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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.
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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
The SDGVM carbon cycle ATMOSPHERIC CO2 Photosynthesis GPP BIOPHYSICS NPP Soil Litter Fire Mortality GROWTH Thinning Disturbance NBP Biomass LEACHED
The Structure of a Dynamic Vegetation Model Parameters Climate Sn Sn+1 • DVM Soil texture Testing Processes
EO interactions with the DVM Phenology Snow water Burnt area Processes Climate • DVM Land cover Forest age Sn+1 Sn Testing: Radiance fAPAR Soils Observable Possible feedback Parameters
Matching of concepts Real world S Primary observation Model Model Derived parameter
MODIS LAI/fAPAR biome Landcover 2000 MODIS/IGBP Landcover 2000 MODIS/UMD Landcover 2000
GLC2000 (SPOT-VGT) CEH LCM2000
Scale effects on flux estimates (GLC-LCM) GPP NPP NEP Difference in annual predicted fluxes for GB, 1999. GLC – LCM.
Lessons 1 • Land cover matters. • ‘Subjective’ land cover may be more useful than ‘objective’ land cover. • Scale matters. • Can we do this better?
The SDGVM budburst algorithm min(0, T – T0) > Threshold, budburst occurs. When The sum is the red area. Optimise over the 2 parameters, Threshold and T0 (minimum effective temperature). T0 Start of budburst
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
The Date of budburst derived from minimum NDWI (VGT sensor, 2000) N. Delbart, CESBIO Day of year
Application of model to entire boreal regions Model 1985 Model 2002 EO 2002 EO 1985
Impact on Carbon Calculations 1 day advance: NPP increases by 10.1 gCm-2yr-1 15 days advance: 38% bias in annual NPP Observations Carbon Calculation Dynamic Vegetation Model Phenology model Picard et al.,GCB, 2005
Comparison Model-EO: RMSE Model needs to be region specific, here include chilling requirement ?
Lessons 2 • A simple 2-parameter spring warming model gives a good fit between model and EO data • RMS differences between model, VGT data and ground data are ~6.5 days. • Ground data are crucial in investigating bias. • Model failures are identifiable. • Noise errors in NPP estimates are ~8%. Bias effects are ~2.2% per day. • Biophysical content of the parameters is low.
SDGVM module driven by climate data Precipitation Cloud cover Evaporation Humidity Atmosphere Temperature Snowpack Ground Snow melt Snow water equivalent (SWE)
CTCD: Comparison model and EO (& IIASA snow map) Snow Water Equivalent (mm) 01/97 SSM/I SDGVM using ECMWF IIASA maximum snow storage
Lessons 3 • The physical quantity inferred from the EO data is almost certainly not what it is called. • 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.
Severity of disagreement – AVHRR/SDGVM 1998 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
Severity of disagreement – example Mid Europe
Severity of disagreement – example SW China
Lessons 5 • 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. • These time series permit the model to be interrogated with satellite data, and model failures to be identified.
Detecting incorrect land cover Crop class incorrectly set Crop class correctly set 0.9 0.0 Pearson’s product moment Temporal correlation
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.
Environmental effects on coherence Coherence of Kielder Forest, July 1995 • Measurements by radar satellites are sensitive to biomass, but: • only for younger ages • weather dependent through soil and canopy moisture
Age Estimation Accuracy Raw Coherence • Small Spatial Scale • Inter-stand variance • Inter stand bias Time Kielder Forest Kielder Forest North South • Large Scale • Meteorology dominant
Estimating NEE with SAR NEE = X N(A(x)) dx X N(age) coherence NEE tc ha-1 y-1 -8 -4 0 4 8 0 10 20 30 40 50 60 70 Age (y) 0 5 10 15 20 25 30 35 40 Age (y) age Sensitivity range
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
Lessons 3 Here the carbon model is essential to interpret the data and its variation.
MODIS Burned Area Russian Federation 500m burned areas 1 month 2002
MODIS Active Fires (& FRP) Russian Federation 1km active fires 1 month 2002