christian beer, ce-ip crete 2006 mean annual gpp of europe derived from its water balance christian...

Christian Beer, CE-IP Crete 2006

Mean annual GPP of Europe derived from its water balance

Christian Beer1, Markus Reichstein1, Philippe Ciais2, Graham Farquhar3,

Dario Papale4

(1) MDI-BGC, Max Planck Institute for Biogeochemistry, Germany(2) Laboratoire des Sciences du Climat et de L'Environnement, France(3) Research School of Biological Sciences, Australia(4) Forect ecology Lab., University of Tuscia, Italy


Carbon balance – observations at ecosystem level

Eddy Covariance Technique

Inventory

Carbon fluxes in Zotino, Siberia.Lloyd et al., 2002


Global Scale:

1) Upscaling Inventory data

2) Models using- Remote Sensing Data (LUE)- Atm. [CO2] (transport inversion)- Climate & Soil data (TEMs)

Carbon balance at global scale: observations?

GP

P

TE

R


Global Scale:

1) Upscaling Inventory data

2) Models using- Remote Sensing Data (LUE)- Atm. [CO2] (transport inversion)- Climate & Soil data (TEMs)

Carbon balance at global scale: observations?

GP

P

TE

R?


Objective

Data-driven estimation of European mean GPP.

Ball et al., 1987From Sellers et al., 1997

Making use of linkage between C and H2O cycles: -Scaling WUE from stand level to watersheds-Multiplying WUE with water balance of watersheds-Summing up GPP of watersheds


Outline

Generalisation of WUE in forests

WUE map of Europe

Mean WUE and GPP of watersheds

Uncertainties of European GPP number

Plausibility


Ecosystem-level WUE: Definitions

VPDET

GPPWUE

ET

GPPWUE

VPD

GPP & ET: - NEE & LE from CE-IP database (Papale et al., 2006)-GPP derived by NEE partioning (Reichstein et al., 2005)-gap-filling of half-hourly data-aggregation to annual sums

pwpfieldcap wwWHC

)6.0exp(1 LAIFPC

WHC at sites: Applying hydraulic parameters to reported soil texture classes (Cosby et al., 1984)

FPC: Foliage Projective Cover


Aim: WUE map


Large variability of WUE between forest sites

Station Species WUE [g/kg]

BE-Vie Fagus 4.93DE-Hai Fagus 5.15DE-Tha Picea 4.59DK-Sor Fagus 6.15FI-Hyy Pinus 3.50FI-Sod Pinus 2.90FR-Hes Fagus 4.03FR-LBr Pinus 3.08FR-Pue Quercus 3.78IT-Ro1 Quercus 3.03NL-Loo Pinus 4.01

Environmental gradients!!


Generalisation of forest WUE

0.1 0.15 0.2 0.25 0.3 0.3515

20

25

30

35

40

45

WHC of soilW

UE

*VP

D [g

C/k

gH2O

*hP

a] BE-Vie

DE-Hai

DE-Tha

DK-Sor

FI-Hyy

FI-Sod

FR-Hes

FR-LBr

FR-Pue

IT-Ro1

NL-Loo



0.1 0.15 0.2 0.25 0.3 0.3515

20

25

30

35

40

45

WHC of soilW

UE

*VP

D [g

C/k

gH2O

*hP

a] BE-Vie

DE-Hai

DE-Tha

DK-Sor

FI-Hyy

FI-Sod

FR-Hes

FR-LBr

FR-Pue

IT-Ro1

NL-Loo

4.5 5 5.5 6 6.5 7-4

-2

0

2

4

LAI

Res

idua

l WU

E

Fagus

BE-Vie

DE-Hai

DK-Sor

FR-Hes

1 2 3 4 5-8

-6

-4

-2

0

2

4

6

LAI

Res

idua

l WU

E

Pinus

FI-Hyy

FI-Sod

FR-LBr

NL-Loo



321 aFPCaWHCaWUEVPD



321 aFPCaWHCaWUEVPD

11 sets of (a1,a2,a3)

‚Leave-one-out validation‘


WUE map of Europe

MODIS LAI,1 km

European soil texture map, 1 km

WUEVPD, 1 km (33 maps)

MODIS Land Cover

Forest Grass/Cropland

Mean WUEVPD:18±5g*hPa/kg+


LAI Soil texture

WUEVPD

WUE map of Europe

Mean WUEVPD of crop/grassland


WUE, 10 km VPD, 10 km

WUEVPD, 10 km

WUEVPD, 1 km

WUE map of Europe


Watershed-wide GPP

MODIS LAI,1 km

European soil texture map, 1 km


WUE, 10 km VPD, 10 km


WUE, watershed

Precip for weighting average

GPP, watershed

ET=Precip-Runoff

MODIS Land Cover

Forest Grass/Cropland

Mean WUEVPD:18±5g*hPa/kg+


Watershed-wide GPP – Basis for European GPP estimate

Reichstein et al., 2006

River WUE[g/kg] GPP[gC/m²/a]

Seine 0.90 545

Rhone 2.93 1323

Tejo 0.29 141

Rhine 3.52 1444

Elbe 2.22 1084

Danube 2.58 1278

Gota 6.71 2388

Iijoki 6.35 2269


GPP result & uncertainties

GPP of Europe = 3.21±0.36 PgC/a (11% uncertainty)

6 climate data sets:

VPD:-DAO 2000-2003-REMO 1961-2003

Precipitation:-GPCP 2000-2003-CRU 1961-1990-REMO 1961-2003

33 maps of WUEVPD +

Not taken into account: Uncertainties due to soil texture, LAI, land cover


Discussion

Missing productive land:~ Six-fold area of Ireland with GPP=1000 gC/m²/a Underestimation of 0.4PgC/a (13%)

Assuming GPP=1000 gC/m2/a for Gota, Iijoki, Oulujoki: Overestimation of 0.1PgC/a (3%)


Plausibility – Comparison of NPP assessments

GPP = 3.2 PgC/a & NPP/GPP = 0.5 NPP ~ 1.6 PgC/a

NPP(forest) ~ 0.8 PgC/a (Schulze et al., 1999 + Nabuurs et al., 2003)

NPP(crop) ~ 0.5 PgC/a (Imhoff et al., 2004 + FAOSTAT, 2005)

NPP(grass) ~ 1 PgC/a (PASIM model, Vuichard, 2007)

Total: ~ 2.3 PgC/a

Lower estimate compared to inventory!?Uncertainty of NPP/GPP ratio?


Conclusions

GPP can be estimated by the water balance on global scale

Challenge: Extrapolating WUE in space WUEVPD = f(WHC,LAI)

Uncertainty of mean GPP at least 11%


Perspectives

Relationship WUEVPD=f(WHC,LAI) for grass?

Interannual GPP estimates by annual water balance (P-R)

Comparison of GPP anomalies to NEE anomalies by atmospheric CO2 inversions, or TEMs

Coupling such simple GPP model to inversions of atmospheric transport? (Comment by Christian Rödenbeck)

Parameterisation of large-scale TEMs


Acknowledgments

Spatial Data:Joint Research Center: Soil texture map

MODIS Team: Land Cover and LAI

Gridded climate data by REMO, CRU, DAO

Mean river discharge: The Global Runoff Data Centre, D-56002 Koblenz, Germany

Eddy Flux Obs.,Thank you!

M. Aubinet (2x)C. Bernhofer (2x)K. PilegaardA. GranierS. RambalR. ValentiniD. LousteauT. VesalaE. MoorsT. LaurilaD. SchulzeN. Buchmann,A. KnohlW. KutschG. KielyH. SoegaardZ. NagyZ. BarkzaZ. Tuba

Comments during the ‚database workshop‘ in Amsterdam

christian beer, ce-ip crete 2006 mean annual gpp of europe derived from its water balance christian...

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