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EO data assimilation in land process models

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Presentation on theme: "EO data assimilation in land process models"— Presentation transcript:

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

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