Lincoln, NE, USA) and a four-component net
radiometer (CNR-1, Kipp & Zonen, Delft, Netherlands), respectively.
Air temperature (Ta ) and relative humidity (RH) were measured
at 2.0 m (HMP45C, Vaisala, Helsinki, Finland). A horizontal wind
speeds sensor (014A, Campbell Scientific Inc., Logan, UT, USA)
was attached at 2.0 m to measure horizontal wind speed (Ws ).
Soil temperature (Ts ) at 0.05 m was measured by a thermistor
(107L, Campbell Scientific Inc., Logan, UT, USA). Soil water contents (SWC) at depths of 0.10, 0.20, 0.30, and 0.40 m were measured
by time domain reflectometry probes (CS616, Campbell Scientific
Inc., Logan, UT, USA). Soil heat flux (G) was measured using two
soil heat plates (HFP01, Hukeflux Inc., Delft, Netherlands) in separate locations at 0.08 m below the soil surface. Precipitation above
the canopy was measured with a tipping bucket rain gauge (Model
52203, RM Young Inc., Traverse City, MI, USA). All meteorological
factors were measured every 2 s, and averaged half-hourly by a data
logger (CR23X, Campbell Scientific Inc., Logan, UT, USA).
2.3. Above ground biomass and leaf area index measurements
Above ground biomass (AGB) was measured five times during
the 2008 growing season (1 May–15 October) by clipping eight
1 m × 1 m quadrats. In each quadrat, all the plants were cut at the
ground surface and oven dried at 65 ◦ C to constant weight and the
dominant species leaf area ratio (m2 g−1 ) was also measured. The
total leaf area index (LAI, m2 m−2 ) was estimated as the product
of total dry leaf biomass (g m−2 ) and leaf area ratio (Wang and
Zhou, 2008). All the above ground biomass dies over winter so we
assumed LAI was set to zero on 1 May (DOY 122) and 15 October
(DOY 289), corresponding to the beginning and end dates of growing season. Measurements of LAI were linearly interpolated to daily
intervals (Li et al., 2005).
320
F. Yang et al. / Agriculture, Ecosystems and Environment 142 (2011) 318–328
2.4. Data processing and energy balance closure
and soil surface (0.08 m), and Cs is the soil heat capacity calculated
from the following equation:
The half-hourly fluxes of latent heat (LE), sensible heat (H), and
net ecosystem CO2 exchange (NEE) were determined following
(Monteith and Unsworth, 1990):
w q
(1)
H = Cp
w T (2)
NEE =
w c LE =
(3)
(kg m−3 )
where is air density
at a given air temperature, the
latent heat of vaporization (J kg−1 ), CP the specific heat capacity of
air at constant pressure (J kg−1 ◦ C−1 ), w , q , T , and c denote fluctuations of vertical wind speed (m s−1 ), specific humidity (kg kg−1 ), air
temperature (◦ C), and CO2 concentration (mg m−3 ), respectively,
and over bars denote averaging over the sampling interval (30 min
in this study). Positive LE and H values indicate energy transfer
from the ecosystem to the atmosphere while negative fluxes signify the reverse. Negative NEE values indicate net carbon gain by
the ecosystem.
Prior to the scalar flux computation, spikes were detected and
removed (Vickers and Mahrt, 1997), and two-dimension coordinate
rotation was performed to reorient the x-axis parallel to the local
main wind direction and to force the mean vertical velocity to zero
(McMillen, 1988). In addition, carbon dioxide fluxes were corrected
for the effect of air density fluctuations (Webb et al., 1980).
Half-hourly flux data were screened to remove any anomalous statistics and sensor malfunction, following the criteria: (1)
incomplete half-hourly measurement (caused by power failure or
analyzer calibration problems); (2) rain events; (3) outliers (Papale
et al., 2006); and (4) a moving point test (MPT) that determined
the appropriate friction velocity threshold (u∗c , 0.11 m s−1 ) for valid
nighttime CO2 fluxes (Gu et al., 2005; Zhu et al., 2006). Negative
CO2 fluxes occurred prevalently in the non-growing season, which
may be due to the instrument LI-7500 surface heating effect (Burba
et al., 2008). Self-heating corrections were taken into consideration
for the non-growing season data (Burba et al., 2008) but could not
be achieved due to the large noise and unrealistic flux values. We
discarded the negative CO2 fluxes in the non-growing season, likely
to offset the non performance of the self-heating correction, which
anyway did not affect the reliability of the EC flux measurement and
computation (Sottocornola and Kiely, 2010; Thomas et al., 2010;
Koehler et al., 2011).
Following the data screening described above, 34% of the CO2
fluxes were discarded, and the remaining 66% of fluxes were
regarded as the ‘available data’ in the growing season. In order to
derive continuous time series of NEE, required for calculating the
annual CO2 balance, the following procedure was employed to fill
the gaps (Falge et al., 2001a,b): (1) linear interpolation was used to
fill the gaps that were less than 2 h by calculating an average of the
values immediately before and after the data gap; (2) other data
gaps were filled using a look-up table; and (3) if these relationships
could not be established, due to missing meteorological data, the
mean daily variations (MDV) were used to fill the gaps.
The net radiation (Rn ) was partitioned into sensible (H), latent
(LE), soil (G) heat fluxes and soil heat storage (Ssoil ) (Chen et al.,
2009):
Rn = H + LE + G + Ssoil
(4)
The soil heat storage is calculated using the equation described
by (Oliphant et al., 2004):
Ssoil = Cs
∂Ts
d
∂t
(5)
where Ts is the average soil temperature (◦ C), t is time (in this
case ∂t = 1800s), d is soil layer depth between the soil heat plates
Cs = b Cd + w Cw
(6)
is the soil bulk density (1630 kg m−3 ), where b
w is the density
of water, Cd and Cw are the specific heat capacities of the dry mineral
soil (Cd = 890 J kg−1 ◦ C−1 ) and the soil water (Cw = 4190 J kg−1 ◦ C−1 )
and is the volumetric soil water content (m3 m−3 ).
Energy balance ratio (EBR), based on the daily values, was used
to assess the performance of the EC system. The energy balance
ratio (EBR) was calculated using Eq. (7):
EBR =
(LE + H)
(Rn − G − Ssoil )
(7)
EBR was 0.87 for the whole observation period, falling in the
median regions of the reported energy closure, which range from
0.55 to 0.99 for FLUXNET (Wilson et al., 2002).
2.5. Data analysis
The relationship between daytime (incident solar radiation more than 20 W m−2 ) NEE (␮mol CO2 m−2 s−1 ) and PAR
(␮mol photons m−2 s−1 ) was assessed using a Michaelis–Menten
rectangular hyperbola (Eq. (8)) fitted to the half-hourly daytime
‘available data’ (Falge et al., 2001b):
NEEdaytime =
˛NEEsat PAR
+ Reco
˛PAR + NEEsat
(8)
where ˛ is the apparent quantum yield or the initial slope of the
light response curve (␮mol CO2 ␮mol−1 photons) and NEEsat is the
value of NEE at a saturating light level and Reco is a bulk estimate
of ecosystem respiration.
The relationship between nighttime (incident solar radiation
less than 20 W m−2 ) NEE, or Reco (␮mol CO2 m−2 s−1 ), and soil temperature at 5 cm depth (Ts , ◦ C) was calculated by Van’t Hoff equation
(Eq. (9)) fitted to the half-hourly nighttime ‘available data’ (Zhao
et al., 2006; Aires et al., 2008):
NEEnighttime = a exp (bTs )
(9)
where a and b are the regression parameters. The temperature
sensitivity coefficient (Q10 ) of Reco was determined by the following
equation (Eq. (10)):
Q10 = exp(10b)
(10)
3. Results
3.1. Environmental factors
Mean monthly Ta during 2008 was similar to the long-term
average with July being the warmest month. Monthly PPT showed
large seasonal variation, with the most rain falling in June (48 mm),
followed by August (Fig. 1). During the growing season, total PPT
(133 mm) was lower than the long-term mean (162 mm), and July
had only 49% of the long-term rainfall.
The patterns of daily PPT (mm), soil water content (SWC, %),
Ta (◦ C), vapor pressure deficit (VPD, kPa), and LAI (m2 m−2 ) during
2008 are shown in Fig. 2. SWC at 0.10 m was averaged 5.8% and
ranged from 3.1% to 17.5%, and closely followed the precipitation
pattern. The SWC at 0.10 m was below 8% for 309 days, with 111
days occurring in the growing season (168 days), suggesting severe
drought in this desert steppe. SWC at 0.30 m was less responsive
and generally lagged PPT. The study region was characterized by
strong day-to-day variation in Ta , with the maximum daily Ta of
31.5 ◦ C (DOY 217) and the minimum of −28.7 ◦ C (DOY 22). Daily
VPD and Ts also varied dramatically across the growing season.
Treatment
DOY
LAI (m2 m−2 )
SWC (%)
Ta (◦ C)
VPD (kPa)
˛ (␮mol CO2 ␮mol−1 photons)
NEEsat (␮mol CO2 m−2 s−1 )
Reco (␮mol CO2 m−2 s−1 )
n
R2
P-value
Early growing stage
Rapid growing stage
Peak growing stage
Senescence
Entire growing season
SWC ≤ 5%
5% < SWC ≤ 10%
SWC > 10%
Ta ≤ 10 ◦ C
10 ◦ C < Ta ≤ 20 ◦ C
Ta > 20 ◦ C
VPD ≤ 1 kPa
1 kPa < VPD ≤ 2 kPa
VPD > 2 kPa
122–172
173–223
224–254
255–289
122–289
0.02 ± 0.00
0.16 ± 0.01
0.33 ± 0.01
0.15 ± 0.02
0.15 ± 0.01
5.5 ± 0.0
8.0 ± 0.1
11.7 ± 0.1
6.7 ± 0.1
7.6 ± 0.1
17.3 ± 0.2
26.2 ± 0.2
20.3 ± 0.2
14.6 ± 0.3
20.1 ± 0.1
1.61 ± 0.03
2.46 ± 0.04
1.54 ± 0.03
1.27 ± 0.03
1.80 ± 0.02
−0.0024 ± 0.0007
−0.0107 ± 0.0042
−0.0124 ± 0.0030
–
−0.0040 ± 0.0012
−0.0014 ± 0.0010
−0.0055 ± 0.0022
−0.0133 ± 0.0032
–
−0.0028 ± 0.0013
−0.0062 ± 0.0018
–
−0.0026 ± 0.0011
−0.0069 ± 0.0024
−1.94 ± 0.19
−2.12 ± 0.20
−4.90 ± 0.23
–
−2.47 ± 0.19
−1.50 ± 0.48
−1.68 ± 0.13
−4.86 ± 0.22
–
−3.34 ± 0.87
−2.40 ± 0.13
–
−3.35 ± 0.86
−2.05 ± 0.14
0.26 ± 0.10
0.79 ± 0.24
0.95 ± 0.29
–
0.34 ± 0.15
0.08 ± 0.16
0.42 ± 0.17
1.13 ± 0.29
–
0.20 ± 0.24
0.54 ± 0.16
–
0.15 ± 0.21
0.59 ± 0.18
32
32
32
–
32
32
32
32
–
32
32
–
32
32
0.906
0.893
0.944
–
0.902
0.627
0.876
0.946
–
0.770
0.925
–
0.806
0.903
<0.0001
<0.0001
<0.0001
–
<0.0001
<0.0001
<0.0001
<0.0001
–
<0.0001
<0.0001
–
<0.0001
<0.0001
F. Yang et al. / Agriculture, Ecosystems and Environment 142 (2011) 318–328
Fig. 1. Monthly precipitation (PPT) and monthly mean air temperature (Ta ) from
May to October in 2008 and the long-term average (1965–2004).
LAI began to increase in early May and reached the maximum of
0.38 m2 m−2 in early September, and then decreased sharply in the
middle of October, as a consequence of senescence (Fig. 2c). The
2008 growing season (DOY 122–289) could be divided into four
phenological stages as described in Table 1: early growing stage (I,
DOY 122–172); rapid growing stage (II, DOY 173–223); peak growing stage (III, DOY 224–254); and senescence (IV, DOY 255–289).
Fig. 2. Temporal variations of (a) daily precipitation (PPT) and daily mean soil water
content (SWC) at depths of 0.10, 0.20, 0.30, and 0.40 m, (b) daily air temperature (Ta )
and vapor pressure deficit (VPD), and (c) leaf area index (LAI) during 2008, Inner
Mongolian temperate desert steppe.
Table 1
Mean environmental values and the parameters used to describe the rectangular hyperbolic response of daytime net ecosystem CO2 exchange (NEE) to photosynthetically active radiation (PAR) as described in Eq. (8).
LAI, leaf area index; SWC, soil water content at the depth of 0.10 m; Ta , air temperature; VPD, vapor pressure deficit; ˛, the apparent quantum yield; NEEsat , the saturation value of NEE at an infinite light level; Reco , the model-derived
bulk ecosystem respiration; n, the number of samples.
321
322
F. Yang et al. / Agriculture, Ecosystems and Environment 142 (2011) 318–328
Fig. 3. Light response curves for (a) the entire growing season, and (b) different
growing stages. The daytime net ecosystem CO2 exchange (NEE) data were averaged
with photosynthetically active radiation (PAR) bins of 50 ␮mol m−2 s−1 . Bars indicate
±S.E. Eq. (8) was used to fit the data when PAR was below 1600 ␮mol m−2 s−1 , and
the regression coefficients are presented in Table 1.
3.2. Daytime NEE in response to PAR
A Michaelis–Menten model (Eq. (8)), based on half-hourly data,
was used to describe the relationship between daytime NEE and
PAR during the growing season (Fig. 3). Data of NEE were averaged using PAR bins of 50 ␮mol m−2 s−1 . NEE increased with PAR
until it reached 1600 ␮mol m−2 s−1 and then quickly declined
(Fig. 3). Thus, Eq. (8) was only used when PAR was less than
1600 ␮mol m−2 s−1 . In general, the rectangular hyperbolic function
produced a good fit to the data, except the senescence stage. Regression coefficients (R2 ) indicated that changes in PAR accounted for
more than 89% of the variations in half-hourly NEE (Table 1). The
main biophysical factors (LAI, SWC, Ta , and VPD) and regression
coefficients (˛, NEEsat , and Reco ) of light response curves at different
growing stages are summarized in Table 1.
The NEEsat and ˛ values for the entire growing season were
−2.5 ␮mol CO2 m−2 s−1 and −0.0040 ␮mol CO2 ␮mol−1 photons,
respectively. In terms of the different growing stages, photosynthetic parameters (NEEsat and ˛) increased with LAI. The maximum
NEEsat was −4.9 ␮mol CO2 m−2 s−1 occurring at the peak growing stage with maximum LAI. The ˛ values ranged from −0.0024
to −0.0124 ␮mol CO2 ␮mol−1 photons at the early three growing
stages.
In order to further investigate the influences of SWC, Ta , and VPD
on the response of daytime NEE to PAR, NEE data were separated
into three SWC classes (low SWC ≤ 5%, medium 5% < SWC ≤ 10%,
and high SWC > 10%), three Ta classes (low Ta ≤ 10 ◦ C, medium
10 ◦ C < Ta ≤ 20 ◦ C, and high Ta > 20 ◦ C), and three VPD classes (low
VPD ≤ 1 kPa, medium 1 kPa < VPD ≤ 2 kPa, and high VPD > 2 kPa).
The NEE data were further subdivided by PAR into 50 ␮mol m−2 s−1
increments, and bin-averaged for each PAR sub-group (Fig. 4 and
Table 1). Low SWC reduced NEEsat to a third compared to well
Fig. 4. Light response curves under (a) different soil water contents (SWC), (b) different air temperatures (Ta ), and (c) different vapor pressure deficits (VPD). The net
ecosystem CO2 exchange (NEE) data were averaged using photosynthetically active
radiation (PAR) bins of 50 ␮mol m−2 s−1 . Bars indicate ±S.E. Eq. (8) was used to fit
the data when PAR was below 1600 ␮mol m−2 s−1 , and the regression coefficients
are shown in Table 1.
watered conditions and ˛ decreased with decreasing SWC. When
Ta was between 10 and 20 ◦ C, NEEsat was higher than during higher
temperatures, and ˛ decreased.
3.3. Daytime NEE in response to SWC, Ta , and VPD
Daytime NEE data were averaged by the environmental parameters into bins, 1% for SWC, 1 ◦ C for Ta , and 0.5 kPa for VPD over
all PAR values. A quadratic function described the effects of SWC,
Ta , and VPD on daytime NEE with a local maxima for each variable
(Fig. 5). A piecewise regression model was used to explore the possibility of an optimal NEE from these three variables. Regression
results showed that the optimal values for the maximum NEE were
of 12.6% (P < 0.001) for SWC, 24.3 ◦ C (P < 0.001) for Ta , and 1.7 kPa
(P < 0.001) for VPD.
3.4. Reco in response to Ts and SWC
For the growing season, NEE data were bin-averaged using Ts
(at 5 cm) bins of 1 ◦ C. While Ts was less than 30 ◦ C, NEE increased
exponentially with an increase in Ts (Fig. 6). At temperatures greater
F. Yang et al. / Agriculture, Ecosystems and Environment 142 (2011) 318–328
323
Table 2
Regression coefficients as described in Eqs. (9) and (10).
Treatment
DOY
SWC (%)
Early growing stage
Rapid growing stage
Peak growing stage
Senescence
Entire growing season
SWC ≤ 5%
5% < SWC ≤ 10%
SWC > 10%
122–172
173–223
224–254
255–289
122–289
5.3
7.3
11.3
6.9
7.4
±
±
±
±
±
0.0
0.1
0.1
0.1
0.1
a
b
R2
Q10
0.0053
0.0256
0.0207
0.0126
0.0089
0.0075
0.0062
0.0186
0.0485
0.0030
0.0258
0.0168
0.0441
0.0315
0.0528
0.0366
0.703
0.007
0.352
0.065
0.785
0.252
0.785
0.694
1.624
1.031
1.294
1.183
1.555
1.370
1.696
1.441
SWC, soil water content at the depth of 0.10 m; Q10 , the temperature sensitivity coefficient of ecosystem respiration.
than 30 ◦ C, the relationship broke down and NEE was severely suppressed. The apparent temperature sensitivity (Q10 ) of Reco was
estimated to be 1.6 for the entire growing season (Fig. 6a and
Table 2), and 1.6, 1.0, 1.3, and 1.2 for early, rapid, peak, and senescence growing stages (Fig. 6b and Table 2), respectively.
Fig. 5. Responses of daytime NEE to (a) soil water content at 0.10 m depth (SWC), (b)
air temperate (Ta ), and (c) vapor pressure deficit (VPD). The daytime NEE data were
averaged with a bin width of 1% for SWC, 1 ◦ C for Ta , and 0.5 kPa for VPD, respectively.
Bars indicate ±S.E. Solid lines were fitted by quadratic function fashion; and dotted
lines were fitted by a piecewise regression model.
In order to further examine the effect of SWC on the relationship
between Ts and Reco , the nighttime NEE data were separated into
three SWC classes (low SWC ≤ 5%, medium 5% < SWC ≤ 10%, and
high SWC > 10%). Then the nighttime NEE data was bin-averaged
using 1 ◦ C bins of Ts (Fig. 6c and Table 2). A decrease in the goodness of fit coefficient (R2 from 0.785 to 0.252) with decreasing SWC
indicates that Reco became decoupled from Ts . Q10 was the highest
Fig. 6. Response of nighttime net ecosystem CO2 exchange (NEE) to change in soil
temperature (Ts ) for the (a) entire growing season; (b) different growing stages
(early, rapid, peak, and senescence), and (c) different soil water contents (SWC). The
NEE data were averaged with Ts bins of 1 ◦ C. Bars indicate ±S.E. Eq. (9) was used to
fit the data when Ts was below 30 ◦ C, and the regression coefficients are presented
in Table 2.
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F. Yang et al. / Agriculture, Ecosystems and Environment 142 (2011) 318–328
Fig. 8. The relationship between daily net ecosystem CO2 exchange (NEE) and leaf
area index (LAI). 7 days NEE or soil water content at 0.10 m (SWC) around each LAI
measurement were averaged. Bars indicate ±S.E.
Fig. 7. (a) Relationship between nighttime net ecosystem CO2 exchange (NEE) and
soil water content at 0.10 m (SWC). The NEE data were averaged with SWC bins of 1%.
Bars indicate ±S.E. The solid line was fitted using a quadratic function and the dotted
line was fitted by a piecewise regression model. The threshold of SWC was 15%. (b)
Covariation of SWC with soil temperature (Ts ) in the half-hourly resolution. The SWC
data were averaged with Ts bins of 1 ◦ C. Bars indicate ±S.E. SWC was response to Ts
in quadratic function.
(1.7) at the medium SWC, while Q10 at low SWC was similar to that
at high SWC.
There was a quadratic relationship between nighttime Reco and
SWC with the maximum Reco occurring at the intermediate water
contents (Fig. 7a). Piecewise regression showed that the threshold
SWC value for Reco was 15%.
3.5. Daily NEE in response to LAI
During the peak growing season, when the conditions were the
most favorable for rapid growth, including moderate soil moisture
and a high LAI, daily-averaged NEE reached −2.8 g CO2 m−2 day−1
(Table 3). For the study period, 7 days NEE around the each LAI
measurement were averaged, SWC as well, to investigate the relationship between daily NEE and LAI. The result showed that NEE
responded linearly to an increase in LAI (Fig. 8). However, LAI only
accounted for about 26% of the variance in NEE (Eq. (11)).
NEE = −5.56LAI − 0.12,
n = 5, R2 = 0.26
(11)
Fig. 9. Average diurnal cycles of (a) net ecosystem CO2 exchange (NEE), and (b)
vapor pressure deficit (VPD) at different growing stages. Bars indicate ±S.E. Time of
day is Beijing Standard Time (BST).
3.6. Diurnal and seasonal variation in NEE
The diel amplitude of carbon fluxes varied substantially within
the different growing stages (Fig. 9). During the rapid and peak
growing stages, the diurnal cycle of NEE is often asymmetric with
substantially greater net uptake in the morning than in the afternoon (Fig. 9). The half-hourly maximum and minimum NEE were
−3.07 and 0.85 ␮mol CO2 m−2 s−1 , occurring in the growing stage.
Table 3
Daily net ecosystem CO2 exchange (NEE) and major environmental factors at different growing stages.
Treatment
DOY
NEE (g CO2 m−2 day−1 )
SWC (%)
Early growing stage
Rapid growing stage
Peak growing stage
Senescence
Entire growing season
122–172
173–223
224–254
255–289
122–289
−0.79
−0.61
−2.75
−0.40
−1.02
5.4
7.8
11.6
6.5
7.5
±
±
±
±
±
0.20
0.19
0.30
0.25
0.13
±
±
±
±
±
0.2
0.4
0.5
0.4
0.3
Ta (◦ C)
14.6
23.3
17.2
10.4
16.8
±
±
±
±
±
VPD (kPa)
0.8
0.6
0.5
0.8
0.5
1.24
1.84
1.05
0.85
1.31
±
±
±
±
±
0.08
0.12
0.06
0.05
0.05
Ts (◦ C)
18.6
27.1
21.5
14.0
20.8
±
±
±
±
±
0.7
0.5
0.6
0.7
0.5
LAI (m2 m−2 )
PPT (mm)
0.02
0.16
0.33
0.15
0.15
31.3
69.8
28.4
3.5
133.0
SWC, soil water content at the depth of 0.10 m; Ta , air temperature; VPD, vapor pressure deficit; Ts , soil temperature; LAI, leaf area index; PPT, precipitation.
F. Yang et al. / Agriculture, Ecosystems and Environment 142 (2011) 318–328
Fig. 10. (a) Seasonal patterns of daily net ecosystem CO2 exchange (NEE, g CO2
m−2 day−1 ) in 2008 and 5-day average of NEE (g CO2 m−2 day−1 ) over the growing season. (b) Cumulative NEE over the course of 2008. Negative NEE denotes net
carbon uptake by the ecosystem.
Fig. 10a shows the pattern of daily integrated NEE in 2008.
In terms of the growing seasonal patterns of 5-day average NEE,
the ecosystem switched from a net carbon sink to a source and
then back to a sink four times (Fig. 10a). The alternating carbon
sink/source pattern was highly linked with precipitation events.
Each event resulted in an increase in SWC, e.g. more PPT for the
third (DOY 184–204) and the fourth (DOY 224–269) periods of net
carbon sink; and less PPT (drought) for the third (DOY 209–219)
and the fourth (DOY 274–289) periods of net carbon source. Daily
NEE for the whole growing season averaged −1.0 g CO2 m−2 day−1 ,
varying from −6.0 (DOY 240) to 3.7 (DOY 164) g CO2 m−2 day−1 .
The desert steppe ecosystem gained carbon 46.4 g C m−2 from the
atmosphere during the 2008 growing season, and returned carbon
39.2 g C m−2 to atmosphere in the non-growing season. Integration
of NEE over the measurement year was −7.2 g C m−2 (Fig. 10b), suggesting the Inner Mongolian temperate desert steppe was a weak
carbon sink.
4. Discussion
325
the relatively low LAI (Ehleringer and Pearcy, 1983) and availability
of soil nutrients (Ruimy et al., 1995).
Drought depressing photosynthetic parameters (˛ and NEEsat )
were found in the Inner Mongolia desert steppe, which was consistent with the results from other semiarid grasslands (Li et al.,
2005; Zhang et al., 2007). For some combinations of environmental
factors, e.g. low Ta and low VPD, Michaelis–Menten model was not
appropriate to describe the relation between NEE and PAR (Fig. 4b
and c). This lack of response may be due to the inactivation or low
activity of enzymes related to photosynthesis under low temperature (Potvin et al., 1986). The fitting failure of Michaelis–Menten
model during senescence may be due to the large decrease in the
chlorophyll content of leaves, accompanying the gradual inactivation in photosynthesis during senescence (Kurahotta et al., 1987).
This phenomenon also occurs in an annual grassland in California
(Xu and Baldocchi, 2004).
The optimal temperature (24.3 ◦ C) for the maximum NEE
reported was close to the value for a Mongolian typical steppe (Li
et al., 2005), and higher than an Inner Mongolian steppe (Fu et al.,
2006b). Temperature has a major influence on the rate of photosynthesis in all plants and is often linked to the temperature sensitivity
of Rubisco (Berry and Bjorkman, 1980). The decrease in NEE at low
temperature is likely due to the slow rate of plant growth in the
early and late growing seasons, whereas the decrease in NEE at
high temperature might be due to the simultaneous low SWC and
high VPD. Maximum NEE occurred at a VPD of 1.7 kPa, similar to
that for a Mongolian typical steppe value (Li et al., 2005). High VPD
affects the hydraulic status of plants and leaves, leading to stomatal
closure (Farquhar et al., 1980; Turner et al., 1985).
Drought is a common limiting factor of vegetation growth and
ecosystem carbon uptake in semiarid grasslands (Fu et al., 2006b).
Maximum NEE occurred when SWC was around 12%, similar to
other studies (Fu et al., 2006b). However, these conditions (daily
SWC higher than 12%) only occurred after precipitation events
and occurred 14% of the time. Short-term drought causes stomatal closure and reduced CO2 assimilation rates (Souza et al., 2004).
Stomatal closure affects the leaf energy balance, increases leaf temperature leading to an increase in photorespiration and reducing
carbon gain (Baldocchi, 1997; Reichstein et al., 2002).
Environmental variables, such as SWC, Ta , VPD, are integrated
to regulate NEE generally. It is difficult to identify the independent effect on NEE, especially between Ta and VPD. Increasing Ta
usually accompanies increasing VPD; however their mechanisms
of regulating NEE are different. High VPD may decrease CO2 input
(photosynthesis) through stomatal closure (Lambers et al., 2008),
whereas high Ta could cause increasing respiration (Lloyd and
Taylor, 1994). SWC may also affect the relationship between NEE
and Ta . Li et al. (2005) reported that the optimal Ta for NEE under
no water-stressed conditions was about 9 ◦ C higher than that under
water-stressed in Mongolia semiarid grassland.
4.1. Effects of abiotic variables on daytime NEE
The maximum NEEsat (−4.9 ␮mol CO2 m−2 s−1 ) at the peak
growing stage was close to the maximum reported for another
Inner Mongolian typical steppe (−5.8 ␮mol CO2 m−2 s−1 ) (Zhang
et al., 2007), but lower than other grassland ecosystems (from −9.6
to −40.2 ␮mol CO2 m−2 s−1 ) (Xu and Baldocchi, 2004; Li et al.,
2005; Aires et al., 2008; Wang and Zhou, 2008). The low ˛ at the
early growing stage was probably due to a combination of immature leaves and low temperature. The ˛ value of −0.0040 ␮mol
CO2 ␮mol−1 photons for the entire growing period was similar to
that of the Mongolian steppe (−0.0047 ␮mol CO2 ␮mol−1 photons)
(Li et al., 2005), however, considerably lower than that reported
for other grassland ecosystems (from −0.008 to −0.465 ␮mol CO2
␮mol−1 photons) (Li et al., 2005), suggesting the low light-use efficiency for the desert steppe (Larcher, 1995). This may be a result of
4.2. Effects of Ts and SWC on Reco
Reco depends on a number of variables including LAI, root
biomass and soil microbial activity, however soil temperature and
moisture are often acknowledged as the major environmental driving variables in respiration models. Q10 (1.6) for the entire growing
season were low compared to the mean value (2.4) reported in a
literature review of soil respiration studies (Raich and Schlesinger,
1992), however, it is close to the values of 1.5 reported by Peng
et al. (2009) for China grasslands and 1.4 reported by Mahecha et al.
(2010) for global convergence at the ecosystem level. The temperature sensitivity in grasslands is highly variable and can range from
1.2 to 3.4 (Xu and Baldocchi, 2004; Flanagan and Johnson, 2005; Fu
et al., 2006b; Zhang et al., 2007; Aires et al., 2008; Wang and Zhou,
2008). It’s very interesting to analyze the extent to which equifi-
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F. Yang et al. / Agriculture, Ecosystems and Environment 142 (2011) 318–328
nality in Q10 values can influence the grassland ecosystem carbon
flux in a widely used terrestrial ecosystem model in the future
study.
Q10 showed higher value under medium SWC, compared to low
or high SWC condition, which was consistent with result reported
by Wang and Zhou (2008). However this was in complete contrast
to the response in a Canada mixed grassland (Flanagan and Johnson,
2005). The discrepancy may be a consequence of the effect of the
finer-textured soil at the Canadian sites (Xu and Qi, 2001). During
high SWC conditions, the water can impede O2 diffusion, thereby
reducing rates of decomposition and microbial production of CO2 .
In this case, CO2 efflux would be less responsive to temperature
(i.e. have a lower Q10 value) (Davidson et al., 1998). Under dry conditions, the main portion of Reco comes from the more recalcitrant
carbon material, which has a low Q10 (Reichstein et al., 2002; Xu
and Baldocchi, 2004). However, this does not explain the high Q10
value (1.6) at the early growing season with low SWC (5%). In this
ecosystem Q10 depends not only on soil water conditions but also
on phenological stage of plant growth (e.g. canopy development
and/or underground root density distribution) and ecosystem models that use a constant Q10 value to calculate Reco , such as CASA
(Potter et al., 1993), TEM (Tian et al., 1999), and Biome-BGC (Hunt
et al., 1996), should reconsider the single variable approach.
Hussain et al. (2011) suggested that the grassland Reco was
strongly limited by soil moisture availability in Germany. In Inner
Mongolia desert steppe Reco and SWC were positively correlated
when SWC was less than 15%, and negatively correlated when
SWC was more than 15%. The quadratic relationship between Reco
and SWC was different from the linear relationship reported for
other grassland ecosystems (Fu et al., 2006b; Xu and Wan, 2008).
When Ts was warmer than 30 ◦ C, and SWC was less than 7% then
Ts and SWC were negatively correlated (Fig. 7b). However, Reco
and SWC were positively correlated when SWC was less than 7%
(i.e. less than the threshold SWC of 15%) (Fig. 7a). Consequently,
Reco and Ts were negatively correlated rather than positively correlated. Therefore, when Ts is greater than 30 ◦ C, drought stress
(SWC < 7%) may be the main factor leading to increased variability in NEE data (Fig. 6). It could be inferred from Fig. 6c and Fig. 7
that drought stress reduced Reco through restricting Reco directly
and the temperature dependence indirectly. These results have
important potential implications for understanding the feedback
of higher temperatures and increased drought conditions under
future climate change scenarios.
4.3. Effects of LAI on daily NEE
LAI could only explain 26% of the observed variance in NEE. The
low explanation for the variance of NEE was similar to the Mongolia
typical steppe (Li et al., 2005). However, the strong correlation
between LAI and NEE in grasslands has also been demonstrated by
others (Flanagan et al., 2002; Aires et al., 2008). Leaf area determines the amount of available photosynthetic material and the
amount of light intercepted by the vegetation (Goldstein et al.,
2000), and canopy development is an important biological process regulating CO2 flux (Polley et al., 2010). However, severe water
stress in the Inner Mongolian temperate desert steppe resulted in
NEE being lower than expected from a direct linear response of NEE
to LAI (Fig. 8).
4.4. Diurnal and seasonal variation in NEE
Asymmetrical distribution of NEE around noon could be caused
by higher respiration in the afternoon due to higher air and soil
temperatures or by a limitation of photosynthesis due to stomatal
closure in response to high VPD (Lasslop et al., 2010). The midday depression of photosynthesis is a common phenomenon for
many plants in semiarid areas, due to inadequate soil moisture coupled with high VPD resulting in stomatal closure (Li et al., 2005;
Fu et al., 2006a; Zhang et al., 2007). The maximum half-hourly
NEE (−3.07 ␮mol CO2 m−2 s−1 ) was an order of magnitude lower
than that of North American native prairie tall grassland (−25.0 to
−31.8 ␮mol CO2 m−2 s−1 ) that are dominated by drought tolerant
C4 plant species (Ham and Knapp, 1998; Dugas et al., 1999; Suyker
and Verma, 2001), the southern plains prairie in USA (from −9.1
to −15.5 ␮mol CO2 m−2 s−1 ) (Sims and Bradford, 2001), the Sahelian savanna in West Africa (from −5 to −10 ␮mol CO2 m−2 s−1 )
(Verhoef et al., 1996), the dry, short tussock grassland in New
Zealand (−4.9 ␮mol CO2 m−2 s−1 ) (Hunt et al., 2002), and the semiarid cold desert grassland in USA (−4 ␮mol CO2 m−2 s−1 ) (Bowling
et al., 2010). However, maximum NEE was similar to Mongolia typical steppe (−3.6 ␮mol CO2 m−2 s−1 ) (Li et al., 2005), and Inner
Mongolian typical steppe during the dry season (−3 ␮mol CO2
m−2 s−1 ) (Wang and Zhou, 2008). The maximum nighttime NEE
(Reco ) of the desert steppe was 0.85 ␮mol CO2 m−2 s−1 for the peak
growing stage and was considerably lower than other grasslands
(from 1.7 to 7.0 ␮mol CO2 m−2 s−1 ) found in the literature (Valentini
et al., 1995; Meyers, 2001; Hunt et al., 2002; Zhang et al., 2007), but
again approached to the Mongolian typical steppe (1.2 ␮mol CO2
m−2 s−1 ) (Li et al., 2005).
The maximum daily NEE (−6.0 g CO2 m−2 day−1 ) was lower
than other grassland ecosystems’ values (from −7.0 to −34.1 g CO2
m−2 day−1 ) reported by Li et al. (2005). For annual NEE of grassland
ecosystems, the desert steppe in the observed year had relative
low net carbon uptake, compared with other carbon sink reports of
grassland ecosystem (−21 to −274 g C m−2 y−1 ) (Dugas et al., 1999;
Frank and Dugas, 2001; Suyker and Verma, 2001; Flanagan et al.,
2002; Frank, 2002; Suyker et al., 2003; Hunt et al., 2004; Kato et al.,
2004; Li et al., 2005; Gilmanov et al., 2007; Nagy et al., 2007; Aires
et al., 2008; Fu et al., 2009). Drought climate and corresponding
drought-adapted vegetation characteristics, such as low LAI, might
be the main environmental constraints for the carbon sink strength
in the desert steppe.
5. Conclusions
Net ecosystem CO2 exchange (NEE) over the temperate desert
steppe in Inner Mongolia was investigated, using eddy covariance technique in 2008. We found that seasonal carbon dynamics
were correlated with changes in SWC and also there was a strong
biophysical regulation of NEE during the growing season. The halfhourly maximum and minimum NEE were extremely low, with
maximum daily NEE of just −6.0 g CO2 m−2 day−1 . Integration of
NEE over measured year (−7.2 g C m−2 y−1 ) show that the Inner
Mongolian temperate desert steppe was a weak carbon sink. Daytime NEE and nighttime NEE (Reco ) were strongly related to PAR and
Ts , respectively. However, drought significantly depressed plant
photosynthesis and ecosystem respiration, causing both photosynthetic parameters (˛ and NEEsat ) and Q10 to decline with increasing
drought. Reco and Ts were well coupled in the absence of water
stress, but gradually decoupled as drought. Daily NEE was correlated with LAI, however, the linear relationship was suppressed
severely by drought. This study showed that drought can have a
major influence on carbon balance and must be taken into account
when modeling NEE of the Inner Mongolian temperate desert
steppe.
Acknowledgments
This research was jointly supported by grants from the State Key
Development Program of Basic Research of China (2010CB951303)
and National Natural Science Foundation of China (90711001,
F. Yang et al. / Agriculture, Ecosystems and Environment 142 (2011) 318–328
40971123). We would like to appreciate Drs. Fengyu Wang, Fang
Bao, and Xiaoyan Ping for their contributions during the experiment, and Drs. Li Zhou and Yu Wang for their help in data
processing. We also gratefully acknowledge the editor and two
anonymous reviewers for their valuable comments and constructive suggestions.
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