Soil Biology & Biochemistry 35 (2003) 1467–1483
www.elsevier.com/locate/soilbio
Explaining temporal variation in soil CO2 efflux in a mature
spruce forest in Southern Germany
Jens-Arne Subke*, Markus Reichstein, John D. Tenhunen
Department of Plant Ecology, University of Bayreuth, 95440 Bayreuth, Germany
Received 22 June 2002; received in revised form 14 July 2003; accepted 21 July 2003
Abstract
An open dynamic chamber system was used to measure the soil CO2 efflux intensively and continuously throughout a growing season in a
mature spruce forest (Picea abies) in Southern Germany. The resulting data set contained a large amount of temporally highly resolved
information on the variation in soil CO2 efflux together with environmental variables. Based on this background, the dependencies of the soil
CO2 efflux rate on the controlling environmental factors were analysed in-depth. Of the abiotic factors, soil temperature alone explained 72%
of the variation in the efflux rate, and including soil water content (SWC) as an additional variable increased the explained variance to about
83%. Between April and December, average rates ranged from 0.43 to 5.15 mmol CO2 m22 s21 (in November and July, respectively) with
diurnal variations of up to 50% throughout the experiment. The variability in wind speed above the forest floor influenced the CO2 efflux rates
for measuring locations with a litter layer of relatively low bulk density (and hence relatively high proportions of pore spaces). For the
temporal integration of flux rates for time scales of hours to days, however, wind velocities were of no effect, reflecting the fact that wind
forcing acts on the transport, but not the production of CO2 in the soil. The variation in both the magnitude of the basal respiration rate and the
temperature sensitivity throughout the growing season was only moderate (coefficient of variation of 15 and 25%, respectively). Soil water
limitation of the CO2 production in the soil could be best explained by a reduction in the temperature-insensitive basal respiration rate, with
no discernible effect on the temperature sensitivity. Using a soil CO2 efflux model with soil temperature and SWC as driving variables, it was
possible to calculate the annual soil CO2 efflux for four consecutive years for which meteorological data were available. These simulations
indicate an average efflux sum of 560 g C m22 yr21 (SE ¼ 22 g C m22 yr21). An alternative model derived from the same data but using
temperature alone as a driver over-estimated the annual flux sum by about 7% and showed less inter-annual variability. Given a likely shift in
precipitation patterns alongside temperature changes under projected global change scenarios, these results demonstrate the necessity to
include soil moisture in models that calculate the evolution of CO2 from temperate forest soils.
q 2003 Elsevier Ltd. All rights reserved.
Keywords: Carbon cycle; Open dynamic chamber; Picea abies; CO2 efflux; Soil temperature; Soil water content
1. Introduction
Recent publications indicate that the terrestrial biosphere
is acting as a C sink (Valentini et al., 2000; Schimel et al.,
2001; IPCC, 2001), thus mitigating a potential global
warming due to radiative forcing by anthropogenic emissions of so-called greenhouse gases (mostly CO2 and CH4,
but also N2O and halocarbons). Most of the C bound in the
terrestrial biosphere is found in the soil (IPCC, 2000), with a
general trend of increasing C storage with decreasing annual
* Corresponding author. Present address: Dipartimento di Scienze
Ambientali, Seconda Università di Napoli, Via Vivaldi, 43, 81100
Caserta, Italy. Tel.: þ39-0823-274656; fax: þ39-0823-274605.
E-mail address: [email protected] (J.-A. Subke).
0038-0717/$ - see front matter q 2003 Elsevier Ltd. All rights reserved.
doi:10.1016/S0038-0717(03)00241-4
average temperatures (Parton et al., 1987; Houghton, 1995).
It is the reaction of this largest of all pools to changes in
climate, which will determine whether ecosystems will
continue to absorb CO2 from the atmosphere, or whether
increased decomposition of soil organic matter (SOM) will
eventually turn present C sinks into C sources.
The decomposition of SOM is a function of environmental variables (both physical and chemical) and the
composition of the SOM. The changes in chemical
composition of organic material with age affect the rate at
which certain fractions of the total SOM pool can be
decomposed (Berg et al., 1996; Coûteaux et al., 1998; Berg,
2000). While most of freshly added organic material
decomposes readily after a few years (Bohn et al., 1985),
the remainder becomes part of a more inert, or stable C pool
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within the soil. In the absence of disturbance, SOM
accumulation can continue over centuries or even millennia
(Jenny, 1980; Bohn et al., 1985; Liski, 1997). The
temperature sensitivity of this old fraction of SOM is
critical, since a global trend towards higher annual mean
temperatures would create a positive feedback for global
change. If this persistent fraction of SOM is tolerant of
rising temperatures, as is indicated by some results (Liski
et al., 1999), the present C sink may prevail or even
strengthen (Grace and Rayment, 2000; Thornley and
Cannell, 2001). However, this temperature insensitivity of
old SOM has been disputed by Ågren (1999), who cites
other results indicating that old SOM is indeed sensitive to
temperature. Trumbore (2000), however, cautions that
extrapolations of research findings (on which the models
mentioned here are founded) have to account for the
heterogeneity of the C stock since storage calculations will
otherwise under-estimate short-term storage and overestimate long-term storage of C. Changes in the allocation
of assimilated C to roots and root turnover following
climatic change may provide yet another process that affects
C storage below ground (Norby and Jackson, 2000) which is
only poorly understood, and hence not reflected in
ecosystem models.
The picture emerging from these ongoing trends in
ecosystem research shows that reliable predictions about the
behaviour of ecosystems with respect to their C storage
potential are only possible if the below-ground processes are
better understood. There is a considerable amount of
literature covering the efflux dependencies of CO2 from
forest soils, covering a wide range of sampling strategies.
However, most studies are based on sporadic sampling of
the soil CO2 efflux, or misrepresent seasonal effects by too
short sampling campaigns. We have made an in-depth
analysis of the factors influencing the CO2 efflux from soil
based on data collected throughout an entire growing season
in a mature spruce forest located in a small mountain range
in Southern Germany.
2. Methods
2.1. Site description
The ‘Weidenbrunnen 2’ site is a 112-yr-old Norway
spruce stand (Picea abies (L.) Karst.) at about 760 m
elevation in the Fichtelgebirge, a mountain range in
northern Bavaria (SE Germany; 508080 N, 118520 E). The
local soil type was classified as cambic podzol over granitic
bedrock characterised by low pH values (3.3 –3.9; Heindl
and Bott, 1995). Soil litter and the organic horizon had an
average thickness of 1.6 and 15 cm, respectively, with
roughly equal thickness in the Of and Oh horizons. Average
tree height was 27 m with a tree density of about 312
trees ha21 and a leaf area index of 7.2 (E. Falge, personnel
communication). The understorey was characterised by
Fig. 1. The ground vegetation of 50 £ 50-m2 grid-map in Weidenbrunnen 2
with the dominant species for each of the 2.5 £ 2.5 m2 indicated. Numbers
indicate the approximate positions of the soil chamber locations, E is the
location of the eddy correlation sensor, and dark dots are mature trees.
a closed cover dominated by the grasses Deschampsia
flexuosa and Calamagrostis villosa, in large monospecific
patches. Small patches of Urtica dioiica and ‘nurseries’ of
dense 2 to 4-yr-old P. abies patches also occur (Fig. 1).
2.2. Sampling system
To capture the temporal variation in CO2 efflux on short
(diurnal) and extended time scales (one growing season), a
sampling system was constructed which was capable of
continuously recording the instantaneous efflux rate from
multiple sampling positions. Great care was taken in the
construction of the sampling chambers to avoid measuring
artefacts, in particular from pressure reductions in the
chamber space which inherently cause problems with open
chamber designs. Fig. 2 shows one open dynamic chamber
installed at the site, showing the chamber design with air
intakes of ambient and chamber air. A schematic diagram of
the gas path between each of the five chambers and the gas
analyser is shown in Fig. 3, while a more detailed
description of the design and instrumental set-up can be
found in Subke (2002).
The instantaneous soil CO2 efflux from each of five
chambers was measured sequentially once every hour.
Chamber tests have shown that the presence of the chamber
only slightly alters the temperature of the topsoil, and had no
J.-A. Subke et al. / Soil Biology & Biochemistry 35 (2003) 1467–1483
1469
each of the five lids between the three collars of each
measuring location (generally once every 1 to 3 d), allowed
sufficient time for the environmental conditions within each
collar to be unaffected by the previous presence of a
chamber lid.
2.3. Installation of the soil respiration system in the field
Fig. 2. Open dynamic soil chamber (design adapted from Rayment and
Jarvis, 1997). Air is drawn from the chamber through the lateral canal, and
ambient air passively follows the pressure gradient through the centrally
mounted inlet tube. The intake of ambient air for the differential
concentration measurements can be seen next to the inlet tube.
discernible effect on the soil moisture (Subke, 2002). To
avoid any artefact due to prolonged presence of the
chamber, however, only readings of the instantaneous
CO2 efflux rate obtained within 24 h of positioning a
chamber lid on a collar were considered valid data. Moving
Five sample locations within the stand were chosen for
soil respiration measurements, and three collars were
installed at each of these locations. Collars were inserted
to a depth of between 1 and 2 cm into the soil, with about
50 cm spacing between collars at each location, and
remained in place for the duration of the experiment. To
capture potential differences in the soil CO2 efflux due to the
ground cover, two sampling locations were selected for each
of the most dominant ground vegetation types, and a third
location was located in a ‘nursery’ of 2-yr-old P. abies
plants (Fig. 1). The chambers were usually positioned as far
as possible from mature trees (between 3 and 4 m) with the
exception of chamber 3, where the collars were within 0.5
and 1 m of a mature tree. Within each collar, all aboveground parts of the vegetation had been removed before
Fig. 3. Schematic diagram of the gas path between the five chambers and the infrared gas analyser (IRGA). A multiplexer controlled the switching of solenoid
valves within the pumping unit (shaded area), thus directing the flow from the five chambers and a calibration line (top) sequentially to the IRGA. A data logger
(not shown) recorded all relevant readings at 1 min intervals.
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measurements were made and any new growth removed
during the season. Collars were installed 48 h before
measurements commenced to avoid artefacts due to the
installation. Throughout this text, each of the 15 soil collars
will be referred to according to the measuring location and
the number of the collar within this location. For example,
the collar description ‘5_2’ refers to the second collar of
sampling location 5 (Fig. 1).
can be simulated. Using meteorological data that was
available for the entire year and measurements of some soil
qualities at the site, it was possible to simulate the organic
SWC over 7 weeks for which good measurements (from five
probes) were available. Since the data from the measuring
probes was both discontinuous and inconsistent, only SWC
values calculated using this model are used in the analysis.
2.6. Data analysis
2.4. Soil respiration measurements
Readings of the differential CO2 concentration and the
flow rate were obtained at 1 min intervals. The soil
respiration rate could be calculated from the respective
variables according to
Fsoil ¼
Cdif fk
;
A
ð1Þ
where Cdif is the differential CO2 concentration between
chamber air and ambient (in mmol mol21), f is the flow rate
(in l min21), A is the chamber base area (315 cm2), and k is a
constant factor combining the conversions of CO2 concentrations from (mmol mol21) to (mmol m23) and for f from
(l min21) to (m3 s21).
The CO2 differential signal was checked for stability to
ensure steady-state conditions within the chamber, and the
average value for the soil CO2 efflux of the last 3 min of a
10 min measuring interval were recorded.
2.5. Correlating measurements
The production of CO2 within the soil is basically a
biochemical process and thus responds strongly to variations in temperature. This dependence may change with the
age of the organic matter (roughly corresponding to
increasing depth within the soil), and also with the
availability of water for the relevant biochemical reactions.
Accordingly, temperature probes and soil moisture sensors
were installed near the soil collars. At each of the five
locations, a temperature profile was sampled at 5, 10 and
30 cm depths once every 30 min. The soil water content
(SWC) was recorded for the upper 10 cm of the organic
layer (Theta Probes, Delta-T devices Ltd, Cambridge, UK).
Since the variation in wind speed has been hypothesised to
affect the transport of CO2 from the soil (Kimball and
Lemon, 1971), wind velocity data, which was available
from an eddy correlation sensor operated at the same site
(Fig. 1), was also considered for analysis.
Measurements of SWC were not consistent throughout
the year owing to the varying number of soil moisture
probes used. To adjust an apparent bias due to the
misrepresentation of the stand SWC by a too small number
of probes, an existing stand process model was employed to
simulate the water content of the organic layer. This process
based model (PROXEL, Reichstein, 2001) includes a multilayer soil compartment, in which the movement of water
2.6.1. CO2 efflux
The soil CO2 efflux was analysed with respect to its
dependence on temperature ðTÞ; SWC, and wind forcing ðuÞ :
FðsoilÞ ¼ fðTÞ £ fðSWCÞ þ fðuÞ :
ð2Þ
While T and SWC both act on the production of CO2 by
autotrophic or heterotrophic respiration, u affects the
physical transport of CO2 from the soil to the atmosphere.
The amount emitted due to pressure induced pumping relates
to a quantity of CO2 stored in the soil pores, which can be
visualised as a buffer between the soil and the atmosphere
that is depleted under turbulent and replenished under calm
conditions, and the long-term average of this flux value is
therefore zero. Accordingly, the dependence on wind
induced pressure fluctuations is only used for instantaneous
CO2 efflux measurements, while efflux averages are tested
for T and SWC dependence only. Different possible
relationships between the environmental variables and the
CO2 efflux were tested for each of these functions (Sections
2.6.2– 2.6.4). These are developed from existing equations,
and the model parameters are estimated to fit the function to
the measurement data using multivariate, non-linear
regression. All regression fits were performed using the
software PV-Wave version 6.21.
2.6.2. Temperature functions
The Arrhenius type function (Eq. (3)) as described by
Lloyd and Taylor (1994) is widely accepted as a realistic
description of the fundamental temperature dependence of
soil respiration. The Q10 function (Eq. (4)) was used as an
alternative exponential temperature relationship since it is
also widely used. It is noted, however, that the concept of a
strict Q10 relationship for soil respiration processes has been
criticised on the basis of the variation of this factor itself
with temperature and SWC (Howard and Howard, 1993;
Lloyd and Taylor, 1994; Kutsch, 1996). The basic
difference between these two functions is that in Eq. (3),
the temperature sensitivity decreases with increasing
temperature, while in Eq. (4), the relationship is constant
throughout the temperature range. In order to assess the
deviations of these relatively complex relationships from a
simple linear one, a linear dependence of soil CO2 efflux on
the soil temperature was also included (Eq. (5))
fðTÞ ¼ Rref eE0 ðð1=56:02Þ2ð1=Tþ46:02ÞÞ ;
ð3Þ
J.-A. Subke et al. / Soil Biology & Biochemistry 35 (2003) 1467–1483
ðT2Tref =10Þ;
fðTÞ ¼ Rref Q10
ð4Þ
fðTÞ ¼ Rref þ mðT 2 Tref Þ;
ð5Þ
where T is the soil temperature (in 8C). All other model
parameters were fitted by the multivariate non-linear
regression. Rref is the soil respiration rate at the reference
temperature Tref (here set to 10 8C, the median temperature
of the data set), E0 is an exponential parameter affecting the
temperature sensitivity (which is related to the activation
energy in the Arrhenius equation, see Lloyd and Taylor,
1994), and m is the slope of the linear regression.
2.6.3. Soil water content functions
Soil CO2 efflux data were not tested against the SWC
alone, since this environmental variable shows less diurnal
or seasonal variation than temperature, so that effects due to
SWC would be masked by the influence of temperature.
Instead, SWC sensitivity of soil CO2 efflux was tested
simultaneously to the temperature dependence by multiplying each of the T functions with one of the SWC functions
described in this section.
Eq. (6) is a modified version of the model proposed by
Bunnell et al. (1977), while Eq. (7) is an alternative
formulation derived from a Gompertz function after
Janssens et al. (2002):
fðSWCÞ ¼
SWC
;
SWC1=2 þ SWC
fðSWCÞ ¼ e2e
ða2b£SWCÞ
;
ð6Þ
ð7Þ
where SWC is the volumetric soil water content (m3
water m23 soil), SWC1=2 is the soil water content, at which
half the maximum respiration (i.e. under conditions without
water stress at a given temperature) occurs, and a and b are
both data set specific constants. Both the Bunnell and
Gompertz model for soil water limitation included in their
original form a constraint for limitation due to high SWCs.
However, no meaningful parameters could be found for
equations including soil CO2 efflux limitation due to high
SWCs, presumably owing to sufficient drainage of the upper
soil layers at the Weidenbrunnen 2 site. Accordingly, Eqs.
(6) and (7) only contain the functions describing limitations
due to dry soil conditions.
2.6.4. Wind forcing function
Vertical movement of air may be induced by pressure
differences that occur at the soil surface. This ‘pumping’
motion may represent a significant means of physical
transport of CO2 from the soil, and the open chamber design
(in contrast to closed chamber models) allows this natural
process to occur within the chamber space. The variation in
wind speed ðsuÞ had been found to be an appropriate
surrogate for pressure fluctuations (Subke, 2002). The
function for wind forcing took the simple linear form of
fðuÞ ¼ csu;
ð8Þ
1471
where su is the standard deviation of the horizontal wind
velocity (recorded at 20 Hz and aggregated into 1 s
averages; standard deviations were formed for 10 min
intervals for these 1 s averages) and c is a linear parameter
fitted during the regression routine.
2.7. Calculating the annual soil C efflux
for Weidenbrunnen 2
The results of the data analysis described so far allow the
calculation of soil CO2 efflux rates for given environmental
conditions. This allows the simulation of the soil CO2 efflux
for periods over which relevant input data are available but
for which no measurements took place. It is thus possible to
calculate efflux sums that can be compared either to the total
stand flux of C, or for different periods to assess the
temporal variability due to climatic variations.
The input for such a soil model created from the regression
results is a continuous data set with all relevant environmental variables. The interest in this context is in long-term
flux sums, so that the function influencing the transport of
CO2 ðfðuÞ Þ is of no relevance, since its average contribution to
the efflux is zero (see Section 4 for more detail). For the
remaining variables (T and SWC), a long-term continuous
data set could be constructed from a number of sources.
Soil temperature measurements form part of a measuring
routine at an intensive research site immediately adjacent to
the Weidenbrunnen 2 plot (data supplied by the Bayreuth
Institute for Terrestrial Ecosystem Research, BITÖK), and
data were available from 1 April, 1997 to the 1 April, 2001.
Following the results of the regression analysis, only the
temperature at 5 cm depth was required, which was
aggregated into hourly averages (from 10-min interval
data). Short gaps in the data set (, 2 h) were filled by linear
interpolation. For longer gaps (up to 36 h), the average was
formed from the temperature readings taken at the same
time of day on the preceding and following days and a
correction of a linear trend of the averaged values performed
to maintain continuity. Following the described steps, most
data gaps could be filled, but one longer period of missing
data remained (8 August –12 September, 2000). Preliminary
calculations showed that annual sums calculated from
temperature data, aggregated into hourly averages, differed
from sums calculated from data aggregated into daily
averages by less than 0.2%. In order to simplify the gap
filling of the remaining gap, data were aggregated into daily
averages. It was possible to create a simple model based on
(1) air temperature (also measured at an adjacent site and
data supplied by BITÖK), (2) the temperature at 5 cm depth
averaged over the preceding 10 d, and (3) an approximation
of the lower soil temperature. For the same periods in 1997–
1999, this model produced calculated temperatures in good
agreement with those measured (linear fit for measured vs.
modelled temperatures: r 2 ¼ 0:91; s:d: ¼ 0:222; n ¼ 36;
P , 0:0001), so that a realistic modelling of the temperature
at 5 cm depth for the period in 2000 can be assumed.
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Comparison of the temperature measured in Weidenbrunnen 2 at 5 cm depth during this study, and the
contemporary data from the Weidenbrunnen flux-tower
site showed a good correlation (r 2 of 0.99 for daily averages
for 145 d; data range: , 1 to . 16 8C). However, there was a
consistent and significant ðP , 0:001Þ deviation from the
1:1 line for the two temperature averages, possibly owing to
a difference in stand structure or a slight difference in the
burial depth of the respective temperature probes. For the
modelling of soil CO2 efflux from the Weidenbrunnen 2 site,
the temperature readings from Weidenbrunnen were
corrected according to y ¼ 0:878x þ 1:31:
Long-term data for the SWC of the organic layer were
not available. However, using the model described for
the derivation of SWC for 1999 (Reichstein, 2001), the
SWC could be modelled from 1 April, 1997 to 31 March,
2001. Data gaps (due to missing precipitation data) never
exceeded more than 7 d, and were filled using the seasonal
average of previous and following years.
3. Results
3.1. Daily and seasonal patterns of soil CO2 efflux
3.1.1. Seasonal and daily flux pattern
Soil respiration was measured continuously from 28
April to 3 December, 1999 with two long gaps in July and
Fig. 4. Seasonal course of measured soil temperatureðn ¼ 3432Þ; modelled SWC of the organic layer, and measured soil CO2 efflux ðn ¼ 2429Þ in 1999. All
data are hourly averages.
J.-A. Subke et al. / Soil Biology & Biochemistry 35 (2003) 1467–1483
September/October (2 and 4 weeks, respectively) due to
instrument failure. The daily average soil CO2 efflux rate
(i.e. flux rates averaged for all collars measured in 1 d)
ranged from 0.58 mmol m22 s21 on 16 November to
3.72 mmol m22 s21 on 26 July. The instantaneous CO2
efflux rate could be as low as 0.43 mmol m22 s21 (17
November, collar 4_2) and as high as 5.15 mmol m22 s21
(on 19 July, collar 3_1). The range of the daily average CO2
efflux rate varied between 0.24 and 2.59 mmol m22 s21 (on
15 November and 31 May, respectively), with greater
variations occurring in summer when the efflux rate is
greatest (Fig. 4). SWC limitation only occurred during short
periods in summer, when the SWC dropped below
0.2 m3 m23 in the organic layer, and the soil CO2 efflux
rate was reduced despite high soil temperatures.
Typical daily courses of the CO2 efflux rates are plotted
in Fig. 5, showing a marked increase from about 2 h after
sunrise to about mid-afternoon and a slow decline throughout the night until the following morning. Soil CO2 efflux
rates usually peaked well after midday but before the
maximum temperature at 5 cm depth was recorded. Daily
time courses of CO2 efflux were generally continuous but
could show considerable variation between hourly readings
(as, for example, on the afternoon of 20 August in Fig. 5).
Flux measurements from all five collars collected within
1 h were aggregated, thus yielding a temporally and
spatially averaged soil CO2 efflux estimate. Since all
chambers were moved between collars of a location
simultaneously, the resulting averages represented three
different spatial averages (for collars 1, 2, and 3 of all
locations, respectively). Temperature regressions (using
daily averages of CO2 flux and temperature and Eq. (3)) for
data obtained under conditions of no soil water limitation
(see below) showed that the three spatial averages did not
differ significantly from a regression using all data (X 2 of
1473
grouped averages ¼ 0.050, X 2 of collar-averages ¼ 0.072,
0.042 and 0.038, with n ¼ 65; 23, 21 and 21, respectively),
so that all flux averages could be treated as a true spatial
average of the stand.
3.1.2. Temperature and soil water content dependence
Parameter fits were performed for the temperature
dependence functions alone, as well as for all combinations
of temperature and SWC functions
FðsoilÞ ¼ fðTÞ fðSWCÞ ;
ð9Þ
where fðTÞ is one of Eqs. (3) –(5), and fðSWCÞ takes the value 1
or is one of Eq. (6) or (7). Best fits of hourly flux-average
data to Eqs. (3) –(5) were achieved for the soil temperature
at 5 cm depth. All three temperature response functions
fitted the data well, and multiplying each of the temperature
functions by one of the SWC limitation functions improved
the fit to the data (Table 1).
Out of all nine regression models indicated in Table 1,
the combination of the Lloyd and Taylor (1994) temperature
model and the SWC model after Bunnell et al. (1977) (Eqs.
(3) and (6)) were chosen as for further data analysis, due to
the slightly better value for the adjusted coefficient of
correlation (adj. r 2).
3.1.3. Interactions between the temperature and moisture
dependence
The results presented in Table 1 clearly show the
dependence of soil CO2 efflux on the SWC. In order to
test whether the temperature dependence of the CO2 efflux
in turn depends on the SWC, the hourly efflux averages were
divided into SWC classes (between 0.20 and 0.32 m3 m23,
SWC classes had a width of 0.01 m3 m23, above and below
this range, classes contained a wider range of values to
allow sufficient numbers of data points for regression
Fig. 5. Soil CO2 efflux rate (symbols and left axis) and soil temperature profile (lines and right axis) measured over 4 d in August 1999. Data are instantaneous
flux readings from all three collars at location 4 (i.e. collars 4_1, 4_2, and 4_3).
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Table 1
Parameters and coefficients of determination for all combinations of
temperature and soil water content dependencies
Moisture limitation
function
plotted, with SWC1/2 ¼ 0.172 m3 m23 multiplied by a value
of Rref ¼ 3:57 (both values are taken from the temperature
and SWC regression in Table 1).
Temperature function
Lloyd and Taylor
Q10
Linear
None
Rref
T-par
adj. r 2
2.05 ^ 0.01
304 ^ 8
0.72
2.03 ^ 0.01
2.61 ^ 0.07
0.70
2.10 ^ 0.01
0.199 ^ 0.004
0.74
Gompertz
Rref
T-par
a
b
adj. r 2
2.65 ^ 0.13
403 ^ 8
0.364 ^ 0.079
8.38 ^ 1.13
0.83
2.58 ^ 0.11
3.64 ^ 0.10
0.452 ^ 0.086
8.09 ^ 1.14
0.82
2.60 ^ 0.12
0.286 ^ 0.015
0.167 ^ 0.107
7.90 ^ 1.45
0.82
Bunnell
Rref
T-par
SWC1/2
adj. r 2
3.57 ^ 0.13
403 ^ 8
0.172 ^ 0.015
0.83
3.66 ^ 0.15
3.65 ^ 0.10
0.188 ^ 0.017
0.82
3.22 ^ 0.10
0.355 ^ 0.014
0.116 ^ 0.010
0.82
‘T-par’ refers to the respective parameters of the temperature sensitive
parts of Eqs. (3)-(5), all other parameters are the same as for Eqs. (3) –(7).
The coefficient of determination has been adjusted for the respective
numbers of parameters; n ¼ 822 for all regressions
analysis; Fig. 6) and temperature regressions (Eq. (3)) were
performed for each of these classes. The model parameter
Rref represents the soil CO2 efflux rate at 10 8C, i.e. it gives a
measure of the magnitude of the efflux and is temperature
insensitive, whereas the parameter E0 indicates the
sensitivity of the efflux to temperature changes. By plotting
both parameters against the different SWC classes, a clear
trend in the magnitude of Rref is revealed, while the
temperature sensitive parameter shows no trend with
changing SWC (Fig. 6). The curve according to the SWC
limitation model by Bunnell et al. (1977) (Eq. (6)) is also
3.1.4. CO2 soil efflux due to pressure pumping
The hypothesised influence of vertical air pumping on the
CO2 efflux from the soil acts only on the gas transport from
the uppermost soil layer and not on the production of CO2.
Since the pressure fluctuations therefore only affect the
instantaneous efflux situation, it was not deemed useful to
average fluxes and environmental variables over any length
of time. Best-fit regressions were applied to the instantaneous soil CO2 efflux data for each collar separately. Only
valid flux data for periods when wind-data was available
were included. The regression model took the form of Eq. (2),
with Eq. (3) for fðTÞ ; Eq. (6) for fðSWCÞ and Eq. (8) for fðuÞ : In
order to compare the effect of wind forcing on the efflux data,
a second set of regressions were performed with fðuÞ set to
zero. Variables used in the regression are the soil temperature
at 5 cm depth and SWC of the organic layer for T and SWC,
respectively, and the standard deviation of the horizontal
wind-speed 10 min previous to an efflux reading for su:
As can be seen from Table 2, only about half of the
chambers show a slight improvement in the value of the
adjusted r 2 if fðuÞ is included (‘adj. r 2 ðuÞ’ compared to
‘adj: r 2 ðu ¼ 0Þ’). Strikingly, all collars located in D.
flexuosa patches (locations 3 and 4) show a better fit for
regressions including the wind function, while most of the
remaining collars show little or no improvement in the
coefficient of determination. The value of c was significantly different from zero for collars 3 and 4, suggesting that
here an increased variability in wind speed is indeed
positively correlated with the soil CO2 efflux rate.
Fig. 6. Variations in the regression parameters Rref (triangles and left axis) for T05 ¼ 10 8C and E0 (circles and right axis) for different SWC classes. The line is
the effect of SWC limitation found for simultaneous regression of T and SWC (hatched grey line: Rref ¼ 3:57 £ ðSWC=SWC þ 0:172Þ;; compare Table 1).
Error bars are standard errors of the parameter estimation.
1475
2.82
353
0.102
0.028
0.10
14
0.012
0.097
226
0.82
0.82
3.2. Spatial variation
a
The last row shows values of the adjusted r 2 for regressions with f ðuÞ set to zero (parameters of these regressions not shown).
In the nomenclature of collars, the first number indicates the chamber location (1_5 as in Fig. 1), and the second number indicates the collar within each chamber location (1_3).
2.32
348
0.068
20.332
0.10
17
0.016
0.105
251
0.77
0.76
3.17
413
0.253
0.064
0.20
15
0.035
0.091
250
0.85
0.85
2.19
349
0.081
0.536
0.09
22
0.014
0.122
272
0.67
0.645
2.23
404
0.141
0.616
0.13
19
0.024
0.092
295
0.81
0.78
2.63
380
0.192
0.869
0.21
22
0.039
0.140
272
0.72
0.67
3.18
198
0.039
0.296
0.09
17
0.008
0.133
241
0.41
0.39
2.45
362
0.041
0.266
0.09
15
0.011
0.107
216
0.78
0.77
Rref
E0
SWC1/2
c
SE Rref
SE E0
SE SWC1=2
SE c
n
adj. r 2 ðuÞ
adj. r 2 ðu ¼ 0Þ
2.60
310
0.094
0.331
0.11
20
0.016
0.116
222
0.58
0.56
2.86
383
0.103
20.047
0.07
10
0.009
0.066
300
0.89
0.89
2.23
362
0.063
0.014
0.10
19
0.014
0.117
228
0.74
0.74
2.45
383
0.059
0.054
0.10
23
0.013
0.119
223
0.62
0.62
2.21
391
0.034
20.056
0.07
19
0.008
0.101
210
0.82
0.82
4.83
271
0.228
0.671
0.33
23
0.038
0.181
204
0.55
0.525
2.77
305
0.105
0.416
0.12
19
0.018
0.136
234
0.58
0.56
4_1
2_1
1_3
1_2
1_1
Collara
Table 2
Regression parameters of each of the soil collars for FðT;SWC;uÞ
2_2
2_3
3_1
3_2
3_3
4_2
4_3
5_1
5_2
5_3
J.-A. Subke et al. / Soil Biology & Biochemistry 35 (2003) 1467–1483
Direct comparisons between soil respiration rates at
different collars were not possible, since only five collars
were measured simultaneously. In order to test for
characteristic differences between the sampling locations,
the instantaneous flux rate of CO2 from each collar at
between 9.5 and 10.5 8C from the entire growing season
(excluding periods when SWC became limiting) were
compared. A total of 523 measurements had been
conducted within this temperature range (between 80
and 121 for each of the locations), and no effect of
hysteresis (i.e. whether temperature previous to the
measurements had been either above or below the
temperature range) was detectable.
CO2 efflux rates of locations 1 and 3 were found to differ
significantly from all other locations, and from each other
(1-way ANOVA and Tukey test, F4500 ¼ 92:1; P , 0:05),
while locations 2, 4 and 5 show virtually identical rates. No
significant difference due to the ground vegetation type (also
indicated in Fig. 7) was apparent for data pooled for either
collars or locations.
3.3. Annual soil CO2 efflux for Weidenbrunnen 2
Fig. 8 shows the course of the soil temperature, SWC
and the soil CO2 efflux rate calculated using Eqs. (3) and
(6) with the parameters stated in Table 1. The regression
of all soil CO2 efflux data obtained for SWC . 0.2
against Eq. (3) yielded the parameters Rref ¼ 2:09 ^ 0:01
and E0 ¼ 354 ^ 8 (n ¼ 702; adj. r 2 ¼ 0.82). A comparison of the efflux modelled from the two regressions
illustrates the effect of SWC limitation in the summer
months (Fig. 9).
Based on the temperature and SWC regression results,
the total soil CO2 efflux over the 4-yr-period could be
estimated. Given the extent of the available soil
temperature data, annual totals were calculated from 1
April of each year to 31 March of the following year,
and annual sums are given in Table 3.
According to the rainfall data supplied by BITÖK,
1997 was an extremely dry year. Comparison with
rainfall sums recorded over 14-d intervals showed that
while the annual precipitation sum was lower than in all
previous and following years, technical problems with
the rainfall instruments contributed to considerable underestimations of the annual total. The discrepancies
between the hourly data (on which the SWC model is
based) and the alternative fortnightly measurements
occurred in winter and between late June and late July.
During the 4 weeks between 26 June and 22 July, the
SWC model under-estimated actual SWC by an equivalent of about 38 mm rainfall. The prolonged water stress
indicated for the second half of 1997 (Fig. 8), however,
is consistent with the 14-d interval rainfall data.
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J.-A. Subke et al. / Soil Biology & Biochemistry 35 (2003) 1467–1483
Fig. 7. Average soil respiration rates at around 10 8C of the five sampling locations. Error bars indicate 95% confidence intervals, means with the same letters
are not significantly different ðP ¼ 0:05Þ:
Fig. 8. Four year time course of (a) soil temperature (black line and left axis) and SWC (grey line and right axis), and (b) the modelled soil CO2 efflux rate.
J.-A. Subke et al. / Soil Biology & Biochemistry 35 (2003) 1467–1483
1477
Fig. 9. Modelled soil CO2 efflux for 1999. Grey line: using temperature and SWC data with regression parameters as stated in Table 1 (for Lloyd and Taylortype temperature and the Bunnell type SWC dependence); black line: using temperature data only and regression results as stated in the text.
4. Discussion
4.1. Fundamental temperature dependence
of soil CO2 efflux
A positive relationship between soil temperature and soil
CO2 efflux rate is well established (Lundegårdh, 1926;
Singh and Gupta, 1977; Raich and Schlesinger, 1992) and is
clearly reflected in the seasonal course of both variables in
Fig. 4. The causal link of this positive correlation by the
increased biological activity of both the autotrophic (roots,
e.g. Bouma et al., 1997) and the heterotrophic organisms
(microbial communities and soil dwelling animals) in the
soil and the increased diffusivity of CO2 under higher
temperatures is also undisputed. The exact nature of this
relationship, however, is less clear and receives considerable attention (see Janssens et al., 2002 for a review). Of all
existing empirical relationships, only three were tested on
the data collected during this study These are (1) an
Arrhenius type temperature response, (2) the Q10 concept,
and (3) a simple linear regression.
The Q10 relationship expresses the factor by which a
biochemical reaction increases when the temperature
increases by 10 8C. The wide use of this purely empirical
relationship in biological systems has been criticised by
Lloyd and Taylor (1994) who point out that Q10 functions
systematically overestimate fluxes at high temperatures. In
the modified Arrhenius relationship for temperature and soil
respiration proposed by Lloyd and Taylor, which was also
used in this study, the activation energy decreases with
increasing soil temperature. This soil respiration model (Eq.
(3)) has since been supported by other researchers (Fang and
Moncrieff, 1999; Rayment and Jarvis, 2000), but the Q10
concept is still widely used (Boone et al., 1998; Davidson
et al., 1998; Morén and Lindroth, 2000), in some cases
alongside the Lloyd and Taylor-model (Fang et al., 1998;
Buchmann, 2000). The regression results of both daily and
hourly averages of soil CO2 efflux obtained in this study
(Table 1) support the Lloyd and Taylor model as the more
realistic concept for soil CO2 efflux measurements.
The apparent good fit of a linear regression model
(Fig. 10) presumably stems from larger scatter at high soil
temperatures, where all functions describe the data
equally well, and the less represented measurements at
low soil temperatures, where the linear model underestimates the measured efflux. Depending on soil types
Table 3
Total annual C loss through soil CO2 efflux
1997
1998
1999
2000
Mean ^ st. dev.
CV
a
b
Annual mean T
(8C)
Precipitation
(mm)
T and SWC model
(g C m22 y21)
T only model
(g C m22 y21)
‘T’-‘T&SWC’
(g C m22 y21)
Over-estimate
(%)
5.9
6.0
6.4
6.8
6.0b
572a
1300
1170
945
1019b
497
566
592
586
560 ^ 43
7.8
588
581
602
618
597 ^ 16
2.7
91
15
10
32
37 ^ 37
15.5
2.6
1.5
5.3
6.2 ^ 6.3
The difference between the two regression results is also expressed as the proportion of the efflux calculated with the temperature regression.
Precipitation data for 1997 likely to under-estimate actual precipitation sums; see text for detail.
Mean of the annual temperature and annual precipitation are calculated for the years 1993–2000 (data supplied by BITÖK).
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J.-A. Subke et al. / Soil Biology & Biochemistry 35 (2003) 1467–1483
Fig. 10. Temperature dependence of soil CO2 efflux averaged over 24 h following the closing of the chamber lid. Open symbols are values obtained at average
SWCs of below 0.2 m3 m23. Regression curves are best fits for all data excluding conditions with SWC limitation; solid line: Lloyd and Taylor (1994), hatched
line: Q10, dotted line:linear function.
and different temperatures investigated, linear regressions
have been favoured to describe soil CO2 efflux (Anderson,
1973; Koizumi et al., 1999). As with the Q10 model,
however, it lacks a physiological basis and bears obvious
limitations for extrapolations within physiological scales,
which is certainly desirable for modelling purposes.
According to the parameters in Table 1, for example,
soil respiration would become negative for temperatures
below about 2 1 8C.
The presented soil CO2 efflux data therefore show that
the Lloyd and Taylor (1994) function, based on a
temperature insensitive efflux rate at a set temperature
ðRref Þ and a parameter representing the activation energy
ðE0 Þ that varies according to the soil temperature, is the
fundamentally soundest description of the relationship
between soil temperature and soil CO2 efflux. Therefore,
only the Lloyd and Taylor function was used for the further
analysis of the interaction between soil temperature and the
soil CO2 efflux.
4.2. Temperature profile within the soil
Of the factors influencing the soil respiration rate, soil
temperature shows the greatest short-term variation. The
surface efflux of CO2 is the result of the heterotrophic and
autotrophic activity from the entire soil profile, and depends
on the substrate quality and the environmental conditions at
all depths within this profile. The temperature within a given
soil layer depends on the temperature in adjacent soil layers,
air temperature and heating due to solar radiation. Greatest
temperature fluctuations appear at the soil surface, due to
seasonal and diurnal fluctuations in both radiation and air
temperature. By means of thermal diffusion these fluctuations propagate to deeper soil layers, resulting in a more
dampened and time-lagged temperature signal with
increasing depth (Fig. 5). That the most shallow temperature
sensor shows greatest diurnal variation is therefore obvious
and clearly documented in Fig. 5. Since the organic layer
with the greatest pool of easily decomposed C is hence
exposed to the environmental variable with greatest
fluctuation, the correlation between the CO2 efflux rate
and the most shallow soil temperature can be expected.
For the same forest plot, Buchmann (2000) found that
removing the litter and organic layer of the forest floor (i.e.
the top 13 cm of soil) led to no significant reduction in the
soil CO2 efflux rate, suggesting that the main source of
CO2 is located below these strata. Yet, the correlation
between temperature and soil CO2 efflux in the same study
was less at 15 cm depth than it was at 10 or 5 cm depth
(r 2 ¼ 0:70; 0.80 and 0.80, respectively). The only meaningful interpretation of these apparently contradictory results
in the same study is that the deeper soil layers contribute the
bulk of CO2 without any temperature sensitivity, and the
more shallow layers contribute a small but changeable and
temperature dependent portion of the CO2 flux. However,
looking at short-term variability of soil CO2 efflux found in
our study, peak efflux rates regularly exceed twice the nighttime values of within 24 h of measuring (data not shown, but
see Fig. 5). This pattern indicates that a large fraction of the
CO2 flux originates in those soil strata that are affected by the
diurnal temperature cycle (i.e. the litter and organic layer).
The absence of a reduction in efflux after removal of these
layers found by Buchmann (2000) is therefore likely to be
attributed to measuring artefacts due to disturbance of the
soil environment, and not a major contribution of the efflux
from the mineral soil.
Mariko et al. (2000), Davidson et al. (2000) and others
have pointed out the limitations of using a simple
temperature function from one soil depth only to describe
the process operating throughout the profile and is
J.-A. Subke et al. / Soil Biology & Biochemistry 35 (2003) 1467–1483
influenced by the heterogeneity of substrate and environmental factors. Splitting the temperature response function
into several components to represent the flux contribution
from different soil layers is one step towards a better
understanding of the origin of CO2 within the soil. However,
this would increase the number of parameters in a regression
model thus requiring more data points to yield a significant
regression result (Draper and Smith, 1981, p. 298,
recommend that the number of observations exceeds the
number of parameters 5- to 10-fold). Soil respiration
studies, especially those conducted using manually operated
closed chambers, often do not provide sufficient numbers of
observations to allow regressions of this kind. Despite
sufficient amounts of data obtained in our study, it was not
possible to extract information about compound fluxes from
two different soil depths, and the temperature measured at a
depth of 5 cm alone was found to be adequate for a
description of the surface CO2 flux. Closer analysis of the
interactions of variables showed a high degree of correlation
between the temperature signals from 5 and 10 cm depth
(r 2 ¼ 0:85; P , 0:001), resulting in these two temperatures
to effectively act as one single variable. The correlation
between the more shallow temperatures and the temperature
at 30 cm depth was less, but including the deeper
temperature for the modelling of the flux did not result in
a significantly improved explanation of soil surface efflux
variation.
However, there is evidence that the temperature closer to
the soil surface, where the most extreme short-term changes
in temperature due to changing direct and diffuse radiation
take place, show even greater correlation with the efflux
rate. The peak rates in Fig. 5 generally precede the peak of
the soil temperature curve at 5 cm. The most extreme peaks
are likely to result from times when the soil chamber was
exposed to relatively high radiation and the temperature at
the soil surface had increased considerably. Similar peaks
were observed for all collars and at various times of the day.
If one looks at measurements taken at the same collar over
the course of several weeks, the limitations of using just one
temperature for the correlation becomes evident. While an
1479
exponential regression gives a good fit for data of the entire
growing season, the actual correlation for measurements
taken within 24 h show a different dependence on soil
temperature at 5 cm (Fig. 11a and b).
A likely cause for this difference between short- and longterm temperature dependence is the correlation of the efflux
rate with the inappropriate temperature signal. That the
temperature signal is dampened due to thermal inertia with
increasing depth has already been shown. If one presumes
that the short-term variability in the efflux rate is due to the
temperature variation in the top-most layer alone, the
correlating temperature signal from a deeper soil layer
would have to be corrected for the signal-dampening. If the
efflux rates in Fig. 11b were plotted against a broader
temperature range, the daily temperature dependence curves
would resemble the seasonal temperature dependence more
closely. The magnitude of the flux, however, is still affected
by temperatures from all depths, so that if one wanted to
decrease the scattering around the annual regression line,
a more detailed knowledge of the soil temperature distribution, especially at the soil surface would be necessary.
4.3. Soil moisture limitations
Limitation of soil CO2 efflux due to either low or high
SWCs have been described previously, and a multitude of
regression models exist (Davidson et al., 2000; Janssens
et al., 2002). Drought stress occurs as water becomes
limiting for the normal metabolic activity of microbial
organisms or macroflora (Singh and Gupta, 1977). At the
opposite end of the optimal range for respiratory activity, a
reduction due to high SWCs may occur as water limits the
diffusion of gases in and out of soil pores. With no O2
available for the aerobic decomposition process, CO2
production is inhibited as well as its transport from the
soil pores to the atmosphere (Linn and Doran, 1984). No
limitation due to high SWC was observed at our site,
probably owing to adequate drainage by the mineral soil (a
sandy loam with a clay content of less than 5%). The
limiting effect of low SWC however is pronounced, and
Fig. 11. Dependence of the instantaneous soil CO2 efflux on soil temperature at 5 cm depth for one collar only. Data in (a) are from the entire growing season,
data in (b) are taken within 24 h for the dates shown (all measurements made in 1999).
1480
J.-A. Subke et al. / Soil Biology & Biochemistry 35 (2003) 1467–1483
the adequacy of the Bunnell model (Eq. (6)) for the
description is clearly documented in Fig. 6. Like most other
functions used to describe SWC limitations, this model is
empirically based. Papendick and Campbell (1981) show
that the mechanistically appropriate function scales the CO2
efflux with the cube of the SWC (Davidson et al., 2000).
Given an appropriate parameterisation, this model produces
virtually identical results to those of both the Bunnell and
the Gompertz model, so that in this study, the Bunnell
model, with only one fitted parameter, was favoured. The
exact relationship between SWC and the soil CO2 efflux rate
differs from one soil type to another (Howard and Howard,
1993), and is also likely to depend on adaptations by the soil
microbial communities to local climatic conditions. Severe
soil CO2 efflux limitations in more arid ecosystems, for
example, do not occur until the SWC drops below about
0.1 m3 m23 (Carlyle and Ba Than, 1988; Janssens et al.,
2000, 2003). Since drought stress is not common at the
Weidenbrunnen 2 site, there is only minor environmental
pressure for microbial communities to develop appropriate
adaptations. The value of about 0.2 m3 m23 for the
volumetric water content below which soil CO2 efflux
occurs is similar to those reported in other studies from
temperate and boreal regions of between 0.12 and
0.19 m3 m23 (temperate deciduous: Hanson et al., 1993;
Arneth et al., 1998; Davidson et al., 1998; boreal coniferous:
Gärdenäs, 2000). The logarithmic regression model describing soil water limitation of soil CO2 efflux in an Asian
steppe ecosystem (Chen et al., 1999) indicates that similar
adaptation processes act in quite different ecosystem types.
Carlyle and Ba Than (1988), Kutsch and Kappen (1997),
and Reichstein et al. (2002) have suggested a dependence of
the temperature sensitivity on SWC, a result that is not
supported by our findings. Since dry conditions usually
coincide with high soil temperatures, the effect of either
variable becomes confounded with the other (Davidson
et al., 1998). Given this dependence of variables, reduction
in temperature sensitivity may be due to an increase in
temperature (a well-established relationship, see above)
rather than the supposed soil moisture effect. Regressions
using the Arrhenius type model (which already incorporates
a decrease in temperature sensitivity with increasing
temperatures) showed for the data from Weidenbrunnen 2,
that the decrease in soil CO2 efflux can be explained purely
by a reduction in the (temperature insensitive) basal
respiration rate (Fig. 6). Moisture effects, on the other
hand, have also been observed on the E0 -parameter, so that
further experimental work is needed in order to clarify this
particular effect.
4.4. Soil CO2 efflux facilitation by wind forcing
Measurements of the soil CO2 efflux are very sensitive to
atmospheric pressure, and that static pressure fluctuations
cause a mass flow in and out of the soil pores and hence
increase the rate of diffusion of a gas from the soil has been
shown experimentally by Kimball and Lemon (1971).
Closed chamber systems generally exclude these natural
fluctuations to occur within the chamber space, so that no
investigation into these effects has been reported from
studies using this chamber type alone. Rayment and Jarvis
(2000) who also used an open chamber design found no
improvement of the description of the efflux data by
including the friction velocity in their model. The only
evidence of wind-induced pressure pumping stems from
micrometeorological measurements of soil respiration
(Baldocchi and Meyers, 1991; Arneth et al., 1998). Since
the causal mechanism of the mass flow in and out of the soil
pores is the variation in static air pressure, the direct use of
this variable is most desirable. Wind is the movement of air
due to gradients in air pressure, so that the variation in wind
speed (expressed as su) is a reasonable surrogate for static
pressure fluctuations ðspÞ: Correlations between sp and the
friction velocity (up ; used as a surrogate by Rayment and
Jarvis (2000)) and between sp and u (as used by Arneth
et al., 1998) were both weaker than for su (data not shown).
The effect of pressure pumping on the soil CO2 efflux is
likely to differ according to soil properties like soil bulk
density and soil pore sizes. Using the Bowen Ratio/Energy
Balance method and a closed dynamic soil chamber, Dugas
(1993) measured the CO2 flux from bare clay soil. The
reasonably good agreement between both methods with no
reported effect of wind speed indicates that the effect of
pressure pumping is negligible for this particular soil type.
For our data, an effect of pressure pumping could be
demonstrated for few of the 15 soil collars. However, there
appears to be a correlation between the type of ground
vegetation a collar was located in and the influence of static
pressure fluctuations: All six collars located in D. flexuosa
patches showed a higher coefficient of determination for the
function including wind forcing, compared to only two of
the remaining nine collars (Table 2). No aboveground parts
of the ground vegetation were present within the chambers,
but the litter and the Of layer were strongly influenced by the
respective ground vegetation types. The thickness of the
litter layer in the D. flexuosa patches varies between 2 and
5 cm, compared to 1 –1.4 cm for all other ground vegetation
patches. The bulk density of the litter layer was found to
range from 0.2 to 0.3 g cm23 for all sites, which is
considerably less than that of the organic and mineral soil
(1.5 and 1.7 g cm23, respectively). It is therefore plausible
that the effect of CO2 flushing from the soil pores due to
pressure fluctuations was only detected at those collars with
a more substantial layer of low bulk density (and hence a
greater pore volume).
The range of su recorded was 0.04 to 1.35 m s21
(mean ¼ 0.34, median ¼ 0.30, n ¼ 12 868), so that the
contribution of the pressure-induced efflux could be as large
as 1.17 mmol m22 s21 (by multiplying the maximum value
of su by 0.869, the largest value for parameter c in Table 2).
However, it would be too simplistic to associate a given
value of su with a specific flux contribution, since
J.-A. Subke et al. / Soil Biology & Biochemistry 35 (2003) 1467–1483
the meteorological conditions previous to the measurement
(on a scale of hours to days) would have to be accounted for
first. The flux contribution due to pressure pumping is likely
to be considerable for gusts of wind following a period of
relative calm, while it should be smaller for similar given
wind conditions if the soil pores have been ‘flushed’ by
pumping previously.
For the purpose of modelling the long-term soil CO2
efflux, however, it is not sensible to include pressure
fluctuations as an additional variable, since it only affects
the gas transport mechanism and not the CO2 production
within the soil. Averaged over time, therefore, the net
contribution of the pressure related transport would be zero.
However, our results show that measurements done by
chambers that do not measure under steady-state conditions
are likely to produce biased results owing to the present and
previous meteorological conditions. Similarly meteorological techniques such as eddy covariance, which require
minimum wind speeds and friction velocities to be
applicable, would over-estimate soil CO2 efflux, as only
the ‘flushing’ of CO2 from the soil would be recorded and
not the relatively low efflux rate following times of high
pressure fluctuations.
4.5. Annual soil CO2 efflux
The calculated soil CO2 efflux for the years 1997 to 2000
show considerable inter-annual variability (Table 3). During
years when annual precipitation sums are close to the longterm average (as for the years 1998 to 2000), SWC
limitation leads to a reduction of between 9 and 33 g
C m22 yr21 (or about 1.5 –5.5%) of the respiration sum
under conditions without SWC limitation. If longer periods
of SWC limitation occur, as in 1997, C loss from the soil is
reduced considerably. While the values stated in Table 3 are
likely to be an under-estimation of the annual rainfall, the
severity of the SWC limitation on the annual soil CO2 efflux
is obvious. The annual results of about 570 g C m22 yr21
for the years 1998 to 2000 compare well to the 560 ^ 17 g
C m22 yr21 reported by Raich and Schlesinger (1992) for
coniferous forests between 40 and 608 latitude. Buchmann
(2000) calculated an annual efflux of 710 g C m22 yr21 for a
neighbouring stand ‘Weidenbrunnen 1’ (a 47-yr-old dense
plantation of P. abies) based on measurements from 1998,
while the soil CO2 efflux rates in Weidenbrunnen 2 were
stated as even higher than in Weidenbrunnen 1, so that an
even greater annual sum would result. The theoretical efflux
rates reported by Buchmann with instantaneous flux rates of
1 mmol m22 s21 at 0 8C and about 5 mmol m22 s21 at 15 8C
for the Weidenbrunnen 2 site do not compare well with
those of our study (compare Figs. 2 and 8). The reason for
the higher estimate by Buchmann (2000) may be partly
explained by a misrepresentation of the actual stand soil
CO2 efflux due to a small number of sampling locations (in
the mentioned study ‘four to five’ collars were used in the
stand), or a systematic error of either of the sampling
1481
systems used. An intercomparison of chamber types
conducted within the framework of the EUROFLUX
programme (Lankreijer et al., 2003) showed that open
dynamic chamber systems produce generally lower efflux
estimates than closed dynamic systems. Given the intensive
validation of the open dynamic chamber used in our study
(Subke, 2002), and the higher total sampling area
(4712 cm2 ¼ 314 cm 2 £ 15 collars) in this study vs.
393 cm2 ( ¼ 79 cm2 £ 5 collars) in Buchmann (2000), the
lower estimate of around 580 g C m22 yr21 is likely to be a
more accurate representation of the actual annual efflux.
In most temperate ecosystems, SWC limitation only
occurs sporadically, and differs in frequency and duration
between years (Fig. 8). Sampling strategies that rely on
periodic measurements rather than continuous flux readings
therefore run the risk of under-estimating the effect of SWC
on soil CO2 efflux, if short periods of low SWC are not
sampled. The pronounced decrease in annual C loss from
the soil for 1997 illustrates the necessity to incorporate the
SWC sensitivity of soil CO2 efflux into global change
models. With global precipitation patterns changing along
with regional annual mean temperatures, an estimate of
future contributions to the total C budget by the soil is only
possible if the SWC sensitivity is known and can be
described mathematically.
4.6. Conclusion
The automated and continuous measurements with the
open dynamic chamber system have provided a powerful
basis for a comprehensive analysis into the factorial
dependence of soil CO2 efflux. The results provide detailed
information that can be used to parameterise ecosystem
models. Owing to the continuous soil CO2 efflux data, periodic
events such as sporadic drying of the top-soil were detected
and the SWC limitation could be included in the mathematical
description of the soil CO2 efflux. The considerable interannual variability in the C flux sums underlines the necessity
to include the effect of the SWC in ecosystem models for
humid as well as in arid and semi-arid ecosystems.
At the same time, the results also show in which areas
more research efforts have to be undertaken in order to
understand the dynamics of the C balance of ecosystems. A
simple extrapolation of the results would suggest that an
increase in temperature would result in higher CO2
production from the soil (assuming unchanged SWC
conditions). However, if the amount of organic C available
for microbial decomposition remains unchanged, the total
amount of CO2 efflux would remain constant. One study of
old SOM in boreal soils (Liski, 1997), for example, found
this particular fraction to be insensitive to temperature
changes. A more thorough analysis of specific rates of
decomposition for C pools of different stability within the
soil would be needed in order to predict the likely behaviour
of forest soils under a changed climate.
1482
J.-A. Subke et al. / Soil Biology & Biochemistry 35 (2003) 1467–1483
Acknowledgements
We would like to thank Jörg Gerchau and Gunnar
Lischeid of the Bayreuth Institute for Terrestrial Ecosystem
Research for supplying temperature and rainfall data. This
work was funded by the German Ministry for Education,
Science, Research and Technology (BMBF).
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