Mat Disney and Shaun Quegan

EO data assimilation in land process models

No one trusts a model except the man who wrote it; everyone trusts an observation except the man who made it (Harlow Shapley)

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

Concept for Global Carbon Data Assimilation System

NB carbon and water are inextricably linked, so this is a more generalised vegetation – soil – water- atmosphere scheme

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

Which model(s) should go here?

Land process models

Land models need to deal with transfers of

- energy

- matter

- momentum

between the land surface and the atmosphere.

Three classes of land (coupled carbon-water) models: Models driven by radiation (light use efficiency models) Dynamic Vegetation Models: climate driven Simple box models

Some models emphasise hydrology (not discussed here)

∑=

=365

1

..day

PARfAPARLUENPP

Light Use Efficiency Light Use Efficiency modelsmodels

IncomingPAR CO2

GPP

LUELUEAbsorptionfAPAR

Photo-synthesis

Respiration

NPP

The LUE may depend on biome, soil moisture, temperature, nutrients, age,

Efficiency coefficient: LUE

Measured by satellites

Modeled or measured by satellites

Notes on LUE models

Models built by ecologists tend to focus on leaves as the functional element (e.g. Leaf Area Index).

Models built by remote sensors tend to focus on radiation.

LUE models are driven by EO data, rather than geared to assimilating data.

Properties of DVMs

DVMs originally designed to examine long-term trends under climate change so…

Data-independent, except for varying climate data and static soil texture data

Comprehensive description of biophysics All processes internalised, parameterised Complex, non-linear, non-differentiable,

(discontinuities, thresholds) Expensive to run

Soil texture

The Structure of a Dynamic Vegetation Model

ParametersClimate

Sn Sn+1DVM

Processes Testing

How EO data can affect DVM calculations

Parameters

DVM

Climate

Soils

Sn Sn+1

Processes

Observable

Land coverForest age

PhenologySnow waterBurnt area

Testing:RadiancefAPAR

Possible feedback

fAPAR

Calibration– boreal budburst

Offline setting of global parameters can be thought of as a form of DA, but is better described as model calibration.

In the following e.g, we use new EO observations that are unaffected by snow-melt to parameterise the spring warming boreal phenology model.

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

Day of year

Testing SDGVM with EO data

SDGVM can predict satellite ‘observations’ since it contains a canopy model and the concept of radiation interception

Model “skill”

SkillBad Good

1999

SDGVM fAPAR

AVHRR NDVI

Are derived parameters the problem?

Is the problem the SDGVM or the derived parameter from the EO signal?

The next slide shows the fAPAR derived from Seawifs (JRC) and from MODIS for a site in the UK. The large bias between the two is a general feature of these two datasets.

Biases in derived parameters

Assimilating products

Data Assimilation Scheme(KF, EnKF, 4DVAR, etc)

MODEL

Assumptions

Observations

Observations

Assumptions

Assumptions

For example: soil moisture from SMOS, surface

temperature, LAI from MODIS

Low-level vs derived products

similar products give substantially different values;

assumptions used to derive products usually inconsistent with biospheric models;

Product uncertainties are poorly known Can we use low-level products (Reflectance?

BOA radiance? TOA radiance?)

Assimilating reflectance

Data Assimilation Scheme(KF, EnKF, 4DVAR, etc)

Observations

Observations

MODEL

Assumptions

Observation Operator

Assumptions

Assumptions in the observation operator are made to be consistent

with those in the model

e.g. reflectance, backscatter, etc…

Observation operators

This approach needs observation operators: translate ecosystem model state vector into observable properties e.g.

reflectance data assimilated into DALEC; predicting radar coherence in ERS Tandem data from

the SPA model; relating snowpack properties to SSM/I radiometer data; recognising burnt area and severity of burn.

Which is the right model?

Complex DVM-type models never designed for DA

So, pursuing another approach with a simplified box model designed from the start for DA

– DALEC

The Structure of a Data Assimilation Model (DALEC)

Cf

Cr

Cw

Cl

Cs

GPP

WS1

WS2

WSn

ETPpt

Q

RhRa

Stocks and fluxes of carbon (left) and water (right)

EO data (e.g. LAI, VI, reflectance)

Observation model

Ensemble Kalman

Filter

Blue lines indicate integration of EO data with DALEC

Observation operator: simple RT model + snow

Canopy foliage results

No assimilation

Assimilating MODIS

(bands 1 and 2)

Canopy foliage results

Assimilating MODIS

inc. snow

Assimilating MODIS

exc. snow

Quaife, Williams, Disney et al. RSE in press

EO land cover and carbon

Quaife, Quegan, Disney et al.,

submitted

All EO land cover the same? DGVMs use land cover indirectly

– How do we translate land cover classes to PFTs?

EO land cover and carbon

Quaife, Quegan, Disney et al., submitted

How do we find best model-data framework?

Use ‘God’ models to test assumptions of simpler models

– DVMs + DALEC-type models Model-data fusion inter-comparison e.g. REFLEX:

Regional Flux Estimation Experiment – www.carbonfusion.org– Compare strengths/weaknesses of various model-

data fusion techniques– Quantify errors/biases introduced when

extrapolating fluxes in both space and time using a model constrained by model-data fusion methods.

Key issues for DA in land models 1

Models– Simple enough for effective DA but complex

enough to capture biophysics– Suitable interface with observation

operators– preferably differentiable

Key issues for DA in land models 2

Data– Same meaning of observed parameters as used in

models– Proper characterisation of uncertainty i.e. PDFs– Use OOs to make best use of all available data e.g.

optical, LiDAR, RADAR, thermal ….

We are still searching for the best model-data structure.

Key issues for DA in land models 3

DA through observation operators not only answer, for various practical reasons.

Also pursue general concepts of how EO data can reduce the uncertainty in land models

– Calibration, testing etc.

Thank you

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

Lesson

 

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

Lesson

 

Forward operators may prove a powerful tool in land cover mapping

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

calibrate

Calibrated model is unbiased,unlike methods based on NDVI

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

NDVI predicted by SDGVM

1998 1999

0 1 0 1

NBP

LEACHED

Litter Disturbance

ATMOSPHERICCO2

BIOPHYSICS

Soil

Photosynthesis

GROWTH

Biomass

GPP

NPP

Thinning

Mortality

Fire

A Dynamic Vegetation Model (SDGVM)

Assimilating reflectance

Data Assimilation Scheme(KF, EnKF, 4DVAR, etc)

MODEL

Observations

Assumptions

But how do we use a non-linear observation operator?

The real world

May 99 August 99

Comparing model and measured fAPAR

Seawifs

SDGVM

Model and predicted fAPAR

Average overthe whole of Europe for 1999 and 2000

Note: if SDGVM were driven by the Seawifs values, most model forests would die

Experiments State and parameter estimation. DE1 and EV1 sites, 3

years driving data, all available obs As 1. but using synthetic data (DE2 and EV2) Within site forecasting. Another year of driving data for

DE1 and EV1, but no observations As 3. but using synthetic data (DE2 and EV2) Between site extrapolation. DE3 and EV3 sites, 4 years

driving data, MODIS LAI only

Integrated flux predictions

Flux (gC.m-2)

Assimilated data Total

StandardDeviation

NEP

Assimilation exc. snow 373.0 151.3

Assimilation inc. snow 404.8 129.6

Williams et al. (2005) 406.0 27.8

GPP

Assimilation exc. snow 2620.3 96.8

Assimilation inc. snow 2525.6 42.7

Williams et al. (2005) 2170.3 18.1

REFLEX data sets

“Paired” sites to test extrapolation/estimation– Brasschaat (DE2) and Vielsalm (EV2) (MF)– Hainich (DE3) and Hesse (DE1) (DBF)– Loobos (EV1) and Tharandt (EV3) (ENF)

Meteorological drivers, fluxes, MODIS LAI and stocks

– Attempting to estimate “uncertainty” in fluxes and MODIS LAI

REgional Flux Estimation eXperiment (REFLEX)

FluxNet dataMODIS

MDF

Full analysisModel parameters

DALECmodel

Training Runs

Deciduous forest sites

Coniferous forest sites

Assimilation

Output

REgional Flux Estimation eXperiment (REFLEX)

FluxNet dataMODIS

MDF

Full analysisModel parameters

DALECmodel

Testing site forecastswith limited EO data

MDF

MODIS

Analysis

FluxNet data

testing

Assimilation