shaun quegan and friends
DESCRIPTION
Making C flux calculations interact with satellite observations of land surface properties. Shaun Quegan and friends. Global Carbon Data Assimilation System. Ciais et al. 2003 IGOS-P Integrated Global Carbon Observing Strategy. Terrestrial Component. + Water components: SWE soil moisture. - PowerPoint PPT PresentationTRANSCRIPT
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Shaun Quegan and friends
Making C flux calculations interact with satellite observations of land surface
properties
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Ciais et al. 2003 IGOS-P Integrated Global Carbon Observing Strategy
Geo-referenced emissions inventories
Geo-referenced emissions inventories
Atmospheric measurements
Atmospheric measurements
Remote sensing of atmospheric CO2
Remote sensing of Remote sensing of atmospheric COatmospheric CO22
Atmospheric Transport Model
Atmospheric Transport Model
Ocean Carbon Model
Ocean Carbon Model Terrestrial
Carbon ModelTerrestrial
Carbon Model
Remote sensing of vegetation properties
Growth cycleFires
BiomassRadiation
Land cover/use
Ocean remote sensingOcean colour
AltimetryWindsSSTSSS
Water column inventories
Ocean time seriesBiogeochemical
pCO2
Surface observation
pCO2
nutrients
Optimised model
parameters
Optimised model
parameters
Optimised fluxes
Optimised fluxes
Ecological studies
Biomass soil carbon
inventories
Eddy-covariance flux towers
Coastal studiesCoastal studies
rivers
Lateral fluxes
Data assimilation
link
Climate and weather fields
Geo-referenced emissions inventories
Geo-referenced emissions inventories
Atmospheric measurements
Atmospheric measurements
Remote sensing of atmospheric CO2
Remote sensing of Remote sensing of atmospheric COatmospheric CO22
Atmospheric Transport Model
Atmospheric Transport Model
Ocean Carbon Model
Ocean Carbon Model Terrestrial
Carbon ModelTerrestrial
Carbon Model
Remote sensing of vegetation properties
Growth cycleFires
BiomassRadiation
Land cover/use
Ocean remote sensingOcean colour
AltimetryWindsSSTSSS
Water column inventories
Ocean time seriesBiogeochemical
pCO2
Surface observation
pCO2
nutrients
Optimised model
parameters
Optimised model
parameters
Optimised fluxes
Optimised fluxes
Ecological studies
Biomass soil carbon
inventories
Eddy-covariance flux towers
Coastal studiesCoastal studies
rivers
Lateral fluxes
Data assimilation
link
Climate and weather fields
Global Carbon Data Assimilation System
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Remote sensing of atmospheric CO2
Remote sensing of Remote sensing of atmospheric COatmospheric CO22
Atmospheric Transport Model
Atmospheric Transport Model
Terrestrial Carbon Model
Terrestrial Carbon Model
Remote sensing of vegetation properties
Growth cycleFires
BiomassRadiation
Land cover/use
Optimised model
parameters
Optimised model
parameters
Optimised fluxes
Optimised fluxes
Ecological studies
Biomass soil carbon
inventories
Eddy-covariance flux towers
rivers
Lateral fluxes
Climate and weather fields
Remote sensing of atmospheric CO2
Remote sensing of Remote sensing of atmospheric COatmospheric CO22
Atmospheric Transport Model
Atmospheric Transport Model
Terrestrial Carbon Model
Terrestrial Carbon Model
Remote sensing of vegetation properties
Growth cycleFires
BiomassRadiation
Land cover/use
Optimised model
parameters
Optimised model
parameters
Optimised fluxes
Optimised fluxes
Ecological studies
Biomass soil carbon
inventories
Eddy-covariance flux towers
rivers
Lateral fluxes
Climate and weather fields
Terrestrial Component
+ Water components: SWEsoil moisture
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NBP
LEACHED
Litter Disturbance
ATMOSPHERICCO2
BIOPHYSICS
Soil
Photosynthesis
GROWTH
Biomass
GPP
NPP
Thinning
Mortality
Fire
The SDGVM carbon cycle
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Soil texture
The Structure of a Dynamic Vegetation Model
ParametersClimate
Sn Sn+1DVM
Processes Testing
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EO interactions with the DVM
Parameters
DVM
Climate
Soils
Sn Sn+1
Processes
Observable
Land coverForest age
PhenologySnow waterBurnt area
Testing:RadiancefAPAR
Possible feedback
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Matching of concepts
S Primary observation
Real world
Derived parameter
Model Model
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MODIS/IGBP Landcover 2000
MODIS/UMD Landcover
2000
MODIS LAI/fAPAR biome Landcover2000
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CEH LCM2000 GLC2000 (SPOT-VGT)
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Scale effects on flux estimates (GLC-LCM)
GPP NPP NEP
Difference in annual predicted fluxes for GB, 1999. GLC – LCM.
+1.0% +6.4% +16.1%
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Lessons 1
1. Land cover matters. 2. ‘Subjective’ land cover may be more useful than
‘objective’ land cover.3. Scale matters.4. Can we do this better?
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Start of budburst
T0
days
min(0, T – T0) > Threshold, budburst occurs.
The sum is the red area. Optimise over the 2 parameters, Threshold and T0 (minimum effective temperature).
When
The SDGVM budburst algorithm
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Data
SPOT-VEG budburst 1998, 2000-02: 0.1o
Ground data; Komarov RAS, dates of bud-burst at 9 sites in the region.
Temperature data: ERA-40, 1.125o
GTOPO-30 DEM Land cover: GLC2000
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The Date of budburst derived from minimum NDWI (VGT sensor, 2000) N. Delbart, CESBIO
Day of year
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Variability in optimising coefficients
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Application of model to entire boreal regionsApplication of model to entire boreal regions
Model 1985Model 1985
EO 2002EO 2002EO 1985EO 1985
Model 2002Model 2002
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Comparison of ground data with calibrated model
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Impact on Carbon Calculations Impact on Carbon Calculations
Picard et al.,GCB, 2005
1 day advance: NPP increases by 10.1 gCm-2yr-1
15 days advance: 38% bias in annual NPP
Observations
Phenology modelDynamic Vegetation
Model
Carbon Calculation
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Model needs to be region specific,Model needs to be region specific,here include chilling requirement ?here include chilling requirement ?
Comparison Model-EO: RMSE Comparison Model-EO: RMSE
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Lessons 2
1. A simple 2-parameter spring warming model gives a good fit between model and EO data
2. RMS differences between model, VGT data and ground data are ~6.5 days.
3. Ground data are crucial in investigating bias.4. Model failures are identifiable.5. Noise errors in NPP estimates are ~8%. Bias
effects are ~2.2% per day. 6. Biophysical content of the parameters is low.
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Precipitation
Temperature
Humidity
Cloud cover
Snowpack
Ground
Evaporation
Snow melt
Atmosphere
SDGVM module driven by climate data
Snow water equivalent (SWE)
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SWE estimated from SSM/I data over Siberia
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CTCD: Comparison model and EO (& IIASA snow map)CTCD: Comparison model and EO (& IIASA snow map)SDGVM using ECMWFSDGVM using ECMWF
Snow Water Equivalent (mm) 01/97Snow Water Equivalent (mm) 01/97SSM/ISSM/I
IIASA maximum snow storageIIASA maximum snow storage
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Lessons 3
1. The physical quantity inferred from the EO data is almost certainly not what it is called.
2. The problem here is making the model and the EO data communicate. Until communication is established, the data cannot be used to test or calibrate the model.
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Severity of disagreement – AVHRR/SDGVM
r > 0.497 OR r.m.s.e < 0.2
r < 0.497 AND r.m.s.e > 0.2
r < 0.497 AND r.m.s.e > 0.3
1998
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Severity of disagreement – example
Mid Europe
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Severity of disagreement – example
SW China
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Lessons 5
1. The DVM as currently formulated only supports a simple observation operator. This allows meaningful estimates of time series of observables; absolute values of the observables are of dubious value.
2. These time series permit the model to be interrogated with satellite data, and model failures to be identified.
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Detecting incorrect land cover
Pearson’s product moment
0.0 0.9
Crop class incorrectly set Crop class correctly set
Temporal correlation
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Final remarks
The link between satellite measurements and most surface parameters used by the C models (and how they are represented) is indirect.
In many cases, the only viable source of information on surface properties is from satellites.
The art is to find the right means of communication between the data and the models.
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Environmental effects on coherence
Measurements by radar satellites are sensitive to biomass, but: • only for younger ages• weather dependent through soil and canopy moisture
Coherence of Kielder Forest, July 1995
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Age Estimation Accuracy
Small Spatial Scale– Inter-stand variance– Inter stand bias
Kielder Forest
Time
Raw Coherence
Large Scale– Meteorology dominant
NorthSouth
Kielder Forest
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0 5 10 15 20 25 30 35 40 Age (y)
NE
E t
c ha
-1 y
-1
-8
-4
0
4
8
Estimating NEE with SAR
Sensitivity range
N
(age
)
cohe
renc
e
age0 10 20 30 40 50 60 70 Age (y)
NEE = X N(A(x)) dxX
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Using SPA to model coherence
• Observations+ Model with biomass saturation information
Model Backscatter
SPA was used to predict canopy and soil moisture, and coupled with a radar scattering model to predict coherence. Also needed was the saturation level of biomass, which had to be measured from the data
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Lessons 3
Here the carbon model is essential to interpret the data and its variation.
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UK Forest NEE Calculations 1995
Methods NEE Total (MtC y-1)
[NEE per ha (tC ha y-1)]
Area
(k ha)
FC GIS
(extrap. private forest) -9.37 [-3.2] 2,928
SAR Estimate(measured private forest)
-10.87 [-3.7] 2,928
National Inventory( land class only)
-2.8 [-1.75] 1,600
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Russian Federation500m burned areas1 month 2002
MODIS Burned Area
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Russian Federation1km active fires1 month 2002
MODIS Active Fires (& FRP)