Journal of Arid Environments 74 (2010) 1616e1623
Contents lists available at ScienceDirect
Journal of Arid Environments
journal homepage: www.elsevier.com/locate/jaridenv
Partitioning of evapotranspiration and its relation to carbon dioxide fluxes in
Inner Mongolia steppe
X. Huang a, Y. Hao a, *, Y. Wang a, Y. Wang b, X. Cui b, X. Mo c, X. Zhou b
a
College of Life Sciences, Graduate University of the Chinese Academy of Sciences, YuQuan Road 19, Beijing 100049, China
College of Resources and Environment, Graduate University of the Chinese Academy of Sciences, YuQuan Road 19, Beijing 100049, China
c
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
b
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 13 July 2009
Received in revised form
13 June 2010
Accepted 27 July 2010
Available online 23 August 2010
A process-based model (VIP model) was used to partition evapotranspiration (ET) into transpiration (T)
and evaporation (E). Gross ecosystem productivity (GEP) and ecosystem respiration (Re) were calculated
based on CO2 fluxes measured by eddy covariance in a typical steppe. The results revealed that the water
and CO2 fluxes were low before the growth of vegetation in the spring. During the growth period, plant
transpiration was found to account for 33e74% of the total ET. Additionally, the variations in daily net
ecosystem exchange (NEE) and GEP were found to be correlated with precipitation and T, but not ET
during the study periods. The peak responses of Re to rain events lagged by 1e2 days when compared to
the evaporation peak. The leaf area index (LAI) primarily regulated the changes in water use efficiency
(WUE). Taken together, the results of this study indicated that the development of vegetation and the
pattern of precipitation worked in concert to regulate the components of water and carbon fluxes and
their coupling.
Ó 2010 Elsevier Ltd. All rights reserved.
Keywords:
Eddy covariance
Evaporation
Model
Precipitation pulse
Transpiration
1. Introduction
Arid and semiarid ecosystems are particularly vulnerable to
climate variability, the most important component of which is
water availability (Zhang et al., 1997). Because water is a major
determinant of hydrological and biogeochemical processes, evaluation of the patterns of soil water availability and their effects on
the ecosystems are important research topics (Dunne et al., 1991;
Noy-Meir, 1973; Reynolds et al., 2000, 2004; Scott et al., 2006;
Whitford, 2002). The development of potential vegetation type
and production is also closely associated with soil water availability
(Huxman et al., 2004). In turn, vegetation type and geomorphology
interact to influence water partitioning following precipitation in
different hydroclimatic zones (Scott et al., 2006). In these regions,
the response of major ecological and biogeochemical processes to
future climate conditions is mediated by the spatio-temporal
variations in soil water resource supply. Thus, to predict how these
systems respond to climate change and other disturbances, we
must understand how water availability and productivity are
coupled over the long-term in these regions (Huxman et al., 2004;
Reynolds et al., 2000; Risch and Frank, 2007; Scott et al., 2006).
* Corresponding author. Tel.: þ86 10 88256066.
E-mail address: [email protected] (Y. Hao).
0140-1963/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.jaridenv.2010.07.005
In water-limited grassland ecosystems, small water-associated
variations in CO2 fluxes could have a considerable effect on the
ecosystem carbon cycle (Scott et al., 2006). However, our current
understanding of soil water availability and plant and atmospheric
interactions in these ecosystems is limited (Reynolds et al., 2000).
For example, conflicting evapotranspiration investigations conducted using different experimental methods and in different
locations have prevented understanding the importance of factors
that control the timing and amounts of water lost as transpiration
vs. evaporation Additionally, due to the complicated properties
within and between soils (structure, infiltration, and root zone),
vegetation (physiological production, phenology etc.) and short
study times, it is difficult for experimental studies to quantify the
magnitude and importance of these interactions, particularly over
the long-term.
In the present study, we applied a modeling analysis that
focused on several key components of this ecosystem and was
designed to examine how biotic (plant) and abiotic (precipitation)
factors interact to affect the loss of soil water (evapotranspiration)
and long-term carbon fluxes in an arid and semiarid grassland
ecosystem. Specifically, we measured ecosystem-scale evapotranspiration (ET) and net ecosystem exchange (NEE) in the Inner
Mongolia Steppe, China, and then used a process-based ecosystem
model to separate ET into transpiration (T) and evaporation (E). We
partitioned the daily total ET into E and T, paying particular
X. Huang et al. / Journal of Arid Environments 74 (2010) 1616e1623
attention to days following different sized rainfall events. We then
analyzed the coupled response of evaporation and transpiration
and their covariation with respiration and GEP using this unique
dataset. Specifically, this study was intended to answer the
following questions: (1) how do the relative amounts of transpiration and evaporation in the study area vary from year to year? (2)
What was the relationship between key ecosystem component
fluxes and the water and carbon balance of a spatially extensive
semiarid ecosystem?
2. Materials and methods
2.1. Model description
The Vegetation Interface Process (VIP) model was used to
calculate evaporation (E) from the soil surface and canopy transpiration (T). The ratio of E/T was then used to decompose ET, which was
measured using the eddy covariance method as described below.
The VIP model was designed to simulate canopy carbon assimilation,
radiation absorption, and energy partitioning into heat fluxes and
soil moisture and thermal dynamics. The model had been widely
applied to crops and catchments by Mo and Liu (2001) and Mo et al.
(2004, 2005) and was validated in this study site by comparing the
simulated and observed parameters including daily evapotranspiration and net radiation in three continuous years (Wang et al., 2008,
detail see Appendix). In the VIP model, the features associated with
heat fluxes include: (1) Division of the canopy into sunlit and shaded
leaf groups for energy partitioning; (2) Prognostic equations for
energy balance at the canopy and below the soil surface based on the
PenmaneMonteith double-resource model (Mo et al., 2004); (3)
Partitioning of the soil into three layers and simulation of the soil
water changes using the soil moisture conductivity. In the doubleresource energy balance model, canopy transpiration latent heat
flux (LEc) and direct evaporation from the intercepted water and soil
evaporation latent heat flux (LEs) were described as follows:
LEc ¼
LEs ¼
DRnc þ rCp D0 =rac Dþg 1þ
rc
rac
1 Wfr
DðRns GÞ þ rCp D0 =ras
Dþg 1þ
rs
ras
(1)
(2)
where Ec and Es are the canopy transpiration and direct evaporation
from soil evaporation, respectively, and correspond to T and E used
elsewhere in the text; L is latent heat of vaporization, G was soil
heat flux, Rnc and Rns are the net radiation absorbed by the canopy
and soil, respectively. In addition, D is the slope of the temperatureesaturated vapor pressure curve, g is the psychrometric
constant, r is air density, Cp is the air specific heat capacity under
constant pressure, rac and ras are aerodynamic resistances, rc is
canopy resistance, and rs is soil resistance. D0 is the air vapor
pressure deficit at the reference height (2 m above the canopy),
which indicated the difference in the vapor pressure at the saturation point and actual air at that height. Wfr is the fraction of day
length consumed by wet canopy evaporation.
The mean daytime canopy surface conductance (gcan, measured
in m s1) was calculated as follows:
gcan ¼
gLEga
DðRn GÞ þ rcp VPD LEðD þ gÞ
(3)
where ga is the aerodynamic conductance calculated using MonteitheUnsworth equation:
ga ¼
u
2
u*
1617
1
þ 6:2u* 0:67
(4)
where u is the mean wind speed and u* is the friction velocity.
2.2. Site description and datasets
The experimental site was located within the Inner Mongolia
Grassland Ecosystem Research Station in the Xilin River Watershed
of the Inner Mongolia Autonomous Region (43 320 N, 116 400 E,
1200 m a.s.l.). The study site, which covers 400 600 m, has been
fenced off since 1979 and was located on a smooth wide plain that
contains low hills. The tops of the low hills were 20e30 m above
the surrounding plain, and the hills have slopes of <5 . The area has
a semiarid continental temperate steppe climate characterized by
dry springs and humid summers. The average annual temperature
was 0.4 C, and the growing season was 150e180 d. The annual
precipitation ranges from 320 to 400 mm, with most rainfall
occurring from June to August.
The experimental site contains dark chestnut (Mollisol) soil
with a depth of 100e150 cm (Wang and Cai, 1988). The soil moisture was 0.29 m3 m3 and 0.12 m3 m3 at field capacity and the
wilting point, respectively. The A horizon was 20e30 cm deep and
there was no obvious CaCO3 layer in the soil profile. The mean soil
composition was 21% clay, 60% sand, and 19% silt. Of the 86 flowering plant species, which belong to 28 families and 67 genera, 11
were grass species (Jiang, 1985). The xeric rhizomatous grass,
L. chinensis (Trin.) Tzvelev (syn. Aneurolepidium chinensis (Trin.)
Kitagava), was the constructive species, and S. grandis Smirnov,
Koeleria pyramidara (Lam.) P. Beauv (syn. K. cristata (L) Link), and
Agropyron cristatum (L.) Gaertn. were the dominant species. The
height of the grass clusters ranges from 50e60 cm, and the average
coverage is 30e40%, but can reach 60e70% during rainy years. Litter
has been accumulating since 1979 due to enclosure and exclusion
of grazing in the site.
An eddy covariance system was utilized to continuously measure
carbon dioxide (CO2) and water fluxes in the grassland. The fetch
was calculated to be approximately 200 m using a Markovian
simulation footprint model based on the predominant wind directions (Leclerc and Thurtell, 1990). Sensible heat, latent heat, and CO2
fluxes were measured at 2.2 m above the ground using a 3D sonic
anemometer (CSAT3, Campbell Scientific Inc., MS, USA) in combination with an open path infrared CO2/H2O gas analyzer (LI-7500,
LI-COR Inc. NE, USA). The eddy covariance measurements were
taken at a frequency of 10 Hz, and the turbulent fluxes from January
2003 to December 2006 were recorded on a datalogger (CR5000,
Campbell Scientific Inc.) as half-hour averages.
Other auxiliary micrometeorological variables were measured
at a site close to the EC (Eddy Covariance) system. Air temperature
(Ta), humidity, wind speed, photosynthetic active radiation (PAR),
net radiation (Rn), soil heat flux (G), and soil temperature (Ts) were
also measured. Soil moisture was monitored using time domain
reflectometry (TDR). All data were logged every 30 min using
a digital datalogger (CR23X, Campbell Scientific Inc.).
2.3. Data processing
The measurement of the leaf area index (LAI) and biomass has
been described in detail in a previous study (Hao et al., 2007). All
flux and meteorological data collected were quality controlled (Lee
and Fuentes, 1999). Roughly 20% of the data obtained from our EC
system were discarded, which was similar to most Fluxnet sites.
The gaps due to discarded data and instrument malfunction were
filled using the MDV (Mean Diurnal Variation) (Falge et al., 2001)
1618
X. Huang et al. / Journal of Arid Environments 74 (2010) 1616e1623
and interpolation methods (Aubient et al., 2002; Baldocchi et al.,
2001; Xu and Baldocchi, 2004).
The integral daily Net Ecosystem Exchange (NEE) was decomposed into ecosystem respiration (Re) and Gross Ecosystem
Production (GEP) by Eqs. (5)e(7).
GEP ¼ Re NEE
a
(5)
Daily ecosystem respiration Re was composed of daytime
respiration (Re,day) and nighttime respiration (Re, night):
Re ¼ Re;day þ Re;night
b
(6)
The nighttime respiration, which was the nighttime net
exchange measured by the eddy covariance system, was fitted to
the following equation (Mielnick et al., 2005):
(7)
EWUE ¼
P
NEE
P
ET
(8)
and plant canopy-scale water use efficiency (PWUE) as:
PWUE ¼
P
GEP
P
T
(9)
3. Results and discussions
3.1. Environmental conditions and evapotranspiration partitioning
Variations in the daily average soil water and air temperature
from 2003 through 2006 are shown in Fig. 1. The annual maximum
air temperatures were all observed in August and ranged from 22.8
to 25.7 C, while the minimum air temperature occurred in Jan. or
Feb. and ranged from 27.67 to 30.04 C (Fig. 1(a)). The air
temperature during the growing season was higher in 2005
(15.51 5.21 C) than during the other three years (14.60 4.42 C
for 2003; 14.70 4.65 C for 2004; 15.42 4.83 C for 2006).
Temporal variation in the soil moisture was more pronounced at
5 cm than at 20 cm (Fig. 1(b)). The volumetric soil water content
ranged from 0.27 m3 m3 to 0.05 m3 m3 at 20 cm, with obvious
water stress being observed in 2005 and lasting through the entire
growing season. The daily variation in soil moisture responded
briefly to precipitation; however, it took less than 10 rain-free days
for soil moisture at 5 and 20 cm to decline to near pre-event levels.
Only large rain events (>24 mm in 1 day or >20 mm rainfall for
multiple days) resulted in the soil moisture at 20 cm increasing. The
changes in soil moisture at the shallow surface were partially
attributed to the high evaporation demand, frequent antecedent
dry conditions, and high Ta, as well as the high intensity rainfall,
excess infiltration rates and run-off (Cable, 1980; Sala and
Laurenroth, 1982; Scott et al., 2006).
Fig. 1. Annual variation in daily air temperatures (a), average volumetric soil moisture
and precipitation (b) from 2003 to 2006.
ET was strongly associated with precipitation and soil moisture
in rainfed ecosystems (Hao et al., 2007; Mielnick et al., 2005; Risch
and Frank, 2007). ET, vegetation transpiration (T) and bare-soil
evaporation (E) in the study area reflected the pattern of rainfall
and the soil moisture pulse (Fig. 2). Prior to the rainy growing
season, both ET and T were approximately zero (Fig. 2). At the
beginning of the rainy growing season (May), E dominated ET
except for in 2006, accounting for 54%, 67% and 57% of ET in 2003,
2004 and 2005, respectively (Table 1). These findings were similar
to the results of a study of the Chihuahuan desert shrub ecosystem
(Scott et al., 2006). Later in the season, following soil freeze, ET and
T declined to zero.
Throughout the study period most rain fell during June, July and
August (Table 1). Additionally, transpiration accounted for
approximately 70% of the monthly ET during these three months in
each year evaluated, except for 2005. This occurred because the
vegetation became highly active during this period (Hao et al.,
2008), which resulted in increased extraction of water from the
soil (Fig. 1(b)). Transpiration and evaporation was almost equal in
July and August of 2005, during which time rainfall was much
lower than in the other three years. In the other nine months of the
year, the majority (60e80%) of water lost from the steppe was lost
5
2003
-1
where a, b and c are constants, Ts is the soil temperature ( C) at
a depth of 0.05 m, and qv is the volumetric soil water content at
0e20 cm. The complete dataset (all years) was used to determine
the best-fitted coefficients of the model (a ¼ 6.42, b ¼ 0.087,
c ¼ 1.46). Soil water content was bound to the minimum (0.08) and
maximum (0.30) qv values in the dataset to exclude situations in
which the soil CO2 flux was limited by extremely dry and wet
conditions. Eq. (7) was extrapolated to estimate Re,day using Ts and
qv observed in daytime, assuming the function between respiration
and the two soil parameters was the same in the day and at night.
We defined ecosystem water use efficiency (EWUE) as (Scott
et al., 2006):
Evapotranspiration or evaporation(mm d )
c
Re;night ¼ aeðbTs Þ 2:12 ðqv minqv Þ ðmaxqv qv Þ
2006
2005
2004
T
ET
4
3
2
1
0
150
301
87
238
23
174
325
111
263
DOY
Fig. 2. Daily evapotranspiration (ET) and evaporation (E) in the four study years.
X. Huang et al. / Journal of Arid Environments 74 (2010) 1616e1623
1619
Table 1
Monthly total water and carbon fluxes. * mean average values of each variable during growing seasons from 2003 to 2006 over Inner Mongolia Steppe, China.
P
(mm)
ET
(mm)
T
(mm)
E
(mm)
LAI
(m2 m2)
T/ET
NEE
(g cm2)
GEP
(g cm2)
Re
(g cm2)
2003
May
June
July
August
September
MayeSeptember
57.9
40.7
114.8
79.7
61.2
354.3
39.2
55.0
74.8
78.8
40.4
288.2
18.1
36.0
54.2
58.6
22.4
189.3
21.1
19.0
20.6
20.2
18.0
98.9
0.38
0.75
1.27
1.39
0.65
0.88*
0.46
0.65
0.72
0.74
0.55
0.62*
6
37
22
13
9
75
17
69
69
52
30
237
23
32
47
39
21
162
2004
May
June
July
August
September
MayeSeptember
29.5
73.6
61.9
120.8
58.5
344.3
35.7
58.2
69.6
66.6
43.9
274
11.9
38.5
48.6
47.3
22.9
169.2
23.8
19.7
21.0
19.3
21.0
104.8
0.30
0.84
1.13
1.54
0.68
0.90*
0.33
0.66
0.7
0.71
0.52
0.58*
17
2
38
67
24
114
2
38
86
117
51
294
19
36
48
50
27
180
2005
May
June
July
August
September
MayeSeptember
12.6
38.9
47.0
24.7
2.8
126.0
33.6
41.0
53.6
36.5
12.6
177.3
14.3
22.0
31.2
19.9
5.3
92.7
19.3
19.0
22.4
16.6
7.3
84.6
0.52
0.49
0.56
0.38
0.32
0.45*
0.43
0.54
0.58
0.55
0.42
0.50*
3
3
8
1
15
30
16
27
28
30
1
102
19
30
36
31
14
130
2006
May
June
July
August
September
MayeSeptember
14.98
76.3
122.9
21.93
39.7
275.8
12.2
46.7
88.0
64.4
37.4
248.7
6.8
24.4
59.6
44.4
22.7
157.9
5.4
22.3
28.4
20.0
14.7
90.8
0.16
0.42
0.64
0.73
0.40
0.47*
0.56
0.52
0.68
0.69
0.61
0.61*
32
2
9
17
3
35
13
39
54
16
27
123
19
37
45
33
24
158
to evaporation. Additionally, high precipitation during the warm
summer resulted in increased productivity in the steppe. Most rains
only permeated the upper soil layer; therefore, an average of 80% of
the total fine root mass was concentrated within 0.30 m of the
surface (Chen et al., 2003).
Monthly transpiration accounted for 33e74% of evapotranspiration in growing seasons in the study period (Table 1). On an
annual basis, transpiration and evaporation contributed equally to
ET due to the dormancy of the steppe during winter and high
rates of T and E during summer. Song (1995) found that the
average T/ET was 75%, but that this ratio declined to 50% in areas
near S. grandis communities. However, high seasonal T/ET fluctuation was expected at one site due to climate variability, the soil
moisture dynamic and differential plant functional responses (Niu
et al., 2000; Reynolds et al., 2000; Song, 1995). Indeed, the
seasonal distribution of precipitation was expected to affect the T/
ET. For example, a similar value of rainfall during the 2003 and
2004 growing seasons induced different amounts of seasonal
variability in the T/ET (Table 1). Gai et al. (2004) found that the
water table was >10 m and no run-off occurred when there was
vegetative cover. In the present study, the ratio of total ET to
precipitation varied annually, ranging from 81% to 140% during
the same periods.
During the study period, daily ET, T and E were found to be
significantly correlated with LAI, vapor pressure deficit (VPD) and
net radiation (Rn) in this ecosystem (Table 2). Specifically, ET and T
were strongly correlated with LAI and precipitation. However, E
was only correlated with VPD.
3.2. Relationships between components of ET and carbon fluxes
Prior to the rainy season (May to September), ET was minimal and
NEE was slightly positive. Furthermore, these factors showed
seasonal and annual fluctuation (Fig. 3). The variation in NEE during
the study period was found to be associated with precipitation, not ET.
Additionally, NEE was enhanced by the onset of summer rains. As the
vegetation in the steppe ended their dormancy and began to transpire, the NEE was driven down and eventually reflected a net uptake.
Daniel et al. (2006) found that discrete precipitation events triggered
brief, but important episodes of biological activity in water-limited
ecosystems. In the present study, NEE was found to have a systemlevel hysteresis when compared with ET. The dynamics of NEE and ET
observed in the present study confirmed Daniel’s findings. However,
the steppe ecosystem was opportunistic with the available water
resource, and rapidly transformed rain into productivity when under
long-term drought stress (e.g. during the drought of 2005).
By separating NEE into GEP and Re and ET into E (evaporation)
and T (transpiration), interpretation of the mutual relationship
becomes easier (Fig. 3). E and Re both spiked in response to
precipitation, with Re peaking slightly after E. The temporal differences in these processes may have occurred as a result of independent responses of plants and microbes (Daniel et al., 2006; Huxman
et al., 2004) or the increased resistance of CO2 diffusion from the
deeper soil layer as a result of water infiltration into the soil surface
immediately after rainfall (Conant et al., 2004; Scott et al., 2006),
which would explain the peak in activity that occurred several days
after the rainfall. During the study period, the changes in GEP
Table 2
The correlation coefficients of ET, E, and T and Rn, LAI, VPD, and P and the multiple linear regression equations.
Variable
LAI
VPD
P
Rn
Equation
R2
ET
E
T
0.97
0.78
0.99
0.80
0.90
0.70
0.92
0.75
0.92
0.83
0.88
0.74
ET ¼ 0.93 þ 1.09 LAI þ 0.04 VPD þ 0.05 P þ 0.002 Rn
E ¼ 0.69 þ 0.03 LAI þ 0.04 VPD þ 0.055 P þ 0.002 Rn
T ¼ 1.61 þ 1.05 LAI þ 0.01 VPD 0.01 P þ 0.001 Rn
0.98
0.88
0.98
1620
X. Huang et al. / Journal of Arid Environments 74 (2010) 1616e1623
3
2003
2004
-2 -1
2006
a
a
2
y=0.98+1.01x, R =0.46
6
Re
E
1
-2 -1
GEP(gCm d )
-1
CO2 fluxes (gCm d ) or water fluxes (mmd )
2
2005
0
6
4
NEE
ET
F
b
4
2
2
0
0
-2
-0.5
0.0
0.5
1.0
-4
1.5
2.0
2.5
3.0
3.5
-1
6
GEP
T
4
T(m m d )
5
c
b
2
y=1.28+0.82x, R =0.35
4
-2 -1
Re(gCm d )
2
0
180 231 149 199 249 146 196 251 188 243
DOY
Fig. 3. Annual change in daily ecosystem respiration (Re), evaporation (E, a), net
ecosystem exchange (NEE), daily evapotranspiration (ET, b), gross ecosystem productivity (GEP), and transpiration (T, c) in the four study years.
corresponded with T, and T and GEP had similar responses, although
the peaks in GEP seem to precede those in T by 1 or 2 days. These
results were likely due to changes in the diurnal pattern of instantaneous photosynthetic gas exchange relative to the maximum
achievable photosynthetic rates of individual leaves within
a canopy, which influenced the leaf water loss (Scott et al., 2006).
Overall, GEP was found to be well correlated with T, while the
integrated Re was not correlated with E in any of the study periods
(Fig. 4). As expected, changes in soil moisture influenced Re and E
differently, especially during drought. This, together with other
factors, resulted in failure to calculate Re by Eq. (7) (Christopher
et al., 2005; Reichstein et al., 2002b; Scott et al., 2006). However,
if we only considered the data from 2003 and 2004 (normal rainfall
years), there was a good linear relationship between Re and E
(R2 ¼ 0.5, Hao et al., 2007). Fierer and Schimel (2002) found that
a frequent dryerewetting cycle induced an increase in the substrate
limit, which led to a decreased correlation between Re and E.
A strong coupling between canopy conductance for water vapor
ðg can Þ and carbon uptake was evident for all years (Fig. 5). The
relationship between gross ecosystem exchange (GEP) and canopy
conductance for water appeared to be linear over a large range of
average conductance. During 2003 and 2004, which had average
amounts of rainfall, the canopy achieved higher gross ecosystem
exchange rates at the same canopy conductance level than during
2005 and 2006, which were dry years. There was a very strong
coupling between carbon and water fluxes, which was manifested
as a close correlation between carbon uptake and mean daytime
canopy conductance (Table 3). These findings reinforce those of
previous studies (Buchmann and Schulze, 1999; Law et al., 2000;
Reichstein et al., 2002a; Valentini et al., 1995).
3
2
1
0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
-1
E(m m d )
Fig. 4. Regression plots of (a) gross ecosystem productivity (GEP) versus daily transpiration (T) and (b) ecosystem respiration (Re) versus daily evaporation (E).
vegetation was active (Fig. 6). These were breaks in monsoon
rainfall that lasted 10 days or longer, during which time there was
little accumulative rainfall (<3 mm) (Hao et al., 2008). Differences
among years were observed in the normalized average rate of E and
T. During the growing seasons of 2003, 2004 and 2006, when
3.3. Influence of rain pulse on components of ET
To quantify the effects of rain pulses and time on the components of ET fluxes, four interstorm periods were selected when the
Fig. 5. Gross ecosystem productivity versus mean daytime canopy conductance for the
4 study years. Arrows point to the respective linear regression lines. Statistics of the
regression are shown in Table 3.
X. Huang et al. / Journal of Arid Environments 74 (2010) 1616e1623
Table 3
Parameters estimated for the linear model describing the CEP gcan relationship
during growing seasons (MayeSep.) from 2003 to 2006.
Year
a
b
N
R2
2003
2004
2005
2006
0.81
0.33
0.32
0.51
0.34
0.97
0.41
0.30
134
134
134
134
0.51
0.90
0.61
0.42
rainfall events occurred, E and T began to increase, reaching the
maximum magnitude approximately 1e3 days after the rainfall
event. Conversely, there was a decreasing tendency in the daily E
and T during periods of prolonged drought. Peak rates were not
maintained for long and began to decline rapidly. Additionally, 50%
of the original flux was attained after 10 days without rain (Fig. 6).
However, the time at which the peak value occurred was different
for T and E, with the peak evaporation rate occurring prior to the
peak in transpiration during all years evaluated in this study, except
for 2005. These results were similar to those of a study conducted in
the Chihuahuan desert shrubland (Scott et al., 2006).
In 2005, the peak E and T rates had a greater lag time than in
other years. In a water-limited ecosystem, ecosystem response was
controlled by rainfall scarcity and its intermittent and unpredictable nature. The uncertainty of both the timing and amount of
rainfall has induced vegetation to develop different strategies in
which the above-ground biomass rapidly decreases and the belowground biomass increases in response to water stress to optimize
reproduction and productivity (Hugo et al., 2006; Noy-Meir, 1973;
Yoshihiro et al., 2007). The response of GEP and Re to rain pulses
1621
was similar to that of E and T (Hao et al., 2009). A similar response of
NEE and ET to precipitation has previously been observed (Hao
et al., 2008).
3.4. Water use efficiency
The steppe ecosystem absorbed more CO2 during the growing
season when there was more precipitation (2003 and 2004) and
released more CO2 during the drought growing seasons (2005 and
2006). Overall, the ecosystem appeared to have a neutral position
in the carbon budget (Table 1). Rainfall-use efficiency has been
a topic of concern in water-limited ecosystems (Epstein et al., 1999;
LeHouerou, 1984; Niu et al., 2000) that has been evaluated in many
different ways and on many scales (leaf, community and ecosystem
scale) (Ehleringer and Osmond, 1989; Niu et al., 2000; Scott et al.,
2006). In the present study, according to Eq. (9), calculating
PWUE does not account for the loss of CO2 through plant respiration and thus only represents an approximation of the photoautotrophic efficiency. However, it is reasonable to assume that the
proportion of plant productivity that was lost to plant respiration
was a constant function of GEP (Scott et al., 2006; Waring et al.,
1998).
The variation in EWUE and PWUE were distinct for the four
growing seasons with different rainfall amounts (Fig. 7), with the
maximum PWUE and EWUE corresponding to the maximum
amount of precipitation during normal precipitation years (2003
and 2004). However, the peak biomass combined with the
maximum rainfall induced the highest water use efficiency (e.g.
2004). In water-limited years (2005 and 2006), the peak PWUE and
200
40
2004
150
2003
E
T
100
P
30
50
20
0
10
-100
-150
0
200
40
2005
150
2006
30
100
50
20
0
-50
10
-100
-150
-2
0
2
4
6
8
days since event
10
12
14 -2
0
2
4
6
8
10
days since event
Fig. 6. Response of soil evaporation (E) and canopy transpiration (T) to rainfall pulse (P).
12
0
14
Precipitation(mmd-1)
Relative value of E or T (%)
-50
1622
X. Huang et al. / Journal of Arid Environments 74 (2010) 1616e1623
4. Conclusions
0
2003
2004
2005
2006
-3
-2
Water Use Efficiency (gCm )/(mmH2O)
3
-6
May
June
Plant
July
August
September
3
0
-3
Ecosystem
-6
May
June
July
August
September
Month
Fig. 7. The annual variation in monthly ecosystem and plant water use efficiency.
EWUE was lower than in 2003 and 2004, as was the difference in
the monthly water use efficiency.
3.5. Effect of LAI on WUE
-2
Water Use Efficiency (gCm )/(mmH2O)
To examine the effects of LAI on an annual scale, we calculated
the mean LAI over the entire growing season (LAImean) and then
compared it to the annual EWUE and PWUE. The results showed
that the annual EWUE and PWUE were closely correlated with the
LAImean (Fig. 8), and that EWUE and PWUE increased as the LAImean
increased.
At the site evaluated in this study, LAI played a key role in
inducing such a positive correlation with WUE through its regulation of T/ET (Table 1). This finding differs from the results of many
previously conducted studies, which have suggested that the
variation in WUE is mainly controlled by VPD (e.g. Ponton et al.,
2006; Scanlon and Albertson, 2004; Verhoef et al., 1996). This
discrepancy may have been caused by differences in the timescales
investigated (i.e. from hours to several weeks) (Hu et al., 2008).
Additionally, many of the studies that have shown a correlation
between WUE and VPD were conducted in ecosystems with welldeveloped canopies (e.g. forest) (Herbst et al., 2002).
Plant
Ecosystem
Plant
2
0
Ecosystem
-2
-4
In this study, we examined how ET was partitioned into transpiration and evaporation in an arid and semiarid steppe from 2003
to 2006. In addition, we investigated how these components of ET
were coupled with NEE. We found that during the dormant period,
ET and T were approximately zero, but that T dominated ET during
the growing period. On an annual basis, plant transpiration
accounted for 44e58% of the total ET, and evaporation and transpiration were equivalent to ET. Evaporation peaked and declined
very rapidly following rainfall events, and evaporation dominated
ET following larger rainfall events. The peak in transpiration usually
lagged behind that of evaporation, and declined more gradually.
The size of the rain pulse affected the response of evaporation and
transpiration to precipitation events.
During the growing season, transpiration, photosynthesis and
respiration were closely linked, but evaporation and respiration
were not. The amount and time of precipitation affected the CO2
absorption by the ecosystem and regulated the dynamics of the CO2
fluxes in concert with the vegetation. Finally, the water use efficiency of the ecosystem and the plants were distinct in each of the
study years. This disaggregation of the water and carbon fluxes into
their respective components revealed the dynamic characteristics
of the biological and non-biological response of the ecosystem as
well as their responses to environmental conditions. The results
presented here should facilitate the development of a mechanistic
understanding of water and carbon flux coupling.
Acknowledgements
Financial support was subsidized by the National Natural
Science Foundation (Grant No. 30590380, 90711001, and
30700079) and the National Key Basic Research Program (NKBRP)
(Grant No. 2010CB833500). We greatly appreciate the help from
Inner Mongolia Grassland Ecosystem Research Station, the Chinese
Academy of Sciences.
Appendix
We used flux data derived from eddy covariance technology
from 2003 to 2005 over the Inner Mongolia Steppe and the
parameterized VIP (Vegetation Interface Processes) model to
simulate ET of the grassland. The results were validated using halfhourly latent heat fluxes (LE) and net radiation (Rn) estimated from
eddy covariance measurements. The model can effectively simulate
latent heat fluxes of the grassland (R2 ¼ 0.80). ET had a close relationship with the quantity and distribution of precipitation (P). In
the humid years of 2003 and 2004, the annual ET was 337 mm and
338 mm, which was greater than P. In the drier year of 2005, ET was
223 mm, which was also larger than P. On average, the water
consumed by ET could be replenished by P, and E and T made
relatively equivalent contributions to ET. About 83% of the annual
ET occurred during the growing season. In addition, E was the
primary component of ET before June, while T was the main
component after June. The maximum ET and T were observed in
July and August, respectively. ET and T were also strongly correlated
with LAI. E changed little during the growing season, and the
difference in ET came from T. The dynamic change of the canopy
conductance affected T.
-6
0.0
0.3
0.6
0.9
2
1.2
1.5
1.8
-2
References
LAI (m m )
Fig. 8. Correlations between LAI and monthly EWUE and PWUE during the growing
seasons of 2003e2006.
Aubient, M., Heinesch, B., Longdoz, B., 2002. Estimation of the carbon sequestration
by a heterogeneous forest night flux correction, heterogeneity of the site and
inter-annual variability. Global Change Biology 8, 1053e1071.
X. Huang et al. / Journal of Arid Environments 74 (2010) 1616e1623
Baldocchi, D.D., Falge, E., Gu, L.H., 2001. Fluxnet: a new tool to study the temporal
and spatial variability of ecosystem-scale carbon dioxide, water vapor, and
energy flux densities. Bulletin of the American Meteorological Society 82,
2415e2434.
Buchmann, N., Schulze, E.D., 1999. Net CO2 and H2O fluxes of terrestrial ecosystems.
Global Biogeochemical Cycle 13, 751e760.
Cable, D.R., 1980. Seasonal patterns of soil water recharge and extraction on
semidesert ranges. Journal of Range Management 33, 9e15.
Chen, Z.Z., Wang, S.P., Wang, Y.F., 2003. Update progress on grassland ecosystem
research in Inner Mongolia steppe. Chinese Bulletin of Botany 20, 423e429.
Conant, R.T., Dalla-Betta, P., Klopatek, C.C., Klopatek, J.M., 2004. Controls on soil
respiration in semiarid soils. Soil Biology & Biochemistry 36, 945e951.
Christopher, W.H., John, M.B., Philip, A.F., 2005. Increased rainfall variability and
reduced rainfall amount decreases soil CO2 flux in a grassland ecosystem.
Global Change Biology 11, 322e334.
Daniel, L.P., Travis, E.H., Brian, J.E., 2006. Resilience and resistance of ecosystem
functional response to a precipitation pulse in a semiarid grassland. Journal of
Ecology 94, 23e30.
Dunne, T., Zhang, W., Aubry, B., 1991. Effects of rainfall, vegetation, and microtopography on infiltration and runoff. Water Resources Research 2, 2271e2285.
Ehleringer, J.R., Osmond, C.B., 1989. Stable isotopes. In: Pearcy, R.W., Ehleringer, J.R.,
Mooney, H.A., Rundel, P.W. (Eds.), Plant Physiological Ecology: Field Methods
and Instrumentation. Chapman and Hall, London, pp. 281e300.
Epstein, H.E., Burke, I.C., Lauenroth, W.K., 1999. Response of the shortgrass steppe to
change in rainfall seasonality. Ecosystems 2, 139e150.
Falge, E., Baldocchi, D.D., Richard, O., 2001. Gap filling strategies for defensible annual
sums of net ecosystem exchange. Agricultural and Forest Meteorology 107, 43e69.
Fierer, N., Schimel, J., 2002. Effects of dryingerewetting frequency on soil carbon
and nitrogen transformations. Soil Biology & Biochemistry 34, 777e787.
Gai, Y., Deng, X.D., Nan, Z.G., 2004. Water balance and water use countermeasure in
Inner Mongolia Steppe. Inner Mongolia Meteorology 1, 26e27.
Hao, Y.B., Wang, Y.F., Huang, X.Z., 2007. Seasonal and interannual variation in water
vapor and energy exchange over a typical steppe in Inner Mongolia, China.
Agricultural and Forest Meteorology 146, 57e69.
Hao, Y.B., Wang, Y.F., Huang, X.Z., 2008. CO2, H2O and energy exchange of an Inner
Mongolia steppe ecosystem during a dry and wet year. Acta Oecologia 33,
133e143.
Hao, Y.B., Wang, Y.F., Mei, X.R., 2009. The sensitivity of temperate steppe CO2
exchange to the quantity and timing of natural interannual rainfall. Ecological
Informatics. doi:10.1016/j.ecoinf.2009.10.002.
Hu, Z.M., Yu, G.R., Sun, X.M., et al., 2008. Effects of vegetation control on ecosystem
water use efficiency within and amongst four grassland ecosystems in China.
Global Change Biology 14, 1609e1619.
Herbst, M., Kutsch, W.L., Hummelshøj, P., Jensen, N.O., Kappen, L., 2002. Canopy
physiology: interpreting the variations in eddy fluxes of water vapour and
carbon dioxide observed over a beech forest. Basic and Applied Ecology 3,
157e169.
Hugo, A.G., Enrique, R.V., Bruce, J.H., 2006. Ecohydrology of root zone water fluxes
and soil development in complex semiarid rangelands. Hydrological Processes
20, 3289e3316.
Huxman, T.E., Snyder, K.A., Tissue, D., Leffler, A.J., 2004. Precipitation pulses and
carbon fluxes in semiarid and arid ecosystems. Oecologia 141, 254e268.
Jiang, S., 1985. An introduction to the Inner Mongolia Grassland Ecosystem Research
Station, Academia Sinica. In: Inner Mongolia Grassland Ecosystem Research
Station (Ed.), Research on Grassland Ecosystem, vol. 1, pp. 1e11.
Law, B.E., Williams, M.M., Anthoni, P.M., 2000. Measuring and modeling seasonal
variation of carbon dioxide and water vapor exchange of a Pinus Ponderosa
forest subject to soil water deficit. Global Change Biology 6, 613e630.
Leclerc, M.Y., Thurtell, G.W., 1990. Footprint prediction of scalar fluxes using
a Markovian analysis. Boundary-Layer Meteorology 52, 247e258.
Lee, X., Fuentes, D., 1999. Long-term observation of the atmospheric exchange of
CO2 with a temperate deciduous forest in southern Ontario, Canada. Journal of
Geophysical Research 104, 15975e15984.
LeHouerou, H.N., 1984. Rain use efficiency: a unifying concept in arid-land ecology.
Journal of Arid Environments 7, 213e247.
Mielnick, P., Dugas, W.A., Mitchell, K., Havstad, K., 2005. Long-term measurements
of CO2 flux and evapotranspiration in a Chihuahuan desert grassland. Journal of
Arid Environments 60, 423e436.
1623
Mo, X.G., Liu, S.X., 2001. Simulating evapotranspiration and photosynthesis of
winter wheat over the growing season. Agricultural and Forest Meteorology
109, 203e222.
Mo, X.G., Liu, S.X., Lin, Z.H., Zhao, W.M., 2004. Simulating temporal and spatial
variation of evapotranspiration over the Lushi basin. Journal of Hydrology 285,
125e142.
Mo, X.G., Liu, S.X., Lin, Z.H., Sun, X.M., Zhu, Z.L., 2005. Multi-objective conditioning
of a SVAT model for heat and CO2 fluxes prediction, prediction in ungauged
basins: promises and progress. In: Proceedings of Symposium S7 Held During
the Seventh IAHS Scientific Assembly at Foz do Iguaçu, Brazil, April 2005. IAHS
Publ. 303.
Niu, H.S., Xu, R., Song, B.Y., 2000. Water use dynamic of Leymus chinensis population. Acta Agrestia Sinica 8, 226e232.
Noy-Meir, I., 1973. Desert ecosystems: environment and producers. Annual Review
of Ecological Systems 4, 25e51.
Ponton, S., Flanagan, L.B., Alstard, K., et al., 2006. Comparison of ecosystem
water-use efficiency among Douglas-fir forest, aspen forest and grassland
using eddy covariance and carbon isotope techniques. Global Change Biology
12, 294e310.
Reynolds, J.F., Kemp, P.R., Tenhunen, J.D., 2000. Effects of long-term rainfall variability on evapotranspiration and soil water distribution in the Chihuahuan
Desert: a modeling analysis. Plant Ecology 150, 145e159.
Reynolds, J.F., Kemp, P.R., Ogle, K., Fernandez, R.J., 2004. Modifying the ‘pulsereserve’ paradigm for deserts of North America: precipitation pulses, soil water
and plant responses. Oecologia 141, 194e210.
Reichstein, M., Tenhunen, J.D., Roupsard, O., et al., 2002a. Ecosystem respiration in
two Mediterranean evergreen Holm Oak forests: drought effects and decomposition dynamics. Functional Ecology 16, 27e39.
Reichstein, M., Tenhunen, J.D., Roupsard, O., et al., 2002b. Severe drought effects on
ecosystem CO2 and H2O fluxes a three Mediterranean evergreen sites: revision
of current hypotheses? Global Change Biology 8, 999e1017.
Risch, A.C., Frank, D.A., 2007. Effects of increased soil water availability on grassland
ecosystem carbon dioxide fluxes. Biogeochemistry 86, 91e103.
Sala, O.E., Laurenroth, W.K., 1982. Small rainfall events: an ecological role in
semiarid regions. Oecologia 53, 301e304.
Scanlon, T.M., Albertson, J.D., 2004. Canopy scale measurements of CO2 and water
vapor exchange along a precipitation gradient in southern Africa. Global Change
Biology 10, 329e341.
Scott, R.L., Huxman, T.E., Cable, W.C., Emmerich, W.E., 2006. Partitioning of
evapotranspiration and its relation to carbon dioxide exchange in a Chihuahuan
Desert shrubland. Hydrological Process 20, 3227e3243.
Song, B.Y., 1995. Studies on evapotranspiration from different plant communities in
steppe region of Inner Mongolia. Journal of Plant Ecology 19, 319e328.
Valentini, R., Gamon, J.A., Field, C.B., 1995. Ecosystem gas exchange in a California
grassland: seasonal patterns and implications for scalling. Ecology 76,
1940e1952.
Verhoef, A., Allen, S.J., De Bruin, H.A.R., Jacobs, C.M.J., Heusinkveld, B.G., 1996. Fluxes
of carbon dioxide and water vapour from a Sahelian savanna. Agricultural and
Forest Meteorology 80, 23le248.
Waring, R.H., Landsberg, J.J., Williams, M., 1998. Net primary production of forests:
a constant fraction of gross primary production? Tree Physiology 18, 129e134.
Whitford, W.G., 2002. Ecology of Desert Systems. Academic Press, San Diego, CA.
Wang, J., Cai, Y., 1988. Studies of genesis, types and characteristics of the soils of the
Xilin river basin. In: Inner Mongolia Grassland Ecosystem Research Station
(Ed.), Research on Grassland Ecosystem, vol. 3, pp. 23e83.
Wang, Y.F., Mo, X.G., Hao, Y.B., Guo, R.P., Huang, X.Z., Wang, Y.F., 2008. Simulating
seasonal and interannual variations of ecosystem evapotranspiration and its
components in Inner Mongolia steppe with VIP model. Journal of Plant Ecology
(Chinese Version) 32, 1052e1060.
Xu, L., Baldocchi, D.D., 2004. Seasonal variation in carbon dioxide exchange over
Mediterranean annual grassland in California. Agricultural and Forest Meteorology 1232, 79e96.
Yoshihiro, I., Tsuneo, K., Takehiko, I., 2007. Response of plant growth to surface
water balance during a summer dry period in the Kazakhstan steppe. Hydrological Processes 22, 2974e2981.
Zhang, X.S., Gao, Q., Yang, D.A., Zhou, G.S., 1997. A gradient analysis and prediction
on the northeast China transect (NECT) for global change study. Acta Botanica
Sinica 39, 785e799.