Land process models Land models need to deal with transfers of


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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



Terrestrial Component



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)





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



The Structure of a Dynamic Vegetation Model



How EO data can affect DVM calculations



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.







Testing SDGVM with EO data

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



Model “skill”



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



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



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)



Observation operator: simple RT model + snow



Canopy foliage results



Canopy foliage results



EO land cover and carbon



EO land cover and carbon



How do we find best model-data framework?

  • Use ‘God’ models to test assumptions of simpler 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



Severity of disagreement – example



Severity of disagreement – example



Lesson



Detecting incorrect land cover



Lesson







NDVI predicted by SDGVM





Assimilating reflectance





Model and predicted fAPAR



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



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)



REgional Flux Estimation eXperiment (REFLEX)




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