Shaun Quegan and friends Making C flux calculations interact with satellite observations of land surface properties

Shaun Quegan and friends

Making C flux calculations interact with satellite observations of land surface

properties

Ciais et al. 2003 IGOS-P Integrated Global Carbon Observing Strategy

Geo-referenced emissions inventories


Atmospheric measurements


Remote sensing of atmospheric CO2

Remote sensing of Remote sensing of atmospheric COatmospheric CO22

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









Ocean Carbon Model

Ocean Carbon Model Terrestrial

Carbon ModelTerrestrial

Carbon Model


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


Coastal studiesCoastal studies

rivers

Lateral fluxes

Data assimilation

link


Global Carbon Data Assimilation System





Terrestrial Carbon Model



Growth cycleFires

BiomassRadiation

Land cover/use

Optimised model

parameters

Optimised model

parameters

Optimised fluxes

Optimised fluxes

Ecological studies

Biomass soil carbon

inventories


rivers

Lateral fluxes









Growth cycleFires

BiomassRadiation

Land cover/use

Optimised model

parameters

Optimised model

parameters

Optimised fluxes

Optimised fluxes

Ecological studies

Biomass soil carbon

inventories


rivers

Lateral fluxes


Terrestrial Component

+ Water components: SWEsoil moisture

NBP

LEACHED

Litter Disturbance

ATMOSPHERICCO2

BIOPHYSICS

Soil

Photosynthesis

GROWTH

Biomass

GPP

NPP

Thinning

Mortality

Fire

The SDGVM carbon cycle

Soil texture

The Structure of a Dynamic Vegetation Model

ParametersClimate

Sn Sn+1DVM

Processes Testing

EO interactions with the DVM

Parameters

DVM

Climate

Soils

Sn Sn+1

Processes

Observable

Land coverForest age

PhenologySnow waterBurnt area

Testing:RadiancefAPAR

Possible feedback

Matching of concepts

S Primary observation

Real world

Derived parameter

Model Model

MODIS/IGBP Landcover 2000

MODIS/UMD Landcover

2000

MODIS LAI/fAPAR biome Landcover2000

CEH LCM2000 GLC2000 (SPOT-VGT)

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%

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?

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

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

Variability in optimising coefficients

Application of model to entire boreal regionsApplication of model to entire boreal regions

Model 1985Model 1985

EO 2002EO 2002EO 1985EO 1985

Model 2002Model 2002

Comparison of ground data with calibrated model

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

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

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.

Precipitation

Temperature

Humidity

Cloud cover

Snowpack

Ground

Evaporation

Snow melt

Atmosphere

SDGVM module driven by climate data

Snow water equivalent (SWE)

SWE estimated from SSM/I data over Siberia

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

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.

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

Severity of disagreement – example

Mid Europe

Severity of disagreement – example

SW China

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.

Detecting incorrect land cover

Pearson’s product moment

0.0 0.9

Crop class incorrectly set Crop class correctly set

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

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

Age Estimation Accuracy

Small Spatial Scale– Inter-stand variance– Inter stand bias

Kielder Forest

Time

Raw Coherence

Large Scale– Meteorology dominant

NorthSouth

Kielder Forest

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

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.

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

Russian Federation500m burned areas1 month 2002

MODIS Burned Area

Russian Federation1km active fires1 month 2002

MODIS Active Fires (& FRP)

Documents

Shaun Quegan and friends Making C flux calculations interact with satellite observations of land surface properties