1. EO data assimilation in land process models
Mat Disney and Shaun Quegan
No one trusts a model except the man who wrote it; everyone trusts an observation except the man who made it (Harlow Shapley)

2. 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
Land surface/Dynamic Global Vegetation Models are central to the entire TCO concept which envisages that such models are driven by gridded data inputs either directly or through landscape syntheses, with a two-way interaction with climate/atmosphere
Ciais et al IGOS-P Integrated Global Carbon Observing Strategy

3. Terrestrial Component
Which model(s) should go here?
Zoom into the diagram to give an indication of the place of DGVMs in land component of TCO with reference to input gridded data and in situ observations.
Critically dependent on gridded data productsbut also on landscape synthesis from consolidated in situ observations
+ Water components:
SWE
soil moisture

4. 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)

5. Light Use Efficiency models
Incoming
PAR
CO2
LUE
Absorption
fAPAR
Photo-
synthesis
Respiration
GPP
NPP
Efficiency coefficient: LUE
The LUE may depend on biome, soil moisture, temperature, nutrients, age,
Modeled or measured by satellites
Measured by satellites

6. 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.

7. 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

8. The Structure of a Dynamic Vegetation Model
Parameters
Climate Sn
Sn+1
DVM
Soil texture
Processes
Testing

9. How EO data can affect DVM calculations
Climate
Soils Sn
Sn+1
Processes
Observable
Land cover
Forest age
Phenology
Snow water
Burnt area
Testing:
Radiance
fAPAR
Possible feedback
fAPAR
Parameters

10. 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.

11. The SDGVM budburst algorithm
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 T0
Start of budburst

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

13. Testing SDGVM with EO data
SDGVM can predict satellite ‘observations’ since it contains a canopy model and the concept of radiation interception

14. Model “skill”
1999
SDGVM fAPAR
AVHRR NDVI
Skill
Bad
Good

15. 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.

16. Biases in derived parameters

17. Assimilating products
Assumptions
Observations
Data Assimilation Scheme
(KF, EnKF, 4DVAR, etc)
Assumptions
Observations
MODEL
Assumptions
For example: soil moisture from SMOS, surface temperature, LAI from MODIS

18. 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?)

19. Assimilating reflectance
Data Assimilation Scheme
(KF, EnKF, 4DVAR, etc)
Observations
Observations
Observation Operator
Assumptions
e.g. reflectance, backscatter, etc…
MODEL
Assumptions
Assumptions in the observation operator are made to be consistent with those in the model

20. 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.

21. 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

22. The Structure of a Data Assimilation Model (DALEC)
EO data (e.g. LAI, VI, reflectance)
Observation model
Ensemble Kalman Filter
Blue lines indicate integration of EO data with DALEC
Ppt Ra Rh ET Cf
WS1 Cl
We have now created a new version of DALEC, adding a simple model of local hydrology, so that both carbon and water dynamics, and their coupling, are simulated. Following the DALEC philosophy, the model is kept simple, with precipitation inputs to the surface soil layer, drainage down through n soil layers with discharge from the lowest, and root water abstraction from a specified number of layers. Daily evapotranspiration is determined from an emulator created from the detailed SPA model. The emulator is driven by external factors such as air temperature, daily radiation etc but also by....[next slide]
Blue parts of the diagram indicate the route via which EO data is integrated with the model. The blue circle enclosing the carbon stocks is intended to show that, in principal, the entire state vector may be used to drive the observation model. In practice only the foliar carbon is used currently.
GPP Cr
WS2 Cw Cs Q
WSn
Stocks and fluxes of carbon (left) and water (right)

23. Observation operator: simple RT model + snow

24. Canopy foliage results
No assimilation
Assimilating MODIS
(bands 1 and 2)

25. Canopy foliage results
Assimilating MODIS
exc. snow
Assimilating MODIS
inc. snow
Quaife, Williams, Disney et al. RSE in press

26. EO land cover and carbon
All EO land cover the same?
DGVMs use land cover indirectly
How do we translate land cover classes to PFTs?
Quaife, Quegan, Disney et al., submitted

27. EO land cover and carbon
Quaife, Quegan, Disney et al., submitted

28. 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
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.

29. Key issues for DA in land models 1
Simple enough for effective DA but complex enough to capture biophysics
Suitable interface with observation operators
preferably differentiable

30. 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.

31. 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.

32. Thank you

33. Severity of disagreement – AVHRR/SDGVM
1998
r > OR r.m.s.e < 0.2
r < AND r.m.s.e > 0.2
r < AND r.m.s.e > 0.3

34. Severity of disagreement – example
Mid Europe

35. Severity of disagreement – example
SW China

36. Lesson
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.

37. Detecting incorrect land cover
Crop class incorrectly set
Crop class correctly set
0.0
0.9
Pearson’s product moment
Temporal correlation

38. Forward operators may prove a powerful tool in land cover mapping
Lesson
Forward operators may prove a powerful tool in land cover mapping

39. Impact on Carbon Calculations
1 day advance: NPP increases by 10.1 gCm-2yr-1
15 days advance: 38% bias in annual NPP
Calibrated model is unbiased,
unlike methods based on NDVI
Observations
calibrate
Carbon Calculation
Dynamic Vegetation
Model
Phenology model
Picard et al.,GCB, 2005

40. Comparison Model-EO: RMSE
Model needs to be region specific,
here include chilling requirement ?

41. NDVI predicted by SDGVM
1998
1999 1 1

42. A Dynamic Vegetation Model (SDGVM)
ATMOSPHERIC
CO2
BIOPHYSICS
Soil
Photosynthesis
GPP
Fire
GROWTH
Mortality
NPP
Thinning
Litter
NBP
Biomass
Disturbance
LEACHED

43. Assimilating reflectance
Observations
Data Assimilation Scheme
(KF, EnKF, 4DVAR, etc)
The real world
MODEL
Assumptions
But how do we use a non-linear observation operator?

44. Comparing model and measured fAPAR
August 99
May 99
Seawifs
SDGVM

45. Model and predicted fAPAR
Average over
the whole of
Europe for 1999
and 2000
Note: if SDGVM were driven by the Seawifs values, most model forests would die

46. 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

47. Integrated flux predictions
(gC.m-2)
Assimilated data
Total
Standard
Deviation
NEP
Assimilation exc. snow
373.0
151.3
Assimilation inc. snow
404.8
129.6
Williams et al. (2005)
406.0
27.8
GPP
2620.3
96.8
2525.6
42.7
2170.3
18.1

48. 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

49. REgional Flux Estimation eXperiment (REFLEX)
FluxNet data
MODIS
Training Runs
Assimilation
MDF
DALEC
model
Output
Deciduous forest sites
Coniferous forest sites
Full analysis
Model parameters

50. REgional Flux Estimation eXperiment (REFLEX)
FluxNet data
MODIS
Testing site forecasts
with limited EO data
MDF
DALEC
model
FluxNet data
testing
MDF
Full analysis
Model parameters
Analysis
Assimilation
MODIS