This research was funded by The FRS-FNRS, Belgium October 2009 - October 2012

Contact Person: Goffin Stéphanie- University of Liege – Gembloux Agro-Bio Tech (GxABT) - Unit of Biosystem Physics, 8 Avenue de la Faculté - 5030 Gembloux - BelgiumTel : +32 (0)81 62 24 90 - Fax : +32 (0)81 62 24 39 e-mail : stephanie.goffin@ulg.ac.be

Horizon Partitioning of Soil CO2 Sources and their Isotopic Composition

(13C) in a Pinus Sylvestris StandGoffin Stéphanie (1), Parent Florian (2, 3), Plain Caroline (2, 3), Maier Martin (4) , Schack-Kirchner Helmer (4), Aubinet Marc (1), Longdoz Bernard (2, 3)

(1) University of Liege - GxABT, Unit of Biosystem Physics, Gembloux, Belgium (2) INRA, UMR 1137, Forest Ecology and Ecophysiology, Centre de Nancy, F-54280 Champenoux, France (3) Nancy University, Henri Poincare University, UMR 1137, Forest Ecology and Ecophysiology, Faculty of Sciences, F-54500 Vandoeuvre les Nancy, France. (4) Institute of Bodenkunde and Waldernährungslehre Soil Science and Forest Nutrition University of Freiburg, Germany

3. RESULTS AND DISCUSSION

0 30 60-80

-70

-60

-50

-40

-30

-20

-10

0

Dep

th [

cm]

% of total CO2

production

0 20 40-80

-70

-60

-50

-40

-30

-20

-10

0

Fine Root impacts

[impacts/0.01m2]

0 7.5 15-80

-70

-60

-50

-40

-30

-20

-10

0

Coarse Root impacts

[impacts/0.01m2]

0 3 6 9-80

-70

-60

-50

-40

-30

-20

-10

0

Corg Profile

[% mass of the fine soil fraction]

O

Ah

AhC

C

a) b) c) d)

3.1 VERTICAL DISTRIBUTION OF CO2 SOURCES:

Horizon % CO2 ProdOl 11.5Ah 64.7

AhC 15.8C 8

2. OBJECTIVES• To improve the mechanistic understanding of soil CO2 efflux Fs

(13Fs) and soil CO2 (13CO2) production P

• To partition soil CO2 production between horizons and to analyze their temporal evolution, using the gradient-efflux approach

2. OBJECTIVES• To improve the mechanistic understanding of soil CO2 efflux Fs

(13Fs) and soil CO2 (13CO2) production P

• To partition soil CO2 production between horizons and to analyze their temporal evolution, using the gradient-efflux approach

1. INTRODUCTIONOne of the key question in climate change research relates to the future dynamics of soil CO2 efflux (Fs). C stable isotopes (13C & 12C), as a tracer tool, improve Fs understanding: origin of C, time lag between CO2 assimilation and emission, etc. So, it is crucial to understand mechanisms controlling both Fs and its isotopic signature (13Fs). Two main processes lead to Fs: CO2 production within the soil P (heterotrophic and autotrophic sources) and transport to the atmosphere. Factors affecting those processes (temperature, moisture, substrate, ect.) vary both temporally and spatially. The vertical variability of CO2 sources is often omitted in models while climate change is likely to affect differently soil horizons. The combination of (i) a multilayer approach and (ii) the stable isotopic tool will undoubtedly improve the mechanistic understanding of Fs.

1000*)1(12

13

13 stdR

PP

P

3. MATERIAL AND METHODSExperimental Site:• Hartheim forest (Germany): 46-year-old slow growing Scot Pine Forest (Pinus Sylvestris L.)• Mean Annual Temperature/ Precipitation: 10.3°C/642 mm• Soil Type: Haplic Regosol (Calcaric humic)- Humus Type: Mull High sand content

z

CODF

xx

s

x

i

][ 2

∆C13/∆t

12Pz

z1

z2

∆C12/∆t

13P

Experimental Setup: (see Poster B51B-0546)

• In situ measurements (frequency = 30 min) (From August 27 to September 14, 2010)

Vertical profile of [CO2] , 13[CO2], SWC, TFs and 13Fs

• Laboratory measurements (on Hartheim soil samples)Horizon Specific dependence of diffusion coefficient (Ds) to soil water content (SWC) and pF curves

Soil parameters: Porosity, Corg,, Root distribution

Soil Gaz Transport (SGT) Model Description:

Production isotopic signature determination:

Parameters: Ds(SWC,T), horizon thickness , surface [12CO2] and [13CO2] profile shape*Inputs: Measured Profiles of: [12CO2], [13CO2], SWC, Temperature (air, soil), Fs and 13Fs * Fitted using Fs and 13Fs measurements

PFdz

d

dt

dCO2

The CO2 (13CO2 ) Mass Balance Equation:

z

F

t

COzP

xxx

][)( 2

The CO2 (13CO2 ) Production Profile: Fi1

12

Fi212

Fi113

Fi213

Fbottom13=0Fbottom

12=0

Fs12 Fs13

z

Ds: Soil Diffusion Coefficient

x: 12 or 13

239 243 247 251 2552

3

4

5

6

CO

2 Pro

duct

ion

[µm

olC

O2m

-2s-1

]

239 243 247 251 2550

1

2

3

4

DOY

Rai

n [m

m]

ProdAhSGT

ProdAh(T°3cm

) R2=0.67

b)

a)

240 248 2560

0.5

1

1.5

DOY

Prod

Ol [

µm

olC

O2m

-2s-1

]

ProdOlSGT

ProdOl(SWC0cm

) R2=0.46

0.16 0.2 0.24-28

-26

-24

Daily mean SWC7[m3m-3]

Dai

ly m

ean 1

3 PAh

[‰]

y=-34.67*SWC7-19.29

R2=0.71

0.16 0.2 0.24-28

-27

-26

Daily mean SWC7 [m3m-3]

Dai

ly m

ean 1

3 Fs [

‰]

y=0.15*x-27.33

R2=0.00

240 248 256-29

-27

-25

DOY

Dai

ly M

ean 1

3 PAh

[‰]

13PAhSGT

13PAh(SWC7&VPD) R2=0.80

a) b)

c)

3.2 TEMPORAL VARIABILITY OF HORIZON CO2 SOURCES INTENSITY:

3.3 TEMPORAL VARIABILITY OF ISOTOPIC SIGNATURE OF CO2 SOURCES:

2.5cm0 cm

-20 cm

-40 cm

-80 cm

Ol

Ah

AhC

C

• below Ol, a general decrease of CO2 sources with depth

• The decrease of CO2 sources with depth corresponds to similar trend in C organic content, the fine and coarse root numbers :

Within first 0-30 cm: 87% of P (% excluding Ol production), 81% of the C organic content, 66% and 81% of the fine and coarse roots number respectively

• Litter contributed to 11.5% of total CO2 productionwithin the range of values reported in the literature (from 3% to ±20%)

• Vertical profile of soil (excluding Ol) basal production rate (R15, obtained as shown in Fig. 4) was better represented by a Gaussian function of depth than by the function suggested in Moyes et al (2010) (Fig 2).

4/1

0

0,1515 )1()(

P

z z

zRzR

2

1

1115 )exp()(

c

bzazR

Moyes et al (2010):

Gaussian Function:

0 0.4 0.8 1.2 1.6

0

-10

-20

-30

-40

-50

-60

-70

-80

R15

[µmolCO2m-2s-1]

Dep

th [

cm]

Simulated R15

Moyes et al. (2010):R15

as a decreasing function of depth (zP=0

at 26.08 cm)

R15

as a Gaussian function of depth (a1=1.88 b

1=5.46 c

1=18.83)

• Soil production shows clear diel and daily fluctuations in Ah and AhC.• The diel fluctuations are dampened and phase shift with depth• The diel and daily fluctuations are best explained by the T measured in the respective horizons (Fig 4)

temperature is the most important driver of soil CO2 production CONFIDENCE IN MODEL VALIDATION

• In Ah, the P dependence on T at 3cm depth ProdAh(T°3cm) differs more from SGT model outputs ProdAhSGT during rain events (Fig 3).

At the beginning of rain upward spikes in Ah production not explained by temperature Birch effect?

• T sensitivity of Fs is statistically the same as Ah production (Fig 4) no way to access to AhC production with Fs.

• T sensitivity decreased with depth (C horizon production is almost insensitive to temperature) (Fig 4)

Ah, AhC and C Production

Ol Production

• The high fluctuations of Ol production coïncide with fluctuations of friction velocity (u*) measured above the canopy as soon as the turbulence (u*) rise, the simulated litter production drops

CO2 advective transport in shallow soil layers, should be added to diffusion process in SGT• Using low turbulence data, the Ol production was best explained, unlike other horizons, by surface soil water content

(SWC) (R2=0.46) (Fig5).

• The fluctuations of 13P are best explained by humidity conditions: SWC, VPD.

• The production isotopic signature (13P), in the most productive horizons (Ah, AhC), shows clear daily fluctuationsFig. 7 13PAh: from -28.27 to -25.49‰ 13PAhC: from -28.58 to -27.12‰

• The Fs isotopic signature (13Fs) presents less clear daily fluctuations 13Fs: from -27.57 to -26.99‰

• In Ah: 71% of the daily fluctuations of 13PAh explained by SWC measured within the horizon (Fig 6a). The more the soil is dry, the more the Ah CO2 production is enriched in 13C (consistent with Risk et al, 2012) Such enrichment was not visible in the 13Fs 13Fs does not represent the dynamics of simulated surface 13P (Fig 6 a & b).

• In Ah, the VPD measured 3 days before (VPDDOY-3) influence significantly the daily fluctuations of 13PAh: VPDDOY-3 => 13PAh consistent with shift in 13C of photosynthates with moisture limitation: the transport time of photoassimilats from aboveground to Ah=3 days ?

80% of the 13PAh daily fluctuations are explained by surface SWC and VPDDOY-3. (Fig7a)

• In AhC, the daily fluctuations of 13PAhC are best explained by the VPD measured 5 days before (VPDDOY-5): VPDDOY-5= 13PAhC : the transport time of photoassimilats from aboveground to AhC=5 days ?

32% of the 13PAhC daily fluctuations are explained by VPDDOY-5.

4. CONCLUSIONS• Soil CO2 source distribution is consistent with soil variable distribution (Corg, fine and coarse root numbers) • Temperature is the main driver of CO2 production, except in the Ol horizon.• SWC is the main driver of CO2 production in Ol horizon and diffuse transport seems not sufficient to simulate it

• Moisture conditions (SWC, VPD) are the main drivers of 13 of CO2 sources 13PAh: immediate effect of SWC and a delayed effect of VPDDOY-3 13PAhC: delayed effect of VPDDOY-5

An immediate effect of SWC and a delayed effect of VPD on 13PAh

A delayed effect of VPD on 13PAhC

• Influences of moisture conditions on 13P are not detectable from surface chamber measurements

12 16 200

4

8

Temperature [°C]

CO

2 Pro

duct

ion

[µm

olC

O 2m-2

s-1]

Fs:0.45*T°-1.44

Ah:0.47*T°-3.59

AhC:0.20*T°-2.23

C:0.05*T°-0.25

R2=0.57

R2=0.73

R2=0.38

R2=0.23

R15

Fig 1: a) Long term Average (from August 27 to September 15, 2010) of CO2 production in the litter and in each layer of 10 cm-thick expressed in percent of total CO2 produced b) Fine root counting distribution (with its 95%-confidence interval) c) Coarse root counting distribution (with its 95%-confidence interval) d) Organic Carbon distribution measured at 2 different locations.

Figure 2 : Grey bars represent the basal respiration at 15°C (R15) deduced from SGT model results; Dotted black line the Moyes function; Solid Black line represent a fitted Gaussian function.

Figure 3 : a) Solid Black Line: Evolution of the Production terms in the Ah horizon (SGT Model output)-Dotted Grey Line : Evolution of the Production terms as an increasing function of temperature [µmolCO2m-2s-1] b) Solid Black Line : Evolution of rain [mm]

Figure 4 : black crosses and line : Surface CO2 efflux response to temperature measured at -5 cm depth and its linear regression -Dark grey crosses and line : Ah CO2 production response to temperature measured at -1 cm and its linear regression - Medium grey points and line : AhC CO2 production response to temperature measured at -40 cm and its linear regression - Light grey points and line : C CO2 production response to temperature measured at -70 cm and its linear regression

Figure 5 : Black crosses: Evolution of Litter Production terms [µmolCO2m-2s-1] (SGT Model) during low turbulence-Dotted Grey Line : Evolution of Litter Production terms as an increasing function of surface soil water content at 0 cm depth[µmolCO2m-

2s-1]

Figure 6: a) The correlation between the daily mean of (i) 13PAh (SGT Model) and (ii) measured soil water content at 7 cm depth (pvalue>0.001) b) The correlation between the daily mean of (i) measured 13Fs and (ii) SWC7 (pvalue>0.95)

Figure 7: a) The daily mean of PAh as SGT model output and as a function of soil water content and VPD (3 days before) measured respectively at -7 cm and above the canopy. b) The daily mean of PAhC as SGT model output and as a function of VPD (5 days before) measured above the canopy..

240 248 256-29

-28

-27

-26

DOY

Dail

y M

ean

1

3 PA

hC

[‰

]

13PAhCSGT

13PAhC(VPDDOY-5

) R2=0.32

0.16 0.2 0.24-28

-26

-24

Daily mean SWC7[m3m-3]

Dail

y m

ean

1

3 PA

h [

‰]

y=-34.67*SWC7-19.29

R2=0.71

0.16 0.2 0.24-28

-27

-26

Daily mean SWC7 [m3m-3]

Dail

y m

ean

1

3 Fs [

‰]

y=0.15*x-27.33

R2=0.00

240 248 256-29

-27

-25

DOY

Dail

y M

ean

1

3 PA

h [

‰]

13PAhSGT

13PAh(SWC7&VPDDOY-3

) R2=0.80

a) b)

c)

Dai

ly M

ean

13P

Ah

[‰]

Dai

ly M

ean

13P

AhC

[‰

]

a)

b)