assimilating eo data into terrestrial carbon cycle models · assimilating eo data into terrestrial...

NERC Centre for Terrestrial Carbon Dynamics & University of Sheffield

Assimilating EO Data into Terrestrial Carbon Cycle Models

Shaun Quegan (+ CTCD, CESBIO, JRC et al.)

The current challenge in C cycle research

Objective To produce estimates & predictions of ecosystem carbon exchange with quantifiable uncertainty.

Complications Observations have gaps & instrumental weaknesses. Models tend to oversimplify and may miss key processes and linkages.

Solution Data assimilation provides a method to combine models and data to produce a more accurate description of ecosystem dynamics.

Global Carbon Data Assimilation System

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

AltimetryWinds

SSTSSS

Water column inventories

Ocean time seriesBiogeochemical

pCO2

Surface observation

pCO2nutrients

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

AltimetryWinds

SSTSSS

Water column inventories

Ocean time seriesBiogeochemical

pCO2

Surface observation

pCO2nutrients

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


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

Terrestrial component





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


Carbon Cycle – Earth Observation Interfaces

VEGETATION MODELLINGCLIMATE &

WEATHER

VEGETATION-SOILCARBON

ALLOCATION

SOIL TYPESOIL CARBON

EARTH OBSERVATION

LAND COVER

INITIALISATION

DISTURBANCE,PHENOLOGY

CALIBRATION

BIOMASS

TESTING

fAPAR, SNOW COVER

ASSIMILATION

This lecture

Issues

u Monitoring Consistency of models and data:

- are model and measurement quantities compatible?

- comparability of valuesTimescales (re-analysis)

u PredictionAre model internal processes and parameters testable and credible?

S is the state vector describing the vegetation-soil system.

Soil

1S

Climate

DVM 2S

Parameters

The Functioning of a DVM

Calibrating the SDGVM phenology module with EO data

SIBERIA-II: Multi-Sensor Concepts for Greenhouse Gas Accounting of Northern Eurasia

5th Framework Project , 2002-2005

Land cover (IIASA) 1o x 1o forest map

Lake Baikal

80E 120E 80E 120E

50N

75N

The Central Siberia dataset: ~ 2 M km2

The CESBIO budburst algorithm

u Data set: SPOT-VEG 1999-2001u Based on minimum in time-series of NDWI datau Uncertainties in recovered budburst date ~ 7

days

The Date of budburst derived from minimum NDWI (VGT sensor, 2000) N. Delbart, CESBIO

Day of year

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

The calibration procedure

Data - model comparison 1999

Budburst from NDWI dataModel budburst:

optimised parameters

Calibration parameters (forest only)

7.0294.42000

6.0117-2.91999

MMSE(days)

Threshold(degree-

days)

T0

(degrees)Year

Green-up relation to N Pacific Index

Effect of uncertainty in green-up day

‘True’ Assimilation

Vegetation model Scattering or reflectance model

Compare prediction with measurement

Modify model state to improve consistency between data and prediction

Observation model(forward model)

Basis of radiation models (optical)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

400 600 800 1000 1200 1400 1600 1800 2000 2200 2400

wavelength / nm

no

rmal

ised

ab

sorp

tio

n

chlorophyll

water

dry matter

Leaf scattering model

pigments

‘structure’

dry matter

water

Leaf reflectance and transmittance

Basis of radiation models (optical)

u Model of canopy scattering:– Leaf properties– Scattering object density

(LAI), orientation, and spatial distribution

– Soil / understoreyproperties for low density canopies

u solutions by analytical or numerical methods

Link to the C models

u C models include concept of radiation model– For calculation of intercepted radiation

u Observation model– Provides link from subset of C-model state

variables to EO observation– Main linkages:

u LAI, Density (for limited conditions)u leaf properties (hyperspectral data)

– leaf dry matter, chlorophyll (nitrogen), water– xanthophyll cycle (light use efficiency)

Exploiting quantities derived from radiance

CLIMATECO2

SOIL

VEGETATION PHYSIOLOGY AND

BIOPHYSICS

DISTURBANCEGENERATOR

VEGETATION DYNAMICS

VEGETATION PHENOLOGY

NUTRIENT CYCLING

time

LAI

The propagation of the light follows the Beer-Lambert Law:

).exp(.)( lKPARlI −=One gets:

).exp(1 LAIKfAPAR −−=

1=l2=l

LAIl =

PARLight propagation

-> In SDGVMd, fAPAR and LAI are linked in a deterministic way.

Sheffield Dynamic Vegetation Global Model

Application of the models

u Testing C models (SDGVM)– Confront predictions with observations

Time update“predict”

Measurement update

“correct”

A prediction-correction system

Earth Observation

-Phenology,-LAI,-Land cover…

PAR (weekly)

LAI/FAPAR(weekly)

APAR (weekly)

Pot. GPP(weekly)

ΕLand cover(yearly)

*

Light

GPP (daily)

Gs (daily)Temp(daily)Temp

Rainfall-Runoff=ET + ε

Simple SVAT model at the catchmentscale:

TranspirationEvaporation

(daily)

Rainfall,temp, humidty, LAI(daily)

Water

Run Off(daily)

Assimil. Obs. Run Off(monthly)

Pixelscale

Williams et al., 2004

GPP Croot

Cwood

Cfoliage

Clitter

CSOM/CWD

Ra

Af

Ar

Aw

Lf

Lr

Lw

Rh

D

Temperature controlled

6 model pools10 model fluxes9 rate constants10 data time series

Rtotal & Net Ecosystem Exchange of CO2

C = carbon poolsA = allocationL = litter fallR = respiration (auto- & heterotrophic)

0 365 730 1095-4

-3

-2

-1

0

1

2

0 365 730 1095-4

-2

0

2

Time (days, 1= 1 Jan 2000)

b) GPP data + model: SD=123

0 365 730 1095-4

-3

-2

-1

0

1

2 c) GPP & respiration data + model: SD=53

NE

E (g

C m

-2 d

-1)

0 365 730 1095-4

-2

0

2

a) Model only: SD=364

d) All data: SD=26

0

50

100

150

200

0

100

200

300

400

0 365 730 1095

600

800

1000

1200

Foliage

Cf (

gC m

-2)

Fine root

Cr (

gC m

-2)

Wood

Cw (g

C m

-2)

Cf

Cr

Cw

Cl

Cs

GPP

Wc

WS1

WS2

WS3

E T

Ppt

Flux emulator State variable

Flux

Q

Rh

Predictedflux

Carbon modelHydrology

Pools and fluxes

Ra

Cf

Cr

Cw

Cl

Cs

GPPRadiation, VPDtemperature

Wc

WS1

WS2

WS3

E T

Ppt

Radiation, VPDtemperature

Soilsdata

Driver data Flux emulator

State variable

Flux

Input to emulator

Q

Rh

Predictedflux

Ra

Linking C and water fluxes

Cf

Cr

Cw

Cl

Cs

GPP

LAI data

Biomass


Wc

WS1

WS2

WS3

E T

Ppt


Soilsdata

Assimilateddata Driver data Flux emulator

State variable

Observationoperator

Flux

Data assimilation

Input to emulator

Q

Local tower

Rh

Predictedflux

Ra

Local assimilation

Cf

Cr

Cw

Cl

Cs

GPP

Reflectance

Radar


Wc

WS1

WS2

WS3

E T

Ppt


Soilsdata

Assimilateddata Driver data Flux emulator

State variableObservation operator

Flux

Data assimilation

Input to emulator

QRiver gauges

Tall tower &Aircraft

Rh

Predictedflux

Ra

Regional assimilation

Conclusions

u Calibration of DVM parameters with EO data provides a means to improve the predictive power of the models, e.g., phenology, fire.

u Well-developed forward models for scattering and reflectance exist; a current challenge is to interface them to biosphericmodels for monitoring and assimilation.

u Because of possible problems in derived products, such asfAPAR, assimilation of radiances sems preferable. However, this is dependent on how radiation absorption is represented in thebiospheric model.

u Successful assimilation schemes have just been developed forbiospheric models. By using existing forward models, these provide a framework for assimilating EO data.

u Watch this space!

assimilating eo data into terrestrial carbon cycle models · assimilating eo data into terrestrial...

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