Ecosystem carbon exchange over a warm

Atmospheric Environment 49 (2012) 257e267
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Atmospheric Environment
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Ecosystem carbon exchange over a warm-temperate mixed plantation in the
lithoid hilly area of the North China
Xiaojuan Tong a, b, Ping Meng a, *, Jinsong Zhang a, Jun Li c, Ning Zheng a, Hui Huang a
a
Key Laboratory of Tree Breeding and Cultivation of State Forestry Administration, Research Institute of Forestry, Chinese Academy of Forestry, Beijing 100091, China
The Key Laboratory for Silviculture and Conservation of Ministry of Education, Beijing Forestry University, Beijing 100083, China
c
Key Laboratory of Water Cycle and Related Land Surface Processes, 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 9 May 2011
Received in revised form
17 November 2011
Accepted 19 November 2011
In recent decades, forest area in China increased rapidly by afforestation and reforestation, especially in its
temperate parts. However, lack of information on carbon exchange in temperate plantations in China
reduced the accuracy of estimation on regional carbon budget. In this study, CO2 flux was measured using
the eddy covariance method over a broadleaf dominant mixed plantation in the lithoid hilly area of the
North China. The results showed that annual maximum photosynthetic capacity (Amax) varied from 0.81 to
1.22 mg CO2 m2 s1 and annual initial light use efficiency (a) from 0.014 to 0.026. Net CO2 uptake was
depressed when vapor pressure deficit (VPD) was more than 2.5 kPa. Annual temperature sensitivity
coefficient (Q10) for ecosystem respiration, ranged from 1.84 to 2.35, was negatively correlated with base
ecosystem respiration (R0) (P < 0.05). Annual R0 decreased but Q10 increased evidently when winter
drought occurred. From 2006 to 2010, annual net ecosystem carbon exchange (NEE), Gross primary
productivity (GPP) and ecosystem respiration (Rec) were 355 34, 1196 21 and 841 43 g C m2 yr1,
respectively. The warm-temperate mixed plantation in the lithoid hilly area of the North China was
a strong carbon sink of the atmosphere, which was usually weaken when spring drought happened.
Ó 2011 Elsevier Ltd. All rights reserved.
Keywords:
Mixed plantation
Eddy covariance
Net ecosystem carbon exchange
Gross primary productivity
Ecosystem respiration
1. Introduction
Forests play an important role in terrestrial carbon cycle and
global climate change. During the 20th century, forest areas generally increased in temperate regions by afforestation, but decreased in
tropic areas due to deforestation. In recent three decades, the
impacts of afforestation and reforestation on global terrestrial
carbon sink are insignificant (IPCC, 2007). Nevertheless, regional
carbon sinks have increased in the areas such as China, where
afforestation and reforestation since the 1970s has sequestered
0.45 Gt C (Fang et al., 2001). The investigations on carbon sequestration by the plantations are very important for estimating the
regional carbon budget.
The effective method understanding the variability in carbon
sink/source functions of forests is directly measuring net carbon
exchange between forests and the atmosphere. The eddy covariance technique provides a useful tool to obtain long-term carbon
flux data and to evaluate the role of forests in global carbon cycle
* Corresponding author.
E-mail addresses: [email protected] (X. Tong), [email protected] (P. Meng).
1352-2310/$ e see front matter Ó 2011 Elsevier Ltd. All rights reserved.
doi:10.1016/j.atmosenv.2011.11.049
(Oechel et al., 2000; Baldocchi et al., 2001b). Net ecosystem CO2
exchange (NEE) between the biosphere and the atmosphere is the
balance between assimilatory and respiratory fluxes. Gross primary
productivity (GPP) is strongly dependent on light during the
growing season when temperature is adequate for growth, whereas
ecosystem respiration (Rec) is strongly dependent on temperature
and moisture (Falge et al., 2002). Shifts in the relative contribution
of assimilation and respiration to total fluxes could affect
future ecosystem carbon sequestration potentials, and the stability
of stored carbon (Alward et al., 1999). The interplay between
assimilation and respiration determines the diurnal and seasonal
patterns, in phase and amplitude, of net ecosystem carbon flux
(McCaughey et al., 2006; Carrara et al., 2004). Inter-annual variability in net ecosystem productivity is driven by temperature
(Carrara et al., 2003; Zha et al., 2009) and the length of growing
season (Black et al., 2000; Goulden et al., 1998; Baldocchi et al.,
2001a; Meyers, 2001; Carrara et al., 2003). Water availability
limits leaf area index (LAI) over the long-term, and inter-annual
climate variability can limit carbon uptake below the potential of
the leaf area present (Law et al., 2002).
In China, forest area enlarged obviously by afforestation and
reforestation in recent decades. Up to 2009, plantations in the
258
X. Tong et al. / Atmospheric Environment 49 (2012) 257e267
North China occupied 8.21 million ha, accounted for 13% of the total
plantation area in China (Jia, 2009). Most of them were located in
the lithoid mountain regions, with thin and barren soils. However,
the tall trees grew in these areas present a potential carbon sink in
the plantations. Based on biometric measurements, Fang et al.
(2007) reported that net ecosystem productivity (NEP) in
a warm-temperate pine plantation was 408 kg C m2 yr1. In China,
compared with the studies in temperate, subtropic and tropic
regions (e.g. Guan et al., 2006; Wen et al., 2010; Zhang et al., 2010),
the researches on forest carbon fluxes in warm-temperate regions
were scarce. Lack of carbon flux measurements for the forest in
these areas reduced the accuracy of estimation on regional carbon
budget. In this study, the eddy covariance method was used to
measure CO2 flux over a broadleaf dominant mixed plantation in
the lithoid hilly area of the North China. The objectives were: (1) to
quantify the carbon sink strength of the mixed plantation, (2) to
investigate the temporal variability in NEE, GPP and Rec, and (3) to
characterize their responses to the environmental factors.
2. Material and methods
2.1. Site description
CO2 flux was measured using eddy covariance method in
a broadleaf dominant mixed plantation of Yellow River Xiaolangdi
forest experimental station (35 010 N, 112 280 E; elevation 410 m).
The station is located at the lithoid hilly area of the North China,
adjacent to the south of Taihang Mountain and the north of Yellow
River Basin, with a warm-temperate continental monsoon climate.
In recent three decades, annual mean temperature is 13.4 C, and
annual precipitation is 642 mm. The amount of rainfall from June to
September accounts for 68% of whole year. In this region, seasonal
drought especially in spring is serious. The main wind direction
from May to September is northeast and southwest. The soil parent
material is composed of limestone. The soil is mainly brown loam,
with a thin layer (averaged 40 cm in thickness) and low soil
nutrient content.
The plantation is composed by cork oak (Quercus variabilis
blume) (80%), black locust (Robinia pseudoacacia L.) (12%) and
arborvitae (Platycladus orientalis) (8%), with ages of 32, 28 and 30
years old and heights 10.5, 9.3 and 8.2 m, respectively. The
understory is dominated by sour jujube (Ziziphus jujuba Mill. var.
inermis (Bunge) Rehd.), bunge hackberry (Celtis bungeana Bl.),
green bristlegrass herb (Setaria viridis (L.) Beauv.), and sowthistleleaf ixeris (Ixeris sonchifolia Hance). The flux tower (36 m) is situated at the center of a large area of the plantation (7210 ha). The
tree density was 1905 stems ha1 and the coverage is about 96%.
In the growing season, mean LAI for the mixed plantation is about
6.3. Mean slope of the area around the flux tower (about 1.8 km2)
is 14 . The topographic map of the flux observation site is shown
in Fig. 1.
2.2. CO2 flux and microclimate measurements
The eddy covariance system consisted of a three-dimensional
sonic anemometer (model CSAT3, Campbell Scientific Inc., USA)
and an open-path and fast response infrared CO2/H2O analyzer
(IRGA, Model LI-7500, Li-Cor Inc., USA), which can be used to
measure 3-D wind speed, air temperature, air humidity and CO2
concentration above the canopy. Both instruments were installed
at a height of 30 m. Raw data were collected at 10 Hz and recorded
by a CR5000 datalogger (Model CR5000, Campbell Scientific Inc.,
USA).
Air temperature and relative humidity were measured with
shielded and aspirated sensors (Model HMP45C, Campbell Scientific Inc.) at heights of 8, 9, 11, 14, 18, 26 and 30 m, respectively. A
pyranometer (Model CM11, Kipp & Zonen, Delft, The Netherlands)
and a net radiometer (Model CNR-1, Kipp & Zonen, Delft, The
Netherlands) were installed at a height of 27 m. In addition,
photosynthetically active radiation (PAR) and precipitation were
measured with a quantum sensor (Model LI190SB, Li-cor, Inc., USA)
and a rain gauge (Model 52203, RM Young Inc., Michigan, USA),
respectively. Soil temperature sensors were placed at the depths of
0, 5, 10, 15 and 20 cm. Soil moisture at a depth of 20 cm was
measured by time domain reflectometry (TDR) probes (Model
CS615-L, Campbell Scientific Inc., USA). Soil heat flux at a depth of
5 cm was monitored at four points around the tower (Model HFT-3,
Campbell Scientific Inc., Logan, UT, USA). All above instruments
were controlled by dataloggers (Model CR10XT and CR23XTD,
Campbell Scientific Inc., USA) and mean data were stored at 30 min
intervals. LAI was measured with a plant canopy analyzer (LAI2000, Li-cor, Inc., Lincoln, Nebraska, USA) from April to October.
Fig. 1. The topographical map of flux observation site in Yellow River Xiaolangdi forest experimental station, with 20 m equidistance lines.
X. Tong et al. / Atmospheric Environment 49 (2012) 257e267
2.3. Flux calculation, data proceeding and gap filling
300
Eddy CO2 flux (Fc) was calculated as the 30 min covariance of
vertical wind velocity (w0 ) and CO2 concentration (c0 ):
250
Fc ¼ rðw0 c0 Þ
200
where r is the density of air. Then, coordination rotation and
WebbePearmaneLeuning algorithm (Webb et al., 1980) were
applied to obtain half-hourly CO2 flux.
Net ecosystem carbon exchange (NEE) was determined as
follows:
NEE ¼ Fc þ Fs
0.12
-1
-2
150
100
50
y = 0.6904x + 12.697
(2)
where Fs is the CO2 storage flux in the air column below the level of
the eddy covariance flux measurements. Negative NEE indicates net
CO2 uptake by the plantation ecosystem and positive NEE denotes
the inverse. At the long-term scale, Fs may be neglected because the
accumulative storage CO2 flux is close to zero (Greco and Baldocchi,
1996; Baldocchi et al., 2000).
At night, CO2 flux would be underestimated due to weak
turbulence under stable atmosphere condition (Baldocchi, 2003).
To solve the problem, a threshold of friction velocity (u*) was
chosen according to the relationship between friction velocity and
CO2 flux (Falge et al., 2001). In this study, nighttime NEE enlarged
with the increase of u* under low wind speed and being constant
when u* was more than 0.35 m s1 (Fig. 2). CO2 flux were deleted
when u* was lower than the threshold (0.35 m s1). It was in
agreement with the value reported by Saitoh et al. (2010). The data
more than three times variance with the average were regarded as
the abnormal values. Moreover, the abnormal data should be
eliminated due to instrument malfunction, unfavorable meteorological conditions such as rain and dew. A linear method was used
to fill the gaps when data missed within 2 h. The larger gaps in the
daytime and nighttime were filled using the mean diurnal variation
(MDV) and nonlinear regression methods, respectively (Falge et al.,
2001).
Energy balance closure is usually used to test the quality of the
flux data. Fig. 3 shows that the slope of daily average turbulent flux
(H þ LE) verse available energy (Rn-G-S), was 0.69. A closure of
energy balance deficit in this study was 0.31. The slope obtained in
this study was within the range reported by Wilson et al. (2002) for
22 FLUXNET sites.
Nighttime NEE ( mg CO 2 m s )
1:1
H+LE (W m -2)
(1)
259
0.10
R 2 = 0.7464, P <0.001
0
-50
-50
0
50
100
150
200
250
Rn-G-S (W m-2)
Fig. 3. Relationship between daily average turbulent heat flux (H þ LE) and available
energy (Rn-G-S).
2.4. Data analysis
In the daytime, the response of half-hourly NEE to PAR can be
established based on MichaeliseMenten kinetics (Marquardt,
1963):
aAmax PAR
þ Rd
aPAR þ Amax
NEE ¼ (3)
where Amax is the maximum photosynthetic capacity, a initial light
use efficiency, Rd the dark ecosystem respiration in the daytime.
In the nighttime, NEE is often used to represent ecosystem
respiration (Rec). The relationship between nighttime NEE and
corresponding temperature could be expressed by an exponential
equation (Lloyd and Taylor, 1994):
Rec ¼ R0 ebTs
(4)
where R0 is the base ecosystem respiration rate when soil
temperature is 0 C, b an experiment coefficient, Ts soil temperature
at a depth of 5 cm. Daytime ecosystem respiration can be estimated
by the extrapolation from the parameterization derived from
Eq. (4). Temperature sensitivity coefficient (Q10) for ecosystem
respiration is the factor which respiration rate rise with every 10 C
increment of temperature and can be expressed as:
Q10 ¼ e10b
0.08
300
(5)
Gross primary productivity (GPP) was obtained as follow:
GPP ¼ NEP þ Rec ¼ Rec NEE
0.06
(6)
where NEP is net ecosystem productivity.
0.04
3. Results and discussion
0.02
3.1. Environmental conditions
0.00
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
-1
u* (m s )
Fig. 2. Relationship between nighttime net ecosystem exchange (NEE) and friction
velocity (u*). Half-hourly fluxes were averaged for every u*-class of 0.05 m s1.
It is necessary to understand the local environmental conditions
before investigating CO2 exchange in the plantation. Seasonal
patterns of daily total PAR, daily mean air and soil temperature
were remarkably similar among years (Fig. 4a and b). For monthly
PAR, the maximum generally appeared in May/June and the
260
X. Tong et al. / Atmospheric Environment 49 (2012) 257e267
PAR (MJ m -2 d-1)
15
12
a
9
6
Ta
Ts
30
20
10
20
0
10
-10
VPD (kPa)
4.0
3.5
Precipitation (mm d -1)
0
c
3.0
2.5
2.0
1.5
1.0
0.5
0.0
120
d
Prec
SWC
100
80
0.40
0.35
0.30
0.25
60
40
0.20
0.15
20
0.10
0.05
0
8
LAI
40
SWC (m3 m-3)
Air temperature (
30
Soil temperature (
b
)
0
40
)
3
0.00
e
6
4
2
0
J M M A O J M M A O J M M A O D M M A O D M M A O D
2006
2007
2008
2009
2010
Fig. 4. Seasonal variations of (a) daily total photosynthetically active radiation (PAR), (b) daily mean air temperature (Ta) and daily mean soil temperature (Ts) at a depth of 5 cm,
(c) daily mean vapor pressure deficit (VPD), (d) daily total precipitation, daily mean soil water content (SWC) at a depth of 20 cm, and (e) leaf area index (LAI).
minimum appeared in January/December. During the period of
2006e2010, the amount of annual PAR varied from 1796 to
1929 MJ m2 with an average of 1869 MJ m2. Annual mean air
temperature in the years 2006e2010 was from 14.3 to 15.2 C,
higher than the average in recent three decades (13.4 C). The low
temperature in the winter of 2008 may be attributed to a La Niña
episode appeared since summer 2007. From 2006 to 2010, annual
precipitation ranged from 456 to 600 mm, with a mean of 524 mm,
less than the average in recent three decades (642 mm). About 60%
rainfall appeared from July to September and spring was generally
drought (Fig. 4d). The asymmetric seasonal distribution of precipitation was attributed to the continental monsoon climate. Vapor
pressure deficit (VPD) was usually large in May/June. Compared
with other four years, mean VPD in the growing season was higher
X. Tong et al. / Atmospheric Environment 49 (2012) 257e267
Temperature is one of the main factors dominating ecosystem
respiration rate. Many studies showed that ecosystem respiration
was positively related to temperature (e.g. Wang et al., 2004;
Powell et al., 2008) and could be expressed by the exponential
function (Lloyd and Taylor, 1994). Nighttime NEE is used to represent nighttime ecosystem respiration. Our results showed that
nighttime ecosystem carbon exchange in the plantation rose
exponentially with an increase of soil temperature at a depth of
5 cm (Table 1). Soil temperature can explain 58%e90% of the variability in ecosystem respiration.
From 2006 to 2010, annual Q10 values in the plantation ranged
from 1.84 to 2.35 and the mean was 2.14, R0 from 0.021 to
0.043 mg CO2 m2 s1 and mean 0.029 mg CO2 m2 s1. The Q10
values in this study were higher than the value obtained in
a temperate deciduous forest (Greco and Baldocchi, 1996), but
lower than the values obtained by the studies in a cool-temperate
deciduous forest (Saigusa et al., 2002), a temperate larch forest
(Wang et al., 2004), and a temperate broad-leaved Korean Pine
forest (Wu et al., 2006). The different Q10 values among studies
might be related to the changes of soil water content, root biomass,
litter input and microbe population (Davidson et al., 1998; Yuste
et al., 2004).
Dörr and Münnich (1987) found the Q10 value in beech and
spruce mixed forest was higher in the dry year than in the humid
year. In this study, the effects of annual mean soil moisture on Q10
and R0 were not evident. However, it was found that annual R0 and
Q10 were primarily controlled by winter soil moisture. Annual R0
enlarged but Q10 declined significantly with the increase of mean
soil water content in January (P < 0.05 and P < 0.01, respectively)
(Fig. 5). In January, mean temperature was close to 0 C and
ecosystem respiration might be considered as base respiration. For
the limit of substrate, temperature sensitivity for the ecosystem
respiration declined evidently with the increase in base ecosystem
respiration. Q10 was negatively correlated with R0 (P < 0.05)
(Table 1), similar to the result obtained in the farmland (Tong et al.,
2007).
0.06
3.5
R0
Q10
y = -0.0914x + 3.5331
2
R = 0.9856, P <0.01
0.05
3.0
2.5
0.04
2.0
0.03
1.5
0.02
1.0
0.01
0.00
0.10
y = 0.0038x - 0.0272
2
R = 0.8967, P <0.05
0.12
0.14
0.16
Q 10
3.2. Temperature response of nighttime NEE
0.07
R 0 (mg CO2 m-2 s-1)
in 2007 owing to low rainfall, strong radiation and high temperature (Fig. 4c). Monthly soil water content (SWC) at the depth of
20 cm peaked in May/July. SWC in 2008 was highest among years
due to great precipitation (Fig. 4d). Maximum LAI generally
appeared in July and ranged from 6.4 to 7.0 in the 5-year periods
(Fig. 4e).
261
0.5
0.0
0.18
0.20
Soil water content (m3 m-3)
Fig. 5. Response of annual base ecosystem respiration (R0) and temperature sensitivity
coefficient (Q10) to monthly mean soil water content at the depth of 20 cm in January.
response of daytime NEE to PAR in the plantation can be expressed
by a rectangular hyperbolic function (Eq. (3)). The light response
parameters of NEE are shown in Table 2. From 2006 to 2010, annual
mean Amax was from 0.81 to 1.22 mg CO2 m2 s1, within the range
of Amax (0.84e1.75 mg CO2 m2 s1) in temperate forests given by
other studies (e.g. Ruimy et al., 1995; Dolman et al., 2002; Arain and
Restrepo-Coupe, 2005; McCaughey et al., 2006; Teklemariam et al.,
2009). The values of a (0.014e0.026) in this study were lower than
the results (0.030e0.050) obtained in other temperate forests
(e.g. Ruimy et al., 1995; Dolman et al., 2002; Arain and RestrepoCoupe, 2005; McCaughey et al., 2006).
The relationship between NEE and PAR was described by Eq. (3)
during the growing period sorted by 5 C air temperature class.
Light response parameters varied with temperature (Table 2). The
optimum temperature for Amax ranged from 20 to 25 C, for a and
Rd from 25 to 30 C. The magnitude of Amax was small under cold
and warm conditions. At low temperature, photosynthesis is often
limited by phosphate availability at the chloroplast (Sage and
Sharkey, 1987). Whereas, at high temperature, the decrease of
photosynthesis might be owing to the decline of CO2 solubility, the
affinity of Rubisco for CO2 and thermal stability of some essential
component of photosynthetic process (Berry and Downton, 1982).
Measured NEE usually differed from the NEE calculated by the
Eq. (3), and the residual NEE (observed NEE minus simulated NEE)
3.3. Light response of daytime NEE
Ruimy et al. (1995) found that CO2 flux over the vegetations had
a curvilinear response to PAR. During the growing season, the
Table 2
Light response parameters of net ecosystem carbon exchange during the growing
season. The relationship between daytime net ecosystem CO2 exchange (NEE) and
photosynthetically active radiation (PAR) was described by MichaeliseMenten
function (Eq. (3)).
Amax (mg CO2 m2 s1)
Table 1
The parameters of temperature response of nighttime NEE. Half-hourly flux was
separated into temperature bin widths of 2 C and averaged over each bin. The
relationship between nighttime NEE and soil temperature at the depth of 5 cm was
described by an exponential equation (Eq. (4)).
Year
R0 (mg CO2 m2 s1)
Q10
R2
n
2006
2007
2008
2009
2010
0.021
0.032
0.027
0.029
0.043
2.35
2.16
2.22
2.06
1.84
0.63***
0.58**
0.90***
0.88***
0.81***
16
13
15
15
15
0.029
2.14
0.71***
74
2006e2010
**
: P < 0.01;
***
: P < 0.001.
Year
2006
1.02
2007
1.22
2008
0.90
2009
0.81
2010
0.90
Ta-class ( C)
10e15
0.48
15e20
1.04
20e25
1.43
25e30
1.05
>30
0.46
Total
0.97
**: P < 0.01.
a
Rd (mg CO2 m2 s1)
R2
0.026
0.015
0.021
0.014
0.017
0.10
0.05
0.06
0.03
0.01
0.34**
0.35**
0.33**
0.20**
0.23**
0.018
0.016
0.018
0.022
0.012
0.019
0.023
0.046
0.060
0.083
0.057
0.057
0.12**
0.27**
0.35**
0.34**
0.10**
0.30**
262
X. Tong et al. / Atmospheric Environment 49 (2012) 257e267
Fig. 6. Relationship between residual daytime net ecosystem exchange (NEE) and vapor pressure deficit (VPD) during the growing seasons. NEEresidual ¼ NEEmeasured NEEsimulated.
The simulated NEE was obtained by Eq. (3).
3.4. Response of NEE to environmental factors at daily and monthly
scales
The relationship between NEE and air temperature was investigated at the daily scale (Fig. 7). It was found that the plots of daily
6
4
2
NEE (g C m-2 d-1)
were generated. Positive or negative residual NEE indicated
measured NEE was greater or less than the simulated one. Fig. 6
shows that residual NEE enlarged with an increase of VPD when
VPD was higher than 2.5 kPa. It was suggested that the ability of net
carbon uptake by the plantation decreased under dry conditions
because stoma closed and ecosystem photosynthesis was inhibited
(Farquhar and Sharkey, 1982). Compared with 2007, 2008 and 2010,
the influence of VPD on daytime net carbon uptake was more
evident in 2006 and 2009 owing to stronger solar radiation and
higher air temperature during the growing season especially in June.
The threshold VPD in this study (2.5 kPa) was larger than those in
a temperate larch forest (Wang et al., 2004) and a north Florida pine
forest (Powell et al., 2008). It is indicated that the plantation in this
study had a greater ability to endure the dry climate.
0
-2
-4
-6
-8
-10
-12
-10 -5
0
5 10 15 20 25 30 35 40
Air temperature ( )
Fig. 7. Response of net ecosystem exchange (NEE) to mean air temperature (Ta) at the
daily scale. The data were derived from 5-year experiments.
3.5. Seasonal variability in GPP, Rec and NEE
Daytime NEE (g C m -2 month -1)
Seasonal variations of NEE, GPP and Rec in the plantation were
obvious (Fig. 10). In spring, photosynthetic rate and LAI of the trees
enhanced with the increase of solar radiation and temperature.
Net CO2 absorption enlarged and the first uptake peak appeared in
May. In June, photosynthesis was inhibited by large stomatal
resistance under dry condition, and ecosystem respiration was
strengthened under high temperature, resulting in a decline of
NEE. The increase of rainfall in July made suitable water condition
that benefited tree growth. As a result, net CO2 absorption
increased and the second uptake peak occurred in July. In autumn,
GPP, Rec and net CO2 uptake reduced with the decrease of solar
radiation and temperature. LAI of the plantation declined rapidly
50
0
-50
-100
-150
y = -0.6378x + 38.011
R 2 = 0.587, P <0.001
-200
-250
0
50
100 150 200 250 300 350
PAR (M J m-2 month-1)
Fig. 8. Response of daytime net ecosystem exchange (NEE) to photosynthetically
active radiation (PAR) at the monthly scale. The data were derived from 5-year
experiments.
263
a
100
Nightime
y = -0.0011x 2 + 0.3793x + 18.573
R 2 = 0.5118, P <0.001
80
60
40
20
NEE (g C m-2 month -1)
0
NEE (g C m-2 month -1)
mean NEE concentrated under low temperature, and scattered
when air temperature were higher than 10 C. The main reason was
that an increase in temperature improved both photosynthesis and
respiration of the plantation. The scatted plots of NEE might be
attributed to net effects of other environmental factors (light,
rainfall et. al.) on photosynthesis and respiration. The growing
season was defined as the period between first and last days where
integrated 5-day running average air temperature was greater than
10 C. In this study, the growing season was generally from mid
March to mid November.
The relation between CO2 exchange and PAR varied with
temporal scales. The response of daytime NEE to PAR was expressed
by a rectangular hyperbolic function at hourly scale (Eq. (3)), but by
linear functions at the monthly scale (P < 0.001) (Fig. 8). Our result
was similar to the report by Chen et al. (2009) who found a linear
correlation between monthly GPP and PAR.
In this study, a quadratic model was used to simulate the
response of NEE to precipitation at the monthly scale (P < 0.001)
(Fig. 9). The amounts of nighttime CO2 release, daytime and daily
net CO2 uptake increased and then decreased with an increase of
precipitation. The peak values occurred at a rainfall of around
150 mm month1 (Fig. 9). Under low precipitation, both photosynthesis and respiration of the plantation ecosystem was
limited by drought. Under high precipitation, respiration was
inhibited due to large soil moisture and photosynthesis reduced
because of low solar radiation. When rainfall was higher or
lower, photosynthetic rate decreased more quickly than respiration rate, leading to small net CO2 uptake rates of the plantation (Fig. 9).
NEE (g C m-2 month -1)
X. Tong et al. / Atmospheric Environment 49 (2012) 257e267
b
0
Daytime
-50
-100
-150
-200
y = 0.0049x 2 - 1.4197x - 20.588
R 2 = 0.4992, P <0.001
-250
-300
c
0
Daily
-50
-100
-150
y = 0.0037x 2 - 1.0358x - 1.0939
R 2 = 0.3826, P <0.001
-200
-250
0
50
100
150
200
250
-1
Precipitation (mm month )
Fig. 9. Response of nighttime, daytime and daily net ecosystem exchange
(NEE) to precipitation at the monthly scale. The data were derived from 5-year
experiments.
because leaves of broad-leaved trees turned to yellow and
senescence, and only conifers remained green during the winter.
In the non-growing season, NEE was weak due to low radiation
and temperature (Figs. 1 and 10). Net CO2 uptake by the plantation
during the growing season accounted for over 90% of the magnitude for the whole year.
From 2006 to 2010, maximum daily GPP ranged from 7.9 to
12.7 g C m2 d1, maximum daily Rec from 5.1 to 5.7 g C m2 d1,
maximum daily NEE from 5.3 to 9.6 g C m2 d1 (Fig. 10). The
peaks of monthly GPP and net CO2 uptake rate usually appeared in
May/July. Monthly Rec generally peaked in July/August (Fig. 11). The
seasonal patterns among years were similar for Rec but different for
GPP and NEE. Due to spring drought, the magnitudes of GPP and net
CO2 uptake were lower in May 2007. In 2008, small GPP and net CO2
absorption in spring and summer were owing to the frozen injury
for trees under low temperature of January (Fig. 4b and Fig. 11). Cold
winter in early 2008 may be attributed to a La Niña episode
occurring since summer 2007. In 2009, the summer peak of GPP
disappeared. The second peak of net CO2 uptake rate was postponed to September due to summer drought (Figs. 4 and 11). In
summer 2009, El Niño happened in the middle and east equatorial
Pacific Ocean. The weaken monsoon led to a reduction in rainfall in
the summer of the North China.
The ratio of Rec to GPP is a useful indicator for characterizing
seasonal and inter-annual variability of carbon balance. Monthly
Rec/GPP ratio ranged from 0.32 to 1.58 (Fig. 11d). The Rec/GPP ratio
was generally low in April and May, owing to its high GPP under
strong radiation. The highest Rec/GPP ratio appeared in November/
December for its low GPP under small radiation and low
temperature.
264
X. Tong et al. / Atmospheric Environment 49 (2012) 257e267
16
NEE, GPP and R ec (g C m-2 d-1)
NEE
GPP
Rec
12
8
4
0
-4
-8
-12
J M M A O J M M A O J M M A O D M M A O D M M A O D
2006
2007
2008
2009
2010
Fig. 10. Seasonal variations of daily net ecosystem exchange (NEE), gross primary productivity (GPP) and ecosystem respiration (Rec) in the mixed plantation.
GPP (g C m-2 month -1)
NEE (g C m-2 month -1)
3.6. Annual carbon budget
60
30
0
-30
a
-60
-90
-120
-150
-180
2006
2007
2008
2009
2010
b
250
200
150
100
50
R ec (g C m-2 month -1)
0
c
160
120
80
40
0
R ec /GPP ratio
d
1.5
1.0
0.5
0.0
J
F
M
A M
J J
M onth
A
S
O
N
D
Fig. 11. Variations of monthly net ecosystem exchange (NEE), gross primary productivity (GPP), ecosystem respiration (Rec) and the ratio of Rec to GPP in the mixed
plantation.
In the lithoid hilly area of the North China, the plantation was
a strong carbon sink of the atmosphere. During the periods of
2006e2010, annual NEE of the broadleaf dominant mixed plantation ranged from 286 to 477 g C m2 yr1, with an average of
355 g C m2 yr1 (Table 3). The rough estimation of carbon fixed
by the plantation with about 30-year ages was 10.7 kg C m2.
Biometric measurement in 2008 showed that the above-ground
biomass of cork oak, a dominate tree species in the mixed plantation, was 12.6 kg m2, approximately equal to a carbon storage of
6.3 kg C m2. Considering the magnitude of carbon in roots and the
increase in soil organic carbon, the obtained NEE was reasonable.
Inter-annual variation of NEE was significant and mainly due to
great shift of GPP and Rec among years. In the years 2006e2010,
annual GPP averaged 1196 g C m2 yr1, with a range from 1142 to
1243 g C m2 yr1; annual Rec averaged 841 g C m2 yr1, with
a range from 723 to 957 g C m2 yr1, annual Rec/GPP ratio averaged
0.70, with a range from 0.60 to 0.77 (Table 3). In the lithoid hilly
area, photosynthesis and respiration of the plantation ecosystem
were limited by the shortage of water and nutrient. GPP and Rec in
our site were much lower than other plantations. Nevertheless, NEE
in this study was greater owing to its high efficiency of carbon use
(small Rec/GPP ratio) (Table 4).
Carrara et al. (2003) found that the inter-annual variability of
carbon balance in a mixed temperate forest was mainly dependent
on the length of growing season and annual temperature. In this
study, temperature, the length of growing season could not interpret the inter-annual variability in NEE during the period of
2006e2010. So did radiation and effective cumulative temperature
more than 10 C. Summarizing the documents on CO2 exchange
over boreal, temperate and tropical forests, Luyssaert et al. (2007)
reported that annual GPP enlarged with increasing annual precipitation but the relationship between annual NEE and precipitation
was unremarkable. Likewise, we found that NEE was not correlated
with precipitation, SWC and VPD at the annual scale. However,
annual NEE was proportional to soil moisture in spring, especially
in May (P < 0.05) (Fig. 12). For dominant tree species in the mixed
plantation, like cork oak, spring was a key period for tree growth
and development. When drought occurred in this period, photosynthesis declined and leaf growth was limited. Insufficient leaf
area might cause significant reduction in carbon absorption by the
trees for the whole year.
Table 4 shows the reports on carbon flux in the sites
from 34 N to 50 N, involving the natural forest and plantation
X. Tong et al. / Atmospheric Environment 49 (2012) 257e267
265
Table 3
Annual carbon budget in the mixed plantation and environmental factors.
Year
PAR (MJ m2 yr1)
Ta
( C)
Te
( C)
GSL
(days)
Prec
(mm yr1)
SWC
(m3 m3)
VPD
(kPa)
NEE (g C m2 yr1)
GPP (g C m2 yr1)
Rec (g C m2 yr1)
Rec/GPP
2006
2007
2008
2009
2010
1899
1855
1796
1929
1867
15.1
15.2
14.7
14.6
14.3
2696
2544
2560
2586
2392
241
234
238
225
225
560
500
600
456
506
0.172
0.169
0.190
0.182
0.180
0.76
0.84
0.81
0.79
0.74
477
286
309
371
332
1200
1243
1157
1142
1237
723
957
848
770
905
0.60
0.77
0.73
0.67
0.73
2006e2010
1869
14.8
2555
233
524
0.179
0.79
355
1196
841
0.70
NEE: annual net ecosystem CO2 exchange; GPP: gross primary productivity; Rec: ecosystem respiration; PAR: photosynthetically active radiation; Ta: air temperature;
Te: effective cumulative temperature above 10 C during the growing season; GSL: growing season length; Prec: precipitation; SWC: soil water content at the depth of 20 cm;
VPD: vapor pressure deficit.
ecosystems in warm-temperate and cool-temperate regions. Net
CO2 uptake rate in this study was within the values observed for
other plantations but larger than the results obtained in natural
forests. The influences of temperature and precipitation on
carbon uptake by the plantation and natural forests were insignificant at the annual scale. However, annual carbon uptake by
the plantations decreased obviously with stand ages (P < 0.05)
(Table 4), in agreement with the results reported by Arain and
Table 4
Comparison of net ecosystem carbon exchange (NEE), gross primary productivity (GPP), ecosystem respiration (Rec) among different forest ecosystems.
Type
Site
Plantation Christchurch,
New Zealand
Duke, USA
Species
GPP
Rec
Rec/ Ta
Prec Observation Reference
Age
NEE
(year) (g C m2 yr1) (g C m2 yr1) (g C m2 yr1) GPP ( C) (mm) period
42 520 S, 172 450 E
Radiata pine
8
603
1780
1177
35 980 N, 79 080 W
Loblolly pine
18
463
e
e
355
1196
841
0.70 14.8 524
276
1713
1437
0.84 13.5 900
0
0
e
e
1995e1997 Arneth
et al. (1998)
1998e2004 Oren
et al. (2006)
2006e2010 This study
44 400 N, 0 050 E
Cork oak, black
30
locust, arborvitae
P. Pinaster
30
Kiryu, Japan
34 580 N, 135 590 E
Japanese cypress
42
479
1539
1060
Tomakomai,
Japan
Takayama,
Japan
42 440 N, 141 310 E
Japanese larch
45
212
1673
1462
Japanese cedar
and Japanese
cypress
49 520 N, 125 200 W Douglas-fir
40e50 339
2205
1860
56
293
2076
1784
0.86 8.4
1293 1998e2005 Schwalm
et al. (2007)
42 420 N, 80 220 W
White pine
65
196
1442
1247
0.86 7.8
710
2002e2003 Arain and
RestrepoCoupe (2005)
48 400 N, 7 050 E
Beech
31
257
1245
988
0.79 9.8
871
1997
Oak, birch
50
237
978
742
0.76 6.4
74
156
1075
919
0.85 2.1
835
90
150
1310
1160
0.89 6.4
858
1996e1998 Barr
et al. (2002)
100
174
1343
1168
0.87 6.1
988
1996e2002 Hollinger
et al. (2004)
200
169
e
e
3.6
695
2002e2003 Guan
et al. (2006)
250
295
e
e
e
e
1996e1997 Anthoni
et al. (1999)
Hesse, France
35 01 N, 112 38 E
0.66 10.8 658
Xiaolangdi,
China
Le Bray, France
Vancouver
Island,
BC, Canada
Turkey point,
Canada
Natural
Forest
Location
0
0
36 08 N, 137 22 E
Takayama, Japan 36 080 N, 137 250 E
Northeastern
48 130 N, 82 090 W
Ontario, Canada
Camp Borden,
Canada
44 190 N,
Howland, USA
45 150 N,
Changbai
Mountain,
China
42 240 N,
Oregon, USA
44 290 N,
Trembling aspen,
black spruce,
white spruce,
white birch,
balsam fir
79 560 W Red maple,
trembling aspen,
white ash,
large-tooth aspen,
black cherry
0
68 44 W Red spruce,
eastern hemlock,
balsam fir, white
pine, northern
white cedar
128 050 E Korean pine,
tuan linden,
mono maple,
Mongolian oak,
manchurian
ash, elm
121 370 W Ponderosa pine
Ta: annual mean air temperature, Prec: precipitation.
1996e2005 Luyssaert
et al. (2007)
0.69 14.1 1309 2001e2002 Takanashi
et al. (2005)
0.87 6.2 1040 2001e2003 Hirata
et al. (2007)
0.84 11.2 1723 2006e2007 Saitoh
et al. (2010)
Granier
et al. (2000)
1994e2002 Saigusa
et al. (2005)
2003e2004 McCaughey
et al. (2006)
266
X. Tong et al. / Atmospheric Environment 49 (2012) 257e267
References
0
NEE (g C m-2 yr -1)
-100
y = -4923.2x + 542.61
Apr-M ay
M ay
R 2 = 0.8068, P <0.05
-200
-300
-400
-500
y = -3369.7x + 238.25
-600
0.14
R 2 = 0.8864, P <0.05
0.16 0.17 0.19 0.20 0.22
Soil water content (m3 m-3)
0.23
Fig. 12. Relationship between annual net ecosystem exchange (NEE) and spring soil
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4. Conclusion
Based on CO2 flux measurements from 2006 to 2010, we found
that the broadleaf dominant mixed plantation in the lithoid hilly
area of the North China was a strong carbon sink of the atmosphere.
Annual average NEE, GPP and Rec in the plantation were 355 34,
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The inter-annual variability of carbon exchange was significant and
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The relationship between nighttime NEE and soil temperature
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Acknowledgments
This study was sponsored by Special Public Sector Research
(GYHY200706046), the National Natural Science Foundation of
China (31100322), the Fundamental Research Funds for the Central
Universities (NO. YX2011-19), and the National Key technology R&D
Program of China (2011BAD38B06). We also thank two anonymous
reviewers for thoughtful suggestions.
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