Modeling the Greenhouse gases of cropland/grasslandModeling the Greenhouse gases of cropland/grasslandAt European scaleAt European scale

N. Viovy, S. Gervois, N. Vuichard, N. de Noblet-Ducoudré, B. Seguin, N. Brisson, J.F. Soussana , P. Ciais

Aim of modeling: Simulate the GHG exchanges in response to Environmental conditions (climate and management) based on parameterization of biological processes of plant functioningAdvantage:• can be spatially explicit• can be used to extrapolate to the future• can be used to test several scenarios of climate evolution, mitigation option etc….

State of art of modeling of greenhouse gases in ecosystems

Large scale process models : (eg. LPJ, ORCHIDEE…)Can be run at european scale but crude description of processesEspecially for agriculture (Mainly designed for natural vegetation, forest)

Local process models (eg. Crops: STICS, grassland PASIM)

Good description of processes and take into account for managementBut only at field level.

Integrated model: (eg. Fasset) Integrate antropogenic dimention at fram level with simplified Ecosystems processes

How to combine these approaches to assess european scale GHG budgetOn agricultural lands

Two possible approaches:

Coupling Large scale models with local scale models

Improve existing processes in large scale models for better Representation of crops and taking into account for management

Coupling ORCHIDEE with STICS and PASIM

ORCHIDEE: Global scale model representing 12 « plant functionnal types »Simulate both biophysical and biogeochemical processes for net Exchange with the atmospherePart of the IPSL climate model.STICS:

Generic crop model designed for main crops type. Prediction of

Crop yield. Take into account for fertilization, irrigation,

PASIM:

Designed to represent pasture. Include both cutting and grazing by

Ruminants and there effects on the GHC balance

(including N2O and CH4)

Stategy of coupling

CO2,CH4,N2O budget on grasslands and crops

Mitigation options

European statisticse.g –fertilizers input,cutting/grazing systems stocking rate, irrigation

ORCHIDEE

Climate forcing (ATEAM)Vegetation map (CORINE)

PASIM/STICS

In situ forcing

Coupling

European scale hybrid model

Comparison with in-situ data

« optimum management »

Data available at european Level

Climate data: Climate data from ATEAM european project (EVK2-2000-00075)

Combination of 10’x10’ climatology with 0.5°x0.5° CRU climateData to construct a « pseudo 10’x10’ » data set for all the 20th century

Land cover: CORINE land cover map

Very high resolution and quality data set (but no information on cropstypes)

Soil: European soil map (problem of access to the data)

The main problem is to obtain regional statistics on managementPractices !

Cropland: Coupling STICS and ORCHIDEE

50 100 150 200 250 300 3500

500

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1500Aerial biomass (gC / m2)

50 100 150 200 250 300 3500

500

1000

1500Aerial biomass (gC / m2)

Wheat Corn

days days50 100 150 200 250 300 350

0

500

1000

1500Aerial biomass (gC / m2)

50 100 150 200 250 300 3500

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1000

1500Aerial biomass (gC / m2)

Wheat Corn

days days

STI CS (an agronomy model) MeasurementsORCHI DEE-STI CS

Improvement of the hybrid model:

e.g : LAI is calculated by STICS, photosynthesis by ORCHIDEE

50 100 150 200 250 300 350-15

-10

-5

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5net carbon flux (gC/ m2/ day)

rain defi cit

sowing

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harvest

50 100 150 200 250 300 350-15

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5net carbon flux (gC/ m2/ day)

rain defi cit

sowing

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harvest

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8evapotranspiration (mm/ day)

rain defi cit

sowing

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harvest

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8evapotranspiration (mm/ day)

rain defi cit

sowing

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harvest

‘validation’ site: Corn at Bondville (Illinois, US)

net carbon flux (gC/ m2/ day)

50 200 250 350-15

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Mea

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ts p

robl

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Days300100 150

Rain deficit sowingharvest rising

net carbon flux (gC/ m2/ day)

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Rain deficit sowingharvest rising

evapotranspiration (mm/ day)

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Mea

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ts p

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Rain deficit sowingharvest rising

evapotranspiration (mm/ day)

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Mea

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Rain deficit sowingharvest rising

‘validation’ site: wheat at Ponca (Oklahoma, US)

January ORCHIDEE – STICS

January ORCHIDEE

January MODIS (Myneni et al.)

July ORCHIDEE

July MODIS (Myneni et al.)

July ORCHIDEE - STICS

Comparison of LAI between ORCHIDEE, ORCHIDEE – STICS and MODIS

GPP (gC/m2/day)

Time evolution of simulated GPP and NEP (averaged over Europe)

ORCHIDEE

ORCHIDEE-STICS

Very stong increase in seasonal cycle

NEP (gC/m2/day)4

-5

9

Simulation for the 20th century: impact of CO2, climate and management

Atmospheric CO2 (ppm)

1920 1940 1960 1980 2000250

300

350

400

367.9

297

1900

Atmospheric CO2

Mean annual temperature (°C) Annual rainfall (mm)

Climate

1920 1940 1960 1980 20001900

Organic fertilizer Inorganic fertilizer

+ irrigation

Species change

Management

1920 1940 1960 19806

7

8

9

10

11

12Wheat annual NPP

NP

P (

tC /

ha/y

)

CO2 CO2 + climateCO2 + climate + management

10.03

11.01

7.460

2

4

6

8

1900 1920 1940 1960 1980

Wheat yield (from FAO)

1.28

8.02

CO2 CO2 + climate CO2 + climate + management

Evolution of production (tC/ha/y)

Difference of production 2000-1900

Grassland: coupling PASIM and ORCHIDEE

Same forcing as for cropland (climatologic run)

Two scenarios:

• cutting • grazing: automatic determination of stocking rate

Cutting scenario

Yield (tC/(ha year))

Total GH effect (tC/ha/y)

NPP (tC/ha/y) N2O (Kg N/ha/y)

Stocking rate (LU/ha/y)

NPP (tC/ha/y) N2O (Kg N/ha/y) CH4 (t/ha/y)

Total GH effect (tC/ha/y)

Grazing scenario

Conclusions and perspectives

The development of the hybrid