Footprint-Adjusted Net Ecosystem CO2 Exchange and Carbon Balance Components

Footprint-adjusted net ecosystem CO2 exchange and

carbon balance components of a temperate forest

Miklos T. Nagy 1, Ivan A. Janssens, Jorge Curiel Yuste 3,Arnaud Carrara 2, Reinhart Ceulemans *

University of Antwerp, Department of Biology, Research Group of Plant and

Vegetation Ecology, Universiteitsplein 1, B-2610 Wilrijk, Belgium

Received 24 August 2005; received in revised form 4 August 2006; accepted 7 August 2006

Abstract

We combined eddy covariance measurements of CO2 exchange with a suite of ecological methods to construct the carbon

balance of a mixed coniferous–deciduous forest in northern Belgium. The CO2 flux measurements were footprint-corrected to

eliminate all fluxes originating from outside of the study site, and the ecological measurements were up-scaled by weighting the

main vegetation types by their relative contribution to the footprint of the CO2 flux measurements. The footprint-corrected annual

net ecosystem exchange (NEE) was much lower than previously published u*-corrected NEE. Annual NEE ranged from �1.1 to

1.1 t(C) ha�1 year�1, and the forest ecosystem was a moderate CO2 sink with a mean annual rate of�0.3 t(C) ha�1 year�1 over the

investigated period (1997–2002). In 2001–2002, the mean NEE was�1.0 t(C) ha�1 year�1. However, despite this net CO2 sink, the

forest was losing carbon because carbon export via wood harvesting amounted to 1.2 t(C) ha�1 year�1. Also in 2001–2002, gross

primary productivity (GPP) calculated from the eddy covariance data was estimated to be 10.4 t(C) ha�1 year�1. Thus, of the

photosynthetically absorbed CO2, 90% was offset by respiration by plants and heterotrophs. The net primary production (NPP) in

the effectively contributing forest ecosystem amounted to 5.5–5.8 t(C) ha�1 year�1. Therefore, the NPP/GPP ratio was slightly

higher than the previously assumed fixed ratio of 0.47. These results highlight the importance of including management-related

carbon fluxes and of applying footprint corrections in carbon-balance studies.

# 2006 Elsevier B.V. All rights reserved.

Keywords: Carbon balance; Net ecosystem exchange; Footprint corrections

www.elsevier.com/locate/agrformet

Agricultural and Forest Meteorology 139 (2006) 344–360

1. Introduction

The increasing atmospheric CO2 concentration and

subsequent climate change have drawn the attention of

* Corresponding author. Tel.: +32 3 820 2256; fax: +32 3 820 2271.

E-mail address: [email protected] (R. Ceulemans).1 Present address: University College Dublin, School of Biology

and Environmental Science, Belfield, Dublin 4, Ireland.2 Present address: Fundacion CEAM, Parque Tecnologico, Calle

Charles R. Darwin, Paterna (Valencia) SP-46980, Spain.3 Present address: University of California-Berkeley, Ecosystems

Science Division, Department of Environmental Science Policy and

Management, 137 Mulford Hall, Berkeley, CA 94720-3114, USA.

0168-1923/$ – see front matter # 2006 Elsevier B.V. All rights reserved.

doi:10.1016/j.agrformet.2006.08.012

scientists worldwide on the carbon cycle. Hence, many

studies aim to determine the carbon balance of

terrestrial ecosystems at local to global scales, and to

predict changes herein under different climatic condi-

tions (Prentice et al., 2001). The net carbon balance of

an ecosystem is the result of several carbon flows that

act simultaneously, but often respond in contrasting

ways to changes in environmental factors (Goulden

et al., 1996). Moreover, spatial differences in species

composition, age, and management practices contri-

bute to the complexity and make the interpretation as

well as the spatial and temporal up-scaling very

difficult.

mailto:[email protected]

http://dx.doi.org/10.1016/j.agrformet.2006.08.012

M.T. Nagy et al. / Agricultural and Forest Meteorology 139 (2006) 344–360 345

The eddy covariance technique offers an indepen-

dent and useful tool to complement chamber-based

CO2-flux measurements. This technique, originally

proposed by Swinbank (1951), allows measuring the

net ecosystem CO2 exchange (NEE) between ecosys-

tems and the atmosphere directly (Desjardins, 1985;

Verma et al., 1986; Baldocchi et al., 1988; Falge et al.,

2001). The eddy covariance technique became widely

applied only in the second half of the 1990’s; therefore

the length of most long-term data sets averages around 7

or 8 years (Valentini et al., 2000).

When NEE, chamber-based CO2-flux measure-

ments, and field measurements of biomass growth,

litter production, etc. are assessed simultaneously, the

carbon balance of an ecosystem can be determined in

great detail. Moreover, the constructed carbon balance

becomes more reliable, because these independently

collected measurements provide mutual constraints.

However, before eddy covariance-based fluxes can be

compared to up-scaled ecological measurements, two

major difficulties have to be overcome:

(1) T
he eddy covariance measurements must originate
from the ecosystem under investigation.

(2) W
e have to be able to correctly identify the surface
area that is actually measured by the eddy

covariance technique. This area is commonly

referred to as ‘‘footprint’’ and changes with wind

direction, wind speed and stability (Schmid, 1997).

An accurate comparison of eddy covariance and other

ecological measurements thus requires both footprint

correction of the eddy flux data set (to exclude all fluxes

originating from outside of the ecosystem under study)

and up-scaling of the ecological measurements in

different areas proportional to their relative contribution

to the footprint of the eddy covariance system.

The eddy covariance technique can be deconvoluted

into its main contributing fluxes: gross primary

productivity (GPP) and total ecosystem respiration

(TER; Falge et al., 2002; Carrara et al., 2004). Waring

et al. (1998) reported that in forest ecosystems net

primary productivity (NPP) is proportional to GPP

(approximately 50%). However, in their study, gross

primary productivity was not measured but estimated

indirectly. Although the assumption of a constant NPP

to GPP ratio has been applied in stand growth-models

with promising results (e.g., Battaglia and Sands, 1997;

Landsberg and Waring, 1997), other studies questioned

this assumption. Makela and Valentine (2001) suggested

that a significant decline in the NPP to GPP ratio with

tree size or age seems highly probable, especially in

even-aged forests. Other ecosystem models suggested

that the NPP to GPP ratio is not constant, but is confined

to a narrow range (Thornley and Cannell, 2000). None-

theless, all of these studies agreed that the assumption of a

fixed NPP to GPP ratio needs verification (or rejection) in

studies where both NPP and GPP are measured indep-

endently within the same ecosystem.

Previous analyses of the 6 years long eddy covariance

dataset (Carrara et al., 2004) suggested that this study site

fluctuated between a negligible CO2 sink and a moderate

CO2 source, but these analyses were not footprint-

corrected nor compared with independent ecological

measurements. The main objectives of this study are

therefore: (1) to construct a detailed footprint-adjusted

carbon balance of a mixed, temperate forest ecosystem

for the period 2001–2002; and (2) to test the hypothesis of

a constant NPP to GPP ratio, by comparing eddy

covariance-based GPP estimates with independent NPP

estimates scaled-up to the same footprint area.

2. Materials and methods

2.1. Site description

The study site is a mixed coniferous/deciduous forest

located in Brasschaat in the Belgian Campine region

(5181803300N, 483101400E). The site is a level-II

observation plot of the European ICP-Forests network

(International Cooperative Programme on Assessment

and Monitoring of Air Pollution Effects on Forests in

the ECE-Region, UNEP-UN/ECE) and is also part of

the CARBOEUROPE flux towers network (Valentini

et al., 2000). The landscape is a coastal plain at a mean

elevation of 16 m and is almost flat (slope <0.3%). The

climate is temperate maritime with a mean annual

temperature of 9.8 8C and 750 mm of annual precipita-

tion. The soil is loamy sand, moderately wet, with a

distinct humus and iron B-horizon (Baeyens et al.,

1993), and is classified as Umbric Regosol (FAO

classification; Roskams et al., 1997). A clay layer lies

below the upper sandy layer at a depth of 1.5–2 m. Due

to this clay layer, the site has poor drainage, and

groundwater depth usually is between 1.2 and 1.5 m

(Baeyens et al., 1993). A more detailed description of

the physical and chemical description of the soil is

available (Janssens et al., 1999; Neirynck et al., 2002).

This relatively small (150 ha) forest consists of many

mono-specific patches of different coniferous and

deciduous species, with a variety of under storey

species as well. Scots pine (Pinus sylvestris L.) covering

about 80% of the coniferous plots and pedunculate oak

(Quercus robur L.) also covering 80% of the deciduous

M.T. Nagy et al. / Agricultural and Forest Meteorology 139 (2006) 344–360346

plots, are the dominant over storey species. The

undergrowth is dominated by black cherry (Prunus

serotina Ehrh.), rhododendron (Rhododendron ponti-

cum L.) and grass (Molinia caerulea L. Moench). A

more complete description of the forest, with vegetation

composition of the various patches has been previously

published (de Pury and Ceulemans, 1997; Janssens

et al., 2000).

The forest stands in the investigated area were

relatively even-aged and had similar characteristics. The

Scots pine stands were all planted in 1929 and were

characterized by a mean stand density of 361 trees ha�1,

mean tree height of 21.4 m, mean diameter at breast

height (DBH) of 0.29 m and mean stem basal area of

24 m2 ha�1. The pedunculate oak stands were slightly

younger (planted in 1936) and had a mean stand density

of about 310 trees ha�1, mean tree height of 17.2 m,

mean DBH of about 0.24 m and stem basal area of

14 m2 ha�1 (Curiel Yuste et al., 2005b; Xiao et al., 2003).

The area-weighted maximum leaf area index (LAI) for

the entire forest, including both over and under storey

LAI was about 3 m2 m�2 (Gond et al., 1999).

This managed forest is bordered by urban area on the

north and west, and by rural area (mostly forested

terrain) on the south and east sides (Fig. 1). The shortest

fetch (about 500 m) to the measuring tower is in the

western sector and a larger fetch (1000 m) is toward the

east side. The main wind direction at the site is

Fig. 1. Aerial photo of the surroundings around the eddy covariance tower. (

border of the forest, (- - -) indicates the approximate border of the wind se

southwest, the roughness length of the forest is about

1 m and the zero plane displacement is 19.2 m (Carrara

et al., 2003).

2.2. Instrumentation and measurements

The net carbon dioxide exchange between the forest

ecosystem and the atmosphere was calculated from

turbulence measurements on top of a 40 m tall flux

tower located in the experimental forest (Fig. 1) using

the eddy covariance technique. The instrumentation on

the flux tower included a three-dimensional sonic

anemometer (Model SOLENT 1012R2, Gill Instru-

ments, Lymington, UK) for wind speed, wind direction

and temperature measurements, and an infrared gas

analyzer (IRGA) (Model LI-6262, LI-COR Inc.,

Lincoln, NE, USA) for CO2 concentration measure-

ments. The data were logged at 20.8 Hz and fluxes were

computed in real time using the EDISOL software

(Moncrieff et al., 1997) and stored as half-hourly

values. More detailed information can be found in

Carrara et al. (2003, 2004).

Continuous meteorological measurements were car-

ried out with additional instruments on top of the tower.

The measured parameters were global radiation (pyr-

anometer, Kipp and Zonen, type CM6B, Delft, The

Netherlands), net radiation (REBS 07, Seattle, WA,

USA), photosynthetically active radiation (PAR quantum

) Indicates the location of the tower, (—) indicates the administrative

ctors.


sensor, JYP-1000, SDEC, Tours, France), precipitation

(tipping-bucket rain gauge, Didcot DRG-51, Didcot

Instrument Co. Ltd., Abingdon, UK), relative humidity

and temperature (psychrometer, Didcot DTS-5A, Abing-

don, UK). The measured data were stored as half-hourly

means on a data logger (Campbell CR 10, CSI, Logan,

UT, USA). Temperature in the soil was measured at 2 and

9 cm depth (Didcot DPS-404, Abingdon, UK). More

details about the instruments and methods can be found in

Overloop and Meiresonne (1999), Kowalski et al. (2000),

and Carrara et al. (2003).

2.3. NEE calculations

The calculation of eddy covariance fluxes and the

processing of data correction were made according to

the guidelines of the standard EUROFLUX methodol-

ogy (Aubinet et al., 2000). The calculations and flux

analyses for the forest over the period 1997–2002 were

carried out and discussed in detail in two earlier

publications (Carrara et al., 2003, 2004). In these

papers, however, data were not selected based on a

footprint analysis.

Fig. 2. Spatial contribution to the total flux in case of different quality classe

conditions, when 0.0625 > j > �0.0625; unstable conditions, when �0.062

corrected footprint was obtained by applying differently strict thresholds fo

2.4. Footprint analysis and database correction

Our footprint calculation is based on the work of

Rebmann et al. (2005). In that study, the results of the

footprint calculations were associated with the quality

assessment of turbulent flux data. Rebmann et al. (2005)

provided for each of the investigated CARBOEURO-

FLUX sites three two-dimensional matrices corre-

sponding to three different stability classes: stable

conditions (j > 0.0625), neutral conditions (�0.0625

< j < 0.0625) and unstable conditions (j < �0.0625).

The stability parameter j was calculated as:

j ¼ zm � d

L(1)

where zm is the observation height (42 m), d the zero

plane displacement (20 m) and L is the Obukhov-length

(Wyngaard et al., 1971).

The grid cells of the matrices represent 150 m �150 m squares surrounding the tower and the value of

each cell shows its relative contribution to the total flux

(Rebmann et al., 2005). The three footprint maps for our

study site are shown in Fig. 2. It is obvious from these

s of turbulent flux data as: stable conditions, when j > 0.0625; neutral

5 > j. j is the stability parameter of the atmospheric conditions. The

r j in different wind sectors (see Section 2).


graphs, that under stable conditions the majority of the

fluxes originate from outside of the studied area, while

under unstable conditions fluxes originate from within

the forest boundaries. Thus, we selected our data set as

follows:

The surroundings around the flux tower were divided

into 12 wind sectors (about 308 range each) (Fig. 1). In a

few sectors all data were rejected either because of an

insufficient fetch or an undesired vegetation type (e.g.

grassland, or recently afforested area; Table 1). In each

of the remaining wind sectors all data obtained under

stable conditions were rejected. In these sectors,

different j thresholds were applied according to the

fetch in that direction. Where the fetch was large

enough, also fluxes obtained under neutral conditions

were maintained. Where the fetch was intermediate, the

j threshold was set at 0 (Table 1).

Where sector borders split a matrix cell, the matrix

values were divided proportionally to the area of the

matrix cell within each wind sector. The use of different

j thresholds depending on available fetch and vegeta-

tion composition modified both the footprint and the

flux data set, and the resulting fluxes were more

representative for the investigated forest. We were

afraid that eliminating data obtained under stable

conditions would induce a systematic bias, but we did

not observe substantial association between weather

patterns and stability. Hence, we assume that this filter

induced no systematic bias in the data.

2.5. Gap filling and annual NEE

The gap filling method applied on this j-modified

data set was based on the gap filling procedures

recommended by Falge et al. (2001). The data set of

each year was separated into two-monthly periods

(January–February; March–April; . . .) and nonlinear

regressions fitted to the data. Daytime and nighttime

data were analyzed separately.

Table 1

Criteria for dismissing CO2 flux data in different wind sectors of the fores

Wind sector Criterium of applied data

0–298 No data

30–2098 j < 0.0625

210–2408 j < 0

241–3008 No data

301–3328 j < 0.0313

333–3598 No data

Criteria were determined on the basis of the stability parameter (j) of the CO

each sector.

Daytime data were sorted in 5 8C-wide temperature

classes and light response functions were evaluated

using a modified form of the Michaelis–Menten

equation (Michaelis and Menten, 1913), with the same

modification as in Carrara et al. (2003):

FNEE ¼a0Rg

1� ðRg=1000Þ þ ða0Rg=FGPP;optÞ� FRE;day

(2)

where FNEE is the daytime flux (mmol CO2 m�2 s�1), a0

the ecosystem quantum yield (mmol CO2 J�1), Rg glo-

bal radiation (W m�2), FGPP,opt the optimum gross

primary production (mmol CO2 m�2 s�1) at a Rg value

of 1000 W m�2 and FRE,day is the ecosystem respiration

during the daytime (mmol CO2 m�2 s�1).

To evaluate the temperature response of nighttime

CO2 fluxes, we fitted the following exponential function

to the data:

FRE;night ¼ r0 eðbTÞ (3)

where FRE,night is the nighttime respiration

(mmol CO2 m�2 s�1), b the fitted temperature sensitiv-

ity parameter, T air temperature (8C) and r0 is the

ecosystem respiration (mmol CO2 m�2 s�1) at 0 8C.

Gaps of more than 2 weeks long were filled using

non-linear regressions (with T for FRE,night and with Rg

for FNEE) created from 6 years pooled data of the same

period of other years. The annual rate of net carbon

ecosystem exchange (NEE) (t(C) ha�1 year�1) was

calculated from the gap filled half hourly CO2 flux

data FNEE and FRE,night (mmol m�2 s�1).

2.6. Total ecosystem respiration

Total ecosystem respiration (TER) was estimated

based on the assumption that dark respiration during

nighttime and daytime follow the same temperature

response. Nighttime respiration responses to air

t

Reasons

Extensive grassland in the sector

Moderately short fetch (600 m)

Less than 500 m fetch and undesired vegetation

Moderately short fetch (600 m)

Extensive grassland in the sector

2 flux data, as well as on the vegetation type and the available fetch in


temperature were fitted in two-monthly periods. The

temperature response was determined from the para-

meters of Eq. (3), and the daytime half-hourly mean air

temperature was then substituted into the equation to

obtain the daytime respiration flux. Annual TER (TER)

was finally obtained as:

TER ¼X6

k¼1

ðRnighttime þ RdaytimeÞk (4)

where Rnighttime and Rdaytime are the two-monthly

sums of nighttime and daytime respiration values,

respectively, and k indicates a bimonthly period.

Because our objective was to compute the carbon

balance for the 2001–2002 period, TER values for

2001 and 2002 were computed separately and subse-

quently averaged.

2.7. Gross primary production

Annual rate of gross primary production

(GPP = GPP) was obtained from the equation:

GPP ¼ TER � NEE (5)

GPP for the reconstructed carbon balance was

calculated as the mean of the GPP values of 2001

and 2002.

2.8. Effect of footprint correction on NEE

An earlier NEE estimate was produced for this forest

over the 1997–2002 period by Carrara et al. (2003,

2004), where fluxes were not discarded based on

footprint criteria, but on a friction velocity (u*)

threshold of 0.2 m s�1. The difference between this

earlier and our current NEE estimate was analyzed

statistically as follows. Two-monthly NEE values were

calculated for both u*- and footprint-corrected fluxes.

Daytime and nighttime NEE were addressed separately.

Differences between the u*- and footprint-corrected

data sets were tested for significance with a t-test at the

P < 0.05 level.

2.9. The effectively contributing ecosystem

(footprint weighted)

The forest ecosystem surrounding an eddy flux

tower is hardly ever perfectly homogeneous. Species

composition and leaf area index usually differ in diff-

erent areas around the tower. Therefore, the ecosystem

that is effectively influencing the eddy covariance

system is determined by turbulence characteristicsand

by the geographic distribution of vegetation types. As

the relative occurrence of different wind directions and

wind speed varied seasonally and inter-annually, also

the vegetation measured by our eddy covariance system

varied seasonally and inter-annually (Table 2). For a

more reliable assessment of the ecosystem represented

by our data set, we therefore computed the ‘‘effectively

contributing ecosystem’’ in which the contribution of

the different vegetation types varied seasonally and

inter-annually. This effectively contributing ecosystem

method was applied to determine not only the

vegetation composition of the ecosystem represented

by the eddy flux data set, but also for the spatial up-

scaling of all other ecological measurements.

2.10. Net primary production, litter fall, biomass

Detailed inventories of standing biomass were

made in one representative oak and one representative

Scots pine stand in the winters of 2000–2001 and of

2003–2004. We assume that these plots were

representative for all other pine and oak plots.

Because all other plots were of similar age and the

soil is relatively homogeneous, this assumption was

not unreasonable. Because oak and Scots pine

represented the vast majority of the over storey

composition, we further assumed that all other

deciduous or coniferous species had similar growth

rates. The estimate of the carbon stored in the standing

biomass reported in the carbon balance represents the

calculated winter 2001/2002 condition. Thus, the

deciduous species were calculated without foliage

biomass, and both oak and pine biomass were

calculated with fine root biomass measured and repo-

rted for February 2003 in both stand types (Konopka

et al., 2005).

Annual net primary production (NPP) was calculated

by combining changes in standing biomass, litter fall

and fine root productivity over the period concerned.

Productivity of stems was determined with two different

methods: (1) by establishing allometric relationships

between stem biomass and diameter at breast height

(DBH; Curiel Yuste et al., 2005a). The difference in

standing stem biomass between repeated forest inven-

tories (2000, 2001, 2003) was then assumed as stem

NPP. (2) with species-specific growth tables (Jansen

et al., 1996). These yield tables are empirical tables used

by forest managers to estimate aboveground wood

volume and current wood increment in standing forests.

As soil and other environmental factors determine

productivity of a forest tree species at a specific

location, several yield classes (ranging from optimal to

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Table 2

Annual and seasonal variation in the vegetation composition of the footprint-weighted ecosystem

Over storey Pinus sylvestris Other coniferous Quercus robur Other deciduous GrassP

Under storey Prunus Rhodo Molinia Org. lay. Total Prunus Molinia Org. lay. Total Prunus Rhodo Endemic Org. lay. Total Prunus Org. lay. Total

Annual variation

1997 31.6 6.1 10.9 10.7 59.3 2.6 0.5 9.8 13.0 8.1 1.6 2.4 8.9 21.0 0.8 3.8 4.6 2.1 1001998 32.7 4.8 8.9 10.2 56.6 2.4 0.3 10.7 13.4 8.6 1.5 2.8 10.2 23.1 0.8 3.5 4.3 2.6 1001999 30.0 4.5 10.7 9.7 54.8 2.9 0.7 10.7 14.2 8.6 2.0 2.0 10.3 22.9 0.7 5.0 5.8 2.4 1002000 35.0 4.8 8.8 9.4 58.0 2.0 0.5 9.4 11.9 9.3 1.5 2.9 10.5 24.2 0.9 2.5 3.4 2.5 1002001 29.4 4.5 10.8 9.7 54.5 2.7 0.7 11.1 14.6 7.6 2.0 2.2 10.5 22.3 0.6 4.9 5.5 3.2 1002002 29.1 4.3 11.2 9.6 54.2 3.6 0.5 12.5 16.5 7.3 1.9 2.2 8.8 20.2 0.6 6.1 6.7 2.4 100

Seasonal variation

Months 1–2 39.8 3.2 7.1 6.6 56.7 2.3 0.4 9.9 12.6 10.1 1.5 3.7 9.7 25.0 1.1 2.6 3.6 2.1 100Months 3–4 32.3 4.8 10.0 9.6 56.7 2.6 0.5 10.7 13.7 8.2 1.7 2.6 9.8 22.3 0.7 3.8 4.6 2.8 100Months 5–6 29.7 4.8 11.5 9.8 55.8 2.5 0.7 11.1 14.4 7.6 2.0 1.7 9.5 20.8 0.6 4.8 5.4 3.7 100Months 7–8 31.2 4.8 11.5 9.5 57.0 2.3 0.6 11.7 14.6 7.6 1.8 1.4 8.0 18.8 0.7 4.4 5.1 4.5 100Months 9–10 33.0 4.8 9.2 9.8 56.9 2.8 0.4 9.7 12.9 9.6 1.6 2.8 10.4 24.4 0.9 3.8 4.7 1.2 100Months 11–12 25.3 6.0 10.7 12.4 54.3 3.5 0.5 10.9 14.9 7.1 1.9 2.8 11.7 23.6 0.4 5.9 6.3 0.9 100

Mean 2001–2002 29.3 4.4 11.0 9.7 54.4 3.1 0.6 11.8 15.5 7.4 2.0 2.2 9.6 21.2 0.6 5.5 6.1 2.8 100

All values are in % of total. The footprint-weighted ecosystem was calculated by weighting the vegetation composition of every wind sector (combining over storey and under storey) with the

proportion of data originating from each particular sector.


extremely poor) have been developed for each species.

Yield classes are tree species-specific at a given site and

can be determined from stand age, mean DBH and mean

height. Current aboveground wood increment can then

be estimated from the empirical yield table. This results

in a fairly correct estimation of the current wood

increment rate of even-aged stands, in particular in the

case of middle-aged forests.

For branch NPP, we also applied allometric relation-

ships (Curiel Yuste et al., 2005a) to determine the

change in standing biomass, and summed this with the

measured fine- and coarse branch litter fall. Above-

ground litter fall (leaves, cones and branches) was

measured from January 2001 until December 2003

(Curiel Yuste et al., 2005a). We used the average of the 3

years’ results for the carbon balance.

Foliage NPP was calculated differently for pine and

oak. In pine, NPP of needles was estimated from

allometric relationships between current-year needles

and DBH (Xiao et al., 2003), and the stand inventory

data. Foliage in oak was estimated directly from the

foliage litter fall during 2001–2003 (Curiel Yuste et al.,

2005a).

Coarse root NPP was estimated with allometric

relationships between coarse root biomass and DBH

(Curiel Yuste et al., 2005a) and repeated forest

inventories (2000, 2001, 2003).

Fine root NPP was estimated by repeated root

coring (see Konopka et al., 2005, for detailed

description of methodology). Belowground litter

production was assumed equal to fine root NPP.

Then, NPP for the over storey was calculated by

weighting the oak and Scots pine NPP according to

their relative contribution to the effectively contribut-

ing ecosystem.

For the main under storey tree species (Prunus and

Rhododendron), biomass, NPP and litter production were

estimated as follows: for Rhododendron, we applied the

estimates of Nadezhdina et al. (2003) measured at the

same site. For Prunus, a similar allometric relationship

between component biomass and diameter at breast

height (DBH) was applied as for oak (Curiel Yuste et al.,

2005a).

2.11. Soil carbon content and soil respiration

Carbon stores in the organic surface layer and in the

mineral soil (up to 30 cm deep) were determined in

2003, separately for the oak and the pine stands (Curiel

Yuste et al., 2005a). As samples were taken only until

30 cm depth, the soil carbon stores reported here are

underestimated (see Curiel Yuste et al., 2005a for more

detailed information about organic layers and mineral

soil content).

Soil CO2 efflux was measured during 2001 at

monthly intervals with a closed dynamic system and an

IRGA (CIRAS-1, PP SYSTEMS, Hitchin, Herts, UK) in

nine plots representative for the forest composition

(different associations of canopy/under storey vegeta-

tion; 10 collars per plot; Curiel Yuste et al., 2005b). The

vegetation types were determined by the combination of

the over storey and under storey species, and

corresponded to the vegetation types that were used

to determine the effectively contributing ecosystem. A

more detailed description of the methods and results are

given by Curiel Yuste et al. (2005b).

2.12. Thinnings, wood export, slash inputs and

decomposition

Wood export was quantified from the annual timber

selling reports of the local forest station (detailed for

each management unit in the forest). The remaining

slash was estimated from the allometric relations

established to determine standing biomass. For the

under storey (which was systematically removed)

slash inputs were estimated by multiplying the

cleaned areas (also annually reported for each

management unit) with our estimate of standing

biomass.

Decomposition of this remaining slash was esti-

mated with an exponential decay model applying

different parameters for woody tissues and foliage/fine

roots (Janssens et al., unpublished results):

RSD ¼ BC eð�kaÞ (6)

where RSD is the respiration from slash decomposition

(t(C) ha�1 year�1), BC is the carbon content of the

remaining slash, k is the decomposition parameter

and a is the time passed since the slash started to

decompose (year). The applied values for the decom-

position parameter k were 0.12 for stumps, 0.22 for

branches and coarse roots, 0.25 for pine and rhododen-

dron foliage and fine roots, and 0.35 for oak and Prunus

foliage and fine roots, respectively (decomposition

parameters were obtained by averaging parameter

values of a range of studies; Berg, 1984; McClaugherty

et al., 1984; Berg and Staaf, 1987; Berg et al., 1993;

Liski et al., 2002).

Respiration originating from slash decomposition was

determined for each plot separately (t(C) ha�1 year�1)

and subsequently scaled-up to the effectively contribut-

ing forest ecosystem as described earlier.


Table 3

Equations and methods used to obtain the components of the carbon balance

Carbon flux Methods/equations

Net ecosystem exchange (NEE) Measured eddy covariance fluxes and footprint correction

Net ecosystem production (NEP) =�NEE

Total ecosystem respiration (TER) Extrapolation of footprint-corrected nighttime eddy covariance fluxes

Gross primary production (GPP) =TER + NEP

Standing biomass Measured in dominant vegetation types (stand inventories and allometric relationships

and root coring) and up-scaled by footprint-weighted ecosystem

Soil carbon Measured in dominant vegetation types and up-scaled by footprint-weighted ecosystem

Net primary production (NPP) 1. Change in woody biomass determined from measured stem growth and allometric

relationships + measured aboveground litter production + fine root production and

up-scaled by footprint-weighted ecosystem

2. Wood production determined with yield tables + measured above-ground litter

production + fine root production and up-scaled by footprint-weighted ecosystem

Autotrophic respiration (RA) =GPP � NPP

Above-ground litterfall Litter traps and up-scaled by footprint-weighted ecosystem

Below-ground litter production =Fine root production and up-scaled by footprint-weighted ecosystem

Wood export Forest management reports and up-scaled by footprint-weighted ecosystem

Slash production Wood export data combined with allometric relationships to determine non-harvested

biomass of removed trees and up-scaled by footprint-weighted ecosystem

Slash decomposition (RSD) Slash production and decomposition model and up-scaled to footprint-weighted ecosystem

Soil respiration (RSoil) Measured vegetation types and up-scaled by footprint weighted ecosystem

Above-ground respiration (Raboveground) =TER � RSoil � RSD

Root and mycorrhizal respiration (RRoot) =RA � Raboveground

Organic matter decomposition (RH) =RSoil � RRoot

See Section 2 for a more detailed description of methods.

2.13. Estimation of other components in the carbon

balance

Missing components in the carbon balance were

calculated as follows:

Autotrophic respiration (RA)

RA ¼ GPP � NPP (7)

Aboveground autotrophic respiration (Raboveground)

Raboveground ¼ TER � RSoil � RSD (8)

where RSoil is soil CO2 efflux and RSD is the CO2 flux

originating from slash decomposition.

Belowground autotrophic respiration (root + mycor-

rhizal respiration; RRoot)

RRoot ¼ RA � Raboveground (9)

Decomposition of soil organic matter (SOM) equals

heterotrophic respiration (RH)

RH ¼ RSoil � RRoot (10)

�1 �1
For all fluxes the units are t(C) ha year .
For reasons of clarity a synoptic overview of the

methods and approaches applied to obtain the values

of the carbon balance components is presented in

Table 3.

3. Results and discussion

3.1. Modified eddy flux dataset and effectively

contributing ecosystem

The main result of applying the footprint criteria was

that under unstable atmospheric conditions 97% of the

remaining flux data originated from within the investi-

gated forest. Under neutral conditions this percentage

was lower (91%), but overall no less than 95% of the

remaining flux data originated from within the investi-

gated forest. Thus, we conclude that the reported CO2

fluxes are representative for the investigated forest.

The vegetation composition of the effectively

contributing ecosystem varied annually and seasonally

(Table 2). However, the temporal variation in the

vegetation type contributing to the fluxes was not large,

because the different vegetation types were distributed

fairly evenly throughout the forest. Averaged over all

years, conifers covered 70.2% of the area in the

effectively contributing ecosystem; deciduous species

27.3%, while 2.5% of the area in the effectively


Fig. 3. Carbon balance and carbon fluxes in the investigated Scots

pine/pedunculate oak mixed forest during the 2001–2002 period. All

values are in t(C) ha�1 year�1. Abbreviations: NEP, net ecosystem

production; GPP, gross primary production; TER, total ecosystem

respiration; NPP, net primary production; RA, autotrophic respiration;

SOM, soil organic matter. Values within square brackets refer to

indirectly calculated estimations (see text), or give an indication of the

range of accuracy. Values indicated with an asterisk (*) are affected by

the use of yield tables rather than inventories.

contributing ecosystem was not covered by trees

(grasses or lanes).

3.2. Biomass and soil carbon pools

Overall, Scots pines stored 83.7 t(C) ha�1 in their

biomass, pedunculate oaks 76.0 t(C) ha�1 and the under

storey biomass was 5.3 t(C) ha�1. Thus the biomass of

the effectively contributing ecosystem amounted to

84.6 t(C) ha�1 (Fig. 3).

The soil carbon content in the effectively contributing

ecosystem was calculated to be 134.3 t(C) ha�1 (Fig. 3),

but this estimate is limited to the upper 30 cm. Oak stands

contained much less carbon (92.2 t(C) ha�1) than the

pine stands (150.8 t(C) ha�1; Curiel Yuste et al., 2005a).

This higher soil carbon content in the Scots pine stands

was expected given the recalcitrance of pine litter to

decay, and the reduced heterotrophic activity in the more

acid environment (Curiel Yuste et al., 2005a).

3.3. NEE estimates

Annual NEE was estimated from the footprint-

corrected and gap-filled database (Table 4). A

considerable inter-annual variation in NEE was

observed between 1997 and 2002, in agreement with

many other flux sites across the world (Baldocchi et al.,

2001). Annual NEE showed a much stronger correlation

with TER (R2 = 0.89) than with GPP (R2 = 0.65).

Hence, inter-annual differences in TER probably

contribute most to the observed interannual variation

in NEE. The highest (=most positive) NEE was

obtained in 2000 (+1.1 t(C) ha�1 year�1) and the lowest

in 2002 (�1.1 t(C) ha�1 year�1). The positive NEE in

2000 might be a consequence of the intensive thinning

in the previous year, of the short length of the growing

season relative to the other years or of high respiration

Table 4

Annual rates of the measured net ecosystem exchange (NEE), nighttime respi

ecosystem respiration (TER), gross primary production (GPP) for the 6 ye

Years NEE

(g(C) m�2 year�1)

Rnighttime


Rda

(g(C

1997 �94.4 416.1 648

1998 10.4 496.1 724

1999 13.5 527.5 740

2000 107.4 524.7 767

2001 �94.9 395.3 623

2002 �108.7 347.6 506

Mean �27.8 451.2 668

S.D. 85.9 75.3 86

C.V. (%) 16.7 14

rates related to temperature and rainfall anomalies

(Carrara et al., 2003). The average NEE over the 6 year

period was �0.3 t(C) ha�1 year�1, indicating a small

net CO2 uptake by the forest ecosystem. The carbon

balance depicted in Fig. 3 gives values averaged over

2001–2002. During this period the mean NEE was

�1.0 t(C) ha�1 year�1 (note that NEP = �NEE).

ration (Rnighttime) and the calculated daytime respiration (Rdaytime), total

ars investigated period

ytime

) m�2 year�1)

TER


GPP


.2 1064.3 1158.7

.9 1221.0 1210.6

.1 1267.6 1254.1

.6 1292.3 1184.9

.0 1018.3 1113.2

.5 854.1 962.8

.4 1119.6 1147.4

.8 170.8 102.1

.5 15.3 8.9


Fig. 4. Bimonthly net ecosystem exchange (NEE) of carbon during daytime (round symbols) and nighttime (square symbols) in u*-corrected total

ecosystem (open symbols) and footprint-corrected forest ecosystem (solid symbols). The values in the graph were calculated as the average of the

bimonthly values in the same bimonthly periods of the 6 years. Significance between the bimonthly data sets of the same periods between u*- and

footprint-corrected NEE estimation is indicated by asterisks: ****P < 0.0001; ***P < 0.001; **P < 0.01; *P < 0.05; and n.s., not significant.

The comparison of our footprint-corrected NEE to the

earlier u*-corrected NEE estimate indicated some

interesting differences (Fig. 4). The footprint-based

NEE consistently showed much lower annual values (i.e.

towards higher sequestration), while the inter-annual

variation remained similar. The difference between the 6-

year-mean NEE estimates by the u*- and footprint-

corrected data sets was 1.2 t(C) ha�1 year�1, and the

forest ecosystem turned out to be a small net CO2 sink

(NEE = �0.3 t(C) ha�1 year�1) after footprint-correc-

tion, in contrast with the u*-based estimate, which

indicated a net CO2 source of 0.9 t(C) ha�1 year�1.

Some further analyses were made to understand what

exactly determined the difference in NEE between both

approaches. Two-monthly daytime and nighttime NEE

values obtained with u* and footprint correction were

compared (Fig. 4). Daytime NEE values did not show

consistent differences between u*-corrected dataset.

Daytime meteorological conditions were usually

unstable; therefore, rejecting only a limited amount of

flux data obtained under occasionally stable conditions

did not cause a strong effect on the daytime flux.

The difference between u*- and footprint-corrected

NEE values was, however, larger for nighttime

conditions (1.1 t(C) ha�1 year�1), with a 19% decrease

in NEE in the footprint-corrected dataset compared to

the u*-corrected dataset. This originates from the facts

that (1) during nighttime, stable and neutral conditions

were dominant, and (2) the footprint filter rejected more

data than the u* filter. Hence, the difference between

both approaches was much larger than during daytime.

It is also obvious from Fig. 2 that the majority of the flux

data measured during stable conditions represented the

areas neighbouring the forest, including the town of

Brasschaat, rural urban areas and parks. Combustion

from domestic heating and traffic in the town of

Brasschaat probably increased nighttime CO2 efflux.

Similarly, the typically more active deciduous vegeta-

tion in the surrounding parks (Curiel Yuste et al., 2005a)

and common application of fertilizer and wood-chips

(soil cover) in gardens are also likely to have produced

higher nighttime CO2 efflux in the areas surrounding the

forest. The stronger discrepancy between the two

filtering approaches during winter nights (when heating

was most pronounced) suggests that the u* filter may not

have excluded all of the high fluxes from the

neighbouring areas (Fig. 5). Thus, both u*- and

footprint-corrected NEE represented the forest ecosys-

tem well during daytime, but during nighttime the

footprint-corrected NEE represented the forest better.

3.4. Total ecosystem respiration

The measured annual nighttime respiration, the

calculated annual daytime respiration and TER values

are presented in Table 4. Annual TER ranged from


Fig. 5. Annual net ecosystem exchange of carbon (NEE) in the u*-corrected total ecosystem flux ( ) and in the effectively contributing forest

ecosystem ( ).

8.5 t(C) ha�1 year�1 in 2002 to 12.9 t(C) ha�1 year�1 in

2000 with an average of 11.2 t(C) ha�1 year�1 over the

6-year period. The highest value corresponds to a year

characterized by a short growing season, relatively high

temperatures and following substantial thinnings

(Carrara et al., 2003). Our 6-year mean TER is slightly

higher than the mean TER of the 18 EUROFLUX

forests included in the review by Janssens et al. (2001;

mean: 10.4 t(C) ha�1 year�1; range: 6–

16 t(C) ha�1 year�1).

In comparison with other Scots pine-dominated

forests, the TER values obtained in this study are higher

than in more northern pine stands, where TER ranged

between 6.2 and 9.1 t(C) ha�1 year�1 in southern

Finland (Kolari et al., 2004) and between 5.7 and

6.4 t(C) ha�1 year�1 in northern Finland (Wang et al.,

2004). In contrast, our values were somewhat lower

than the TER of 13.4 t(C) ha�1 year�1 measured in a

Scots pine forest in the Netherlands that experienced

more similar climatic conditions (Valentini et al., 2000).

The 2001–2002 mean TER used in the carbon

balance was 9.4 t(C) ha�1 year�1 (Fig. 3). This value is

substantially lower than the 6-year-mean, because 2001

and 2002 were characterized by the lowest TER of the

entire period.

3.5. Gross primary production

The highest GPP value (12.5 t(C) ha�1 year�1) was

obtained in 1999, the year before the thinning, and the

lowest (9.6 t(C) ha�1 year�1) in 2002 (Table 4). In

contrast to TER, our mean GPP value for the 6 years

(11.5 t(C) ha�1 year�1) was lower than the mean GPP

value of the EUROFLUX forests (13.4 t(C) ha�1

year�1; Janssens et al., 2001). This contrasting

difference explains the very small NEE at our site.

The low GPP is probably related to the relatively high

age of the pine trees that dominated the footprint area,

as well as to the ongoing thinnings (canopy not

completely closed). Nonetheless, our mean GPP of

11.5 t(C) ha�1 year�1 was well within the reported

ranges for forest GPP of 3.0 to 34.4 t(C) ha�1 year�1

(Waring et al., 1998) and 7.0–22.0 (Janssens et al.,

2001).

In comparison with our results, a higher annual GPP

of 15.5 t(C) ha�1 year�1 was calculated from NEE and

TER from a single year eddy covariance measurement

in a 80-year-old Scots pine stand in the Netherlands

(Valentini et al., 2000). Similar values for GPP were

reported for the southern Finnish pine forests (Kolari

et al., 2004), where GPP ranged between 9.3 and

10.7 t(C) ha�1 year�1. Wang et al. (2004) found that

GPP varied from 7.3 to 9.9 t(C) ha�1 year�1 in pine

forests in northern Finland. These lower GPP values are,

of course, expected given the typical negative relation

between GPP and latitude (Waring et al., 1998).

The GPP value of 2001–2002 was 10.4 t(C) ha�1

year�1, and it must be noted that GPP in these 2 years

was the lowest of the 6 years of measurements (1997–

2002). Therefore, the GPP value depicted in the carbon

balance (Fig. 3) is around 10% lower than the average of

the entire period.


Fig. 6. Schematic illustration of the calculation of net biome pro-

ductivity (NBP). The width of the arrow reflects the magnitude of the

carbon flux. GPP, gross primary productivity; NPP, net primary

productivity; NEP, net ecosystem productivity (equals minus net

ecosystem exchange). All values are in t(C) ha�1 year�1.

3.6. Net primary production

The annual mean NPP in the period 2001–2003 was

4.1 t(C) ha�1 year�1 in the Scots pine stands and

8.9 t(C) ha�1 year�1 in the oak stands (Curiel Yuste

et al., 2005a). We further estimated a NPP of

0.6 t(C) ha�1 year�1 in the under storey vegetation.

Thus the NPP used for the carbon balance of the

effectively contributing ecosystem amounted to

5.8 t(C) ha�1 year�1. The explanation for the more

than double NPP in oak as compared to pine might be in

the soil nutrient availability. Several observations

indicated that soil organic matter accumulated at higher

rates under pines than under oaks (Curiel Yuste et al.,

2005a). Curiel Yuste et al. (2005a) therefore hypothe-

sized that more nutrients were being immobilized in

soils under pine than under oak, and that the pines were

exhibiting an age-related decline in productivity due to

nutrient limitation. The observed accumulation of

organic matter resulted from the poor decomposability

of the pine litter. In the oak stands, litter is less

recalcitrant to decay and soil acidity is less severe;

hence organic matter appears not to be accumulating

and nutrients are recycled. This probably explains why

NPP was much higher in the oaks than in the pines and

why oaks allocated a much smaller proportion of NPP to

fine root growth (Curiel Yuste et al., 2005a).

From the yield tables we predicted stem and branch

wood production as 3.8 m3 ha�1 year�1 in the Scots pine

stands. This value contrasts with the 1.8 m3 ha�1 year�1

estimated with the inventory approach. For pedunculate

oak, in contrast, yield tables predicted a much lower

wood increment than the inventory approach (respec-

tively, 6.5 and 15.5 m3 ha�1 year�1).

When we recalculated NPP with the above-men-

tioned values of aboveground wood increment, the rate

of NPP in the effectively contributing forest ecosystem

was reduced from 5.8 to 5.5 t(C) ha�1 year�1. Hence,

we are reporting both inventory- and yield table based

estimates of NPP in the carbon balance (Fig. 3).

3.7. Litter and slash inputs

Measured above-ground and below-ground litter

production amounted to an annual mean carbon flux

of 3.9 t(C) ha�1 year�1 in the Scots pine and 4.0 t(C)

ha�1 year�1 in the pedunculate oak stands over the

2001–2003 period. We estimated an additional

0.5 t(C) ha�1 year�1 litter production from the under

storey vegetation. The total carbon flux via litter in the

footprint-corrected ecosystem was thus 4.3 t(C) ha�1

year�1.

Some stands within the footprint area were thinned

and the under storey removed during the past and recent

decades. We took these thinnings into account, as the

decomposition of the remaining slash could be a

significant component of TER. Calculations, based on

the reported thinnings and on allometric relations,

showed a wood carbon export of 1.2 t(C) ha�1 year�1

from the ecosystem by timber sales and

0.5 t(C) ha�1 year�1 remaining as above- and below-

ground slash during the 2001–2002 period.

The modelled decomposition of the total slash inputs

during the previous 21 years (Janssens et al., unpub-

lished results) suggested a mean carbon efflux of

0.5 t(C) ha�1 year�1 over the 2001–2002 period (Fig. 3;

0.6 and 0.5 t(C) ha�1 year�1 in the 2 years, respec-

tively).

Summation of the exported carbon and the NEE

produces an estimate of the net biome productivity (NBP;

Fig. 6). During the 2001–2002 period, the forest absorbed

1.0 t(C) ha�1 year�1 as CO2 from the atmosphere, but

lost 1.2 t(C) ha�1 year�1 as wood carbon. Thus, the

forest site had a negative NBP, implying that the

ecosystem was losing carbon (0.2 t(C) ha�1 year�1).

3.8. Soil CO2 efflux

Soil CO2 efflux of the effectively contributing

ecosystem was 5.8 t(C) ha�1 year�1 (Curiel Yuste

et al., 2005b). This value was slightly less than the

mean of 6–7 t(C) ha�1 year�1 reported by Raich and

Schlesinger (1992) for temperate forests and of

6.6 t(C) ha�1 year�1 reported by Janssens et al.

(2001) for the EUROFLUX forests. The contribution

of soil respiration to TER tended to vary seasonally,

with minimum contributions during summer (less than

50% of TER) and maximum contributions during winter

(about 94% of TER). We hypothesized that the observed

changes in the contribution of soil respiration to TER

were a result of the antagonistic growth patterns of


shoots and roots caused by seasonal changes in carbon

allocation, the larger seasonal changes in foliar biomass

than in root biomass, and the differences in soil and air

temperature (Curiel Yuste et al., 2005b).

3.9. Carbon accumulation in biomass and soil

Based on the two inventories, we calculated that

biomass was increasing at a rate of 4.9 t(C) ha�1 year�1

in the pedunculate oak stands and of 0.2 t(C) ha�1 year�1

in the Scots pine stands. Biomass of the under storey of

the ecosystem was estimated to increase by

0.1 t(C) ha�1 year�1. These results suggested that the

total net biomass in the effectively contributing

ecosystem increased at a rate of 1.5 t(C) ha�1 year�1

(excluding harvest). When biomass increases were based

on yield table estimates, the rate of biomass increment

was 1.2 t(C) ha�1 year�1 in the effectively contributing

ecosystem (excluding harvest). Given that almost

1.7 t(C) ha�1 year�1 was removed from the biomass

compartment as wood export or slash, the biomass carbon

pool was declining by 0.2–0.6 t(C) ha�1 year�1.

The carbon balance of the effectively contributing

ecosystem further suggested no change in soil carbon

content when NPP was calculated from the results of the

inventory measurements. Both litter- and slash inputs

(4.3 and 0.5 t(C) ha�1 year�1) were in balance with

their decomposition rates (4.3 and 0.5 t(C) ha�1 year�1,

respectively). When growth was estimated with yield

tables, however, we calculated a small increment in soil

carbon (0.4 t(C) ha�1 year�1). Curiel Yuste et al.

(2005a) did observe C sequestration in the soil of the

pine stands, but unfortunately did not measure in the

other vegetation types. Thus, the forest ecosystem was

losing 0.2 t(C) ha�1 year�1 (see discussion about NBP

above) and this decline concealed a loss of biomass-

carbon of 0.2–0.6 t(C) ha�1 year�1 and a gain in soil

carbon of 0–0.4 t(C) ha�1 year�1.

3.10. Other component fluxes

Other components in the carbon balance model were

determined from simple equations as described in

Section 2. Two rates were obtained for several of the

missing components. These two rates represented

carbon fluxes when NPP was estimated either from

inventory results or from yield tables. Total autotrophic

respiration was estimated as 4.6–4.9 t(C) ha�1 year�1.

Of this total, aboveground autotrophic respiration

consumed the largest fraction (3.0 t(C) ha�1 year�1),

and root-mycorrhizal respiration the smallest: 1.6–

1.9 t(C) ha�1 year�1 (Fig. 3).

Heterotrophic respiration (RH) associated with the

decomposition of soil carbon (excluding the decom-

position of slash) was estimated as 3.9–4.3 t(C) ha�1

year�1.

3.11. Discussion of accuracy

To close the carbon balance, calculations were made

in the same order as the results presented above. As

measurement and scaling errors were thus propagated,

the individual autotrophic and heterotrophic respiratory

components were the most uncertain elements of the

carbon balance. We have no data to validate these

fluxes, nor information about their accuracy. An

alternative approach to test the validity of the flux

estimates in our carbon balance is to compare fluxes that

should be of similar magnitude or whose ratios are often

assumed to be relatively conservative. For instance, the

fact that the measured litter inputs and calculated

decomposition rates were of similar magnitude, despite

the fact that they were obtained independently, suggests

that either the values in the carbon balance are reliable,

or that both are incorrect.

The ratio of soil CO2 efflux to total ecosystem

respiration (0.62) was also very conservative and close

to the average ratio observed among the EUROFLUX

forests (0.63; Janssens et al., 2001).

The ratio of root respiration to soil CO2 efflux was

estimated at 27% when NPP was calculated from

allometric relations and 33% when the NPP calculation

was based on yield tables. Landsberg and Gower (1997)

reported that the average contribution of root respiration

to total soil CO2 efflux was 45% in forests, but the

estimates ranged widely from 22% (Tate et al., 1993) to

90% (Thierron and Laudelout, 1996). More recently,

Bond-Lamberty et al. (2004) reported global trends in

the contributions of roots and heterotrophs to soil CO2

efflux. When applied to our site, their regressions

predict root respiration to be 2.15 t(C) ha�1 year�1 (we

estimated 1.6–1.9), and heterotrophic respiration to be

3.4 t(C) ha�1 year�1 (we estimated 3.9–4.3). Although

our estimate of root respiration thus appears too low, it

is well within the ranges reported both by Landsberg

and Gower (1997) and Bond-Lamberty et al. (2004).

Another approach to check the accuracy of the

estimated values of the component fluxes in our carbon

balance is based on the assumption that respiration of

the different plant parts should be more or less

proportional to their NPP. For example, the ratio of

aboveground to belowground NPP (1.97 and 1.76

depending on the method) was very similar to the ratio

of aboveground to belowground autotrophic respiration


(1.93 and 1.57 for both methods to determine NPP).

Also the ratio of NPPaboveground to Raboveground (1.15 and

1.03, respectively, for the two NPP estimates) was

similar to the ratio of NPPbelowground to RRoot (1.13 and

0.92, respectively, for the two NPP estimates). These

consistent results further increased confidence in the

validity of our carbon balance fluxes.

The observation of Waring et al. (1998) that NPP was

approximately proportional to GPP (0.47 � 0.04 (S.D.))

in 12 contrasting forest ecosystems allows a great

simplification of forest growth models. However, as

these authors calculated respiration and GPP in an

indirect way, their observation remains a hypothesis that

should be tested and verified by independent growth and

respiration measurements (Waring et al., 1998; Medlyn

and Dewar, 1999).

In our study growth was measured directly and GPP

was calculated independently. It should, however, be

noted that the GPP estimate of a specific year (using the

generally applied method for eddy covariance studies)

is not independent from the NPP of the previous years.

The calculation of GPP is based on the measurement of

TER, yet a substantial fraction of TER originates from

the decomposition of the previous years’ litter fall.

Therefore, comparing mean NPP to mean GPP over a

longer measurement period is a better reflection of the

NPP/GPP ratio.

When the mean NPP from the inventory measure-

ments of the 3 years period (2001–2003), i.e.

5.8 t(C) ha�1 year�1 was compared to the mean GPP

over 6 years (1997–2002), i.e. 11.5 t(C) ha�1 year�1,

the ratio was 0.51, a result that supports the hypothesis

of Waring et al. (1998). When the NPP calculation

based on yield tables (5.5 t(C) ha�1 year�1) was

compared to the 6 years’ mean GPP, the NPP/GPP

ratio changed to 0.475, which agreed almost perfectly

with the 0.47 prediction of Waring et al. (1998).

4. Conclusions

Application of a footprint correction to eddy

covariance CO2 flux data in a relatively small forest

reduced NEE by 1.2 t(C) ha�1 year�1 in comparison to

a u* correction, and shifted the NEE of the forest from

positive to negative (�0.3 t(C) ha�1 year�1).

These footprint-corrected NEE data were combined

with a footprint-weighted ecological study and manage-

ment information to construct a complete carbon

balance representative for the forested ecosystem.

Despite being a net CO2 sink, the forest was losing

carbon due to intensive thinnings and subsequent export

of wood. Our presented carbon balance further

describes the main carbon processes in a predominantly

old and even-aged Scots pine ecosystem, where the

declining growth process is accompanied by a low

carbon sequestration in the biosphere.

Our results tended to support the predicted 0.47 ratio

between NPP and GPP, even when these components

were measured independently in a forest ecosystem.

Volumes and rates in the carbon balance presented in

this study may differ from those in other ecosystem

types, but the similar footprint correction applied to

eddy covariance data, ecological measurements, and

management information makes the carbon balance

reported in this study relevant to better understand and

quantify carbon flows in forests.

Acknowledgements

This research was financially supported by the EC’s

Fourth (EUROFLUX contract ENV4-CT95-0078) and

Fifth (CARBOEUROFLUX contract EVKL-CT-1999-

00032) Framework Programs. The authors acknowl-

edge the Division of Forests & Green Areas of the

Ministry of the Flemish Community for access to the

forest site, the Institute for Forestry and Game

Management (IFG) and forest ranger M. Schuermans

for logistic support. We are also grateful to F.

Kockelbergh (UA), N. Calluy (UA) and Y. Buidin

(IFG) for technical assistance. M.T. Nagy was

supported by a grant from the UA-Research Council

(call 2003) during his sojourn at the University of

Antwerpen. This study also contributes to the GCTE

Core Project of the International Geosphere Biosphere

Programme (IGBP).

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Footprint-Adjusted Net Ecosystem CO2 Exchange and Carbon Balance Components