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Mean annual GPP of Europe derived from its water balance. MDI-BGC, Max Planck Institute for Biogeochemistry, Germany Laboratoire des Sciences du Climat et de L'Environnement, France Research School of Biological Sciences, Australia Forect ecology Lab., University of Tuscia, Italy.

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Mean annual gpp of europe derived from its water balance

Mean annual GPP of Europe derived from its water balance

  • MDI-BGC, Max Planck Institute for Biogeochemistry, Germany

  • Laboratoire des Sciences du Climat et de L'Environnement, France

  • Research School of Biological Sciences, Australia

  • Forect ecology Lab., University of Tuscia, Italy

Christian Beer1, Markus Reichstein1, Philippe Ciais2, Graham Farquhar3, Dario Papale4


Carbon fluxes in Zotino, Siberia.

Lloyd et al., 2002

Carbon balance – observations at ecosystem level

Eddy Covariance Technique

Inventory


GPP

TER

Carbon balance at global scale: observations?

  • Global Scale:

  • Upscaling Inventory data

  • Models using

  • Remote Sensing Data (LUE)

  • Atm. [CO2] (transport inversion)

  • Climate & Soil data (TEMs)


GPP

TER

Carbon balance at global scale: observations?

  • Global Scale:

  • Upscaling Inventory data

  • Models using

  • Remote Sensing Data (LUE)

  • Atm. [CO2] (transport inversion)

  • Climate & Soil data (TEMs)

?


Ball et al., 1987

From Sellers et al., 1997

Objective

Data-driven estimation of European mean GPP.

  • 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


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

FPC: Foliage Projective Cover

Ecosystem-level WUE: Definitions

  • 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


Aim: WUE map texture classes


Large variability of WUE between forest sites texture classes

Station Species WUE [g/kg]

BE-Vie Fagus 4.93

DE-Hai Fagus 5.15

DE-Tha Picea 4.59

DK-Sor Fagus 6.15

FI-Hyy Pinus 3.50

FI-Sod Pinus 2.90

FR-Hes Fagus 4.03

FR-LBr Pinus 3.08

FR-Pue Quercus 3.78

IT-Ro1 Quercus 3.03

NL-Loo Pinus 4.01

Environmental gradients!!





‚Leave-one-out validation‘ texture classes

Generalisation of forest WUE

 11 sets of (a1,a2,a3)


WUE map of Europe texture classes

MODIS Land Cover

Forest

Grass/Cropland

MODIS LAI,

1 km

+

European soil

texture map, 1 km

Mean WUEVPD:

18±5g*hPa/kg

WUEVPD, 1 km (33 maps)


WUE map of Europe texture classes

Mean WUEVPD of crop/grassland

LAI

Soil

texture

WUEVPD


WUE map of Europe texture classes

WUEVPD, 1 km

WUEVPD, 10 km

WUE, 10 km

VPD, 10 km


Watershed-wide GPP texture classes

MODIS Land Cover

Forest

Grass/Cropland

MODIS LAI,

1 km

+

European soil

texture map, 1 km

Mean WUEVPD:

18±5g*hPa/kg

WUEVPD, 1 km (33 maps)

WUEVPD, 10 km (33 maps)

WUE, 10 km

VPD, 10 km

Precip for weighting average

WUE, watershed

ET=Precip-Runoff

GPP, watershed


Watershed-wide GPP – Basis for European GPP estimate texture classes

Reichstein et al., 2006


GPP result & uncertainties texture classes

  • 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

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

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


Discussion texture classes

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 texture classes

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 texture classes

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 texture classes

  • 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 texture classes

Eddy Flux Obs.,

Thank you!

M. Aubinet (2x)

C. Bernhofer (2x)

K. Pilegaard

A. Granier

S. Rambal

R. Valentini

D. Lousteau

T. Vesala

E. Moors

T. Laurila

D. Schulze

N. Buchmann,

A. Knohl

W. Kutsch

G. Kiely

H. Soegaard

Z. Nagy

Z. Barkza

Z. Tuba

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

Comments during the ‚database workshop‘ in Amsterdam


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