Modeling the Greenhouse gases of cropland/grassland At European scale N. Viovy, S. Gervois, N....
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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
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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….
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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
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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
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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)
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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 »
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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 !
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Cropland: Coupling STICS and ORCHIDEE
50 100 150 200 250 300 3500
500
1000
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
500
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
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50 100 150 200 250 300 350-15
-10
-5
0
5net carbon flux (gC/ m2/ day)
rain defi cit
sowing
days
harvest
50 100 150 200 250 300 350-15
-10
-5
0
5net carbon flux (gC/ m2/ day)
rain defi cit
sowing
days
harvest
50 100 150 200 250 300 3500
1
2
3
4
5
6
7
8evapotranspiration (mm/ day)
rain defi cit
sowing
days
harvest
50 100 150 200 250 300 3500
1
2
3
4
5
6
7
8evapotranspiration (mm/ day)
rain defi cit
sowing
days
harvest
‘validation’ site: Corn at Bondville (Illinois, US)
net carbon flux (gC/ m2/ day)
50 200 250 350-15
-10
-5
0
5
Mea
sure
men
ts p
robl
em
Days300100 150
Rain deficit sowingharvest rising
net carbon flux (gC/ m2/ day)
50 200 250 350-15
-10
-5
0
5
-15
-10
-5
0
5
Mea
sure
men
ts p
robl
em
Days300100 150
Rain deficit sowingharvest rising
evapotranspiration (mm/ day)
50 200 250 3500
1
2
3
4
5
6
7
8
Mea
sure
men
ts p
robl
em
Days100 150 300
Rain deficit sowingharvest rising
evapotranspiration (mm/ day)
50 200 250 3500
1
2
3
4
5
6
7
8
Mea
sure
men
ts p
robl
em
Days100 150 300
Rain deficit sowingharvest rising
‘validation’ site: wheat at Ponca (Oklahoma, US)
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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
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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
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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
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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
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Grassland: coupling PASIM and ORCHIDEE
Same forcing as for cropland (climatologic run)
Two scenarios:
• cutting • grazing: automatic determination of stocking rate
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Cutting scenario
Yield (tC/(ha year))
Total GH effect (tC/ha/y)
NPP (tC/ha/y) N2O (Kg N/ha/y)
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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
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Conclusions and perspectives
The development of the hybrid