MEASURING AND MODELING SOIL INTRA-DAY VARIABILITY

MEASURING AND MODELING SOIL INTRA-DAY VARIABILITY

OF THE ¹³ CO ₂ & ¹² CO ₂ PRODUCTION AND

TRANSPORT IN A SCOTS PINE FOREST

Goffin Stéphanie, Parent F., Plain C., Epron D., Wylock C., Haut B.,

Maier M., Schack-Kirchner H., Aubinet M., Longdoz Bernard

Background & Objectives

Fs: One of the largest component of C cycle

 10 times greater than fossil fuel emissions

 Uncertainties >>>

? Climate Change Impact ?

? Positive feedback to the GHG effect ?

Soil CO

efflux (Fs)

Soil: large C pool

 Fs changes may rival the

loading of atmosphere by fossil fuel

today

Empirical description Mechanistic understanding

Past Future

Background & Objectives

High Low

[CO₂]

CO₂

CO₂

CO₂

Fs

Transport Production

Autotrophic

Heterotrophic

Abiotic

Diffusion

Avection + Dispersion

Liquid phase

Depth

Background & Objectives

Fs

Transport Production Ps

Autotrophic Heterotrophic

f(porosity, humidity…)

f(Temperature, humidity, substrate)

CO₂

CO₂

CO₂

-70 -60 -50 -40 -30 -20 -10 0

-70 -60 -50 -40 -30 -20 -10 0

-70 -60 -50 -40 -30 -20 -10 0

b) c) d)

Fine and Coarse Roots Corg High

Low Porosity

0 20 40 60 80 100 120

10 12 14 16 18 20 22

Temperature Profile

Temperature [°C]

⇒ Multilayer Approach needed

Background & Objectives

• Discrimination during CO

assimilation

• Discrimination changes with climatic conditions

 During drought, discrimination decrease  photoassimilats more enriched in ¹³CO₂ ( ex: -25‰)

 Temporal variation of ¹³CO₂ may give informations about transfert time of photoassimilates

¹³CO₂=‐8‰

¹³CO₂=‐27‰

Fs

¹³CO₂ = ‐27‰

Sources

¹³CO₂ may differs between CO₂ sources

 ¹³CO₂ may helps for partitionning Fs between sources

⇒ Understanding of 

CO

fluctuations (space & time) needed

Improving mechanistic understanding of F

Multilayer Approach Isotopic Signal Analysis

1) Determine the CO

production rate Ps and its isotopic signature 

Ps for the different soil horizons.

2) Find factors affecting Ps & 

Ps intra & inter day fluctuations

3) Evaluate by modeling which processes (production or transport) drive Fs temporal variability

Sources & 

¹³C Sources & 

¹³C Sources & 

¹³C Sources & 

¹³C Sources & 

¹³C Sources et 

¹³C

Background & Objectives

1. Determine Ps and 

Ps for ≠ layers

F_down

z F_up

C_i Ps_i Fs

i

down up

 

i i

up i down

i Ps

thick F F

t

C  

 



 

•

CO

&

CO

balances for each i layer

• Diffusive Flux-Gradient approach

i _ i x

x

i x

i _ x

x z

D C z

z

C D C

F 



 







 

 

 



i

i _ up i

_ i down

i

i thick

z D C

z D C

t Ps C



 









 



 









 





➨ Ps and Ps for each layer

for ¹²CO₂

& ¹³CO₂

Vertical profile of their dependence on SWC measured on samples at

for ¹²CO₂

& ¹³CO₂

12

CO

&

CO

vertical profile measured by

⇒

Field Measurements

1. Determine Ps and 

Ps for ≠ layers

Membrane porous tube

0 cm 5 cm 10 cm 20 cm

40 cm 80 cm

Soil depth

(Parent et al. 2013)

Field Measurements Half‐hourly In situ measurements during

1. Determine Ps and 

Ps for ≠ layers

Soil  Chamber

2 m

SWC sensors T° sensors

TDLS:

12[CO₂] &

13[CO₂] 1) membrane

tube ≡ [CO₂] &

¹³CO₂ in soil layers 2) from

chamber

≡ EFs &

¹³EFs

21 days

(Parent et al. 2013)

(47°56’N- 7°36’E)

Ah AhC C

Eddy Covariance

tower

Meteorological station

Hartheim experimental site

Slow growing 46 year old Scots Pine Forest (Pinus sylvestris L.)

Mean annual air Temp:10.3°C Mean annual precip: 642 mm

Haplic Regosol (calcaric, humic) (FAO, 2006) Humus type is mull (1-3 cm

thick)

Site Description

1. Determine Ps and 

Ps for ≠ layers

0 30 60 -80

-70 -60 -50 -40 -30 -20 -10 0

Depth [cm]

% of total CO₂ production

0 20 40

-80 -70 -60 -50 -40 -30 -20 -10 0

Fine Rootimpacts [impacts/0.01m²]

0 7.5 15

-80 -70 -60 -50 -40 -30 -20 -10 0

Coarse Rootimpacts [impacts/0.01m²]

0 3 6 9

-80 -70 -60 -50 -40 -30 -20 -10 0

CorgProfile [% mass of the fine soil fraction]

O

Ah

AhC

C

a) b) c) d)

Horizon % CO₂ Prod

Ol 11.5

Ah 64.7

AhC 15.8

C 8

2.5cm 0 cm

-20 cm

-40 cm

-80 cm Ol

Ah

AhC

C

1. Determine Ps and 

Ps for ≠ layers

Vertical Profile of Ps



Mean diel varation explained by LOCAL T° in Ah & AhC

 No significant diel variation in C

 In the litter relationship with u* because of advection not taken into account

dz ) D dC dz(

d dt

) C (

Adv d

Ps     

Mean diel variation

2. Factors affecting Ps and 

Ps

Intra‐day Ps variability

(Goffin et al. 2014)

0.16 0.2 0.24 -28

-26 -24

Daily mean SWC7[m³m^-3] Daily mean13 PAh [‰]

y=-34.67*SWC7-19.29 R²=0.71

)

• Significant day to day variations of 

Ps (> 2.5‰) in Ah

• Best explained by soil moisture

240 248 256

-29 -27 -25

¹³PAh_SGT

¹³PAh(SWC7&VPD(SWC) _DOY-3) R²=0.80

Soil drought impact = enrichment Same impact as for photosynthesis 

discrimination !!!

13

Inter‐day  

Ps variability 

• Not observed with chamber efflux measurements

o Mixing of ≠ layers contribu ons

o Perturbation of local soil climate by chamber ?

2. Factors affecting Ps and 

Ps

3. Who (transport or production) is responsible for Ps and 

Ps temporal variability ?

(Goffin et al. undre review)

• 3 model versions simulating CO

production and transport

• Comparison of their outputs with [CO

] and Fs measurements

o Reference model (RM): 

each layer produce CO₂ following Q10 relationship with the local  t° & diffusion is the only transport process

o Transport Version (TV):

Advection and dispersion are ss o Production Version :

Production is also driven by Photosynthesis Pressure 

Concentration Wave (PPCW) by adding a dependence on VPD 

15

28/08/20102 03/09/2010 09/09/2010 4

6 8 10

F s [µmolCO 2m-2 s-1 ] Reference Model

Model with the Phloem Pressure Concentration Wave Measure

28/08/2010 03/09/2010 09/09/2010 0.5

1 1.5

2x 10⁵

[CO 2] 5cm [µmolCO 2m-3 ]

Reference Model

Model with the Phloem Pressure Concentration Wave Measure

Ref PPCW Measure 0

5000 10000 15000

intra-day variation

Ref PPCW Measure 0

0.5 1 1.5 2

intra-day variation

a) b)

d) c)

o No significant improvement with TV

o PPCW : Not perfect but diurnal fluctuations are better reproduced  and difference in phase is reduced

3. Who (transport or production) is responsible for Ps and 

Ps temporal variability ?

(Goffin et al. undre review)

o RM: Relatively good reproduction of inter‐day variability but intra‐

day variability too low and not in phase

➨ Working on production description instead on transport one is a better option to improve soil CO₂ model

• Set up of an experimental in-situ device to obtain vertical profile of Ps and Ps

• Identify a dependence of one layer to local temperature

• Soil CO

model should develop production description more than transport one to simulate hourly/daily

variability

• Indentify enrichment of Ps with soil drought in Ah horizon

Key points

Thank  you for your  attention

17

Meet me on poster #21

Materials & Method

4. Laboratory Measurements

 Soil horizon specific physical parameters :

• Porosity, pF curves

• Relationships between Ds(SWC)



Undisturbed soil cores of 200

cm

collected in each horizon

Material & Method

4. Laboratory measurements – Ds determination

0 20 40 60 80

0 0.1 0.2 0.3

Depth [cm]

SWC [m³/m³]

Ah1 Ah2 AhC

C

In Situ measurements

0

20

40

60

80

0.00E+00 2.00E‐06 4.00E‐06 6.00E‐06

Depth [cm]

Soil Diffusivity [m²s^‐1]

Ah1 Ah2 AhC

C

Ds(SWC)_AH1 Ds(SWC)_AH2 Ds(SWC)_AHC Ds(SWC)_C

Ds [m²s^‐1]

0.10 0.20 0.30 0.40 0.50

0 0.10 0.20 0.30 0.40

Soil W ater Content [m³/m³]

Ds/D0 []

D_s/D₀(SWC)

Ds/D0 Ah1 Ds/D0 Ah2 Ds/D0 AhC Ds/D0 C cm

Laboratory measurements

dz ) ] CO [ D d dz(

d dt

]) CO [ ( ) d

z (

P   ²  _s ²

19

‐80

‐70

‐60

‐50

‐40

‐30

‐20

‐10 0

0.00E+00 1.00E‐06 2.00E‐06 3.00E‐06 4.00E‐06 5.00E‐06 6.00E‐06 7.00E‐06

Depth [cm]

Soil Diffusivity [m²/s]

from laboratory measurements Harmonic Average

AH1 AH2

AHC

C

Pingintha et al, 2009

Level 0

Litter







 

 

n

k sk

k n

k

k s

D z z D

1 1

Materials & Method

4. Laboratory Measurements– Parametization

Background & Objectives

Materials & Method

Results & Discussion Conclusions

Ah Production terms Litter Production terms

• Soil production shows clear diel and daily fluctuations in Ah

• The diel and daily fluctuations are best explained by the T measured in the topsoil

 temperature is the most important driver of soil CO₂ production

•Unlike other horizons, Ol production was best explained by surface soil water content (SWC) (R²=0.46)

2392 243 247 251 255

3 4 5 6

CO2 Production [µmolCO2m-2s-1]

2390 243 247 251 255

1 2 3 4

DOY

Rain [mm]

ProdAh_SGT

ProdAh(T°_3cm) R²=0.67

b) a)

240 248 256

0 0.5 1 1.5

DOY ProdOl [µmolCO 2m-2 s-1 ] ^ProdOlSGT

ProdOl(SWC

0cm) R²=0.46

Day to day variation

21

1. Determine Ps and 

Ps for ≠ layers

Vertical Profile of CO

sources