Dr. Yiqi Luo Botany and microbiology department University of Oklahoma, USA

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Parameter identifiability, constraints, and equifinality in data assimilation with ecosystem models. (Luo et al. Ecol Appl. In press ). Dr. Yiqi Luo Botany and microbiology department University of Oklahoma, USA. Land surface models and FluxNET data Edinburgh , 4-6 June 2008. - PowerPoint PPT Presentation

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  • Parameter identifiability, constraints, and equifinality in data assimilation with ecosystem modelsDr. Yiqi LuoBotany and microbiology departmentUniversity of Oklahoma, USALand surface models and FluxNET dataEdinburgh, 4-6 June 2008 (Luo et al. Ecol Appl. In press)

  • Observed DataPrior knowledgePosterior distributionParameter identifiability Inverse modelConstrainedEdge-hittingEquifinality

  • Wang et al. (2001) ------ a maximum of 3 or 4 parameters can be determined.

    Braswell et al. (2005) ------ 13 out of 23 parameters were well-constrained.

    Xu et al. (2006) ------ 4 or 3 out of 7 parameters can be constrained, respectively under ambient and elevated CO2.Identiable parameters

  • Three methods to examine parameter identifiabilitySearch methodModel structureData variability

  • Eddy flux data

  • CO2 fluxH2O fluxWind speedTemperaturePARRelative humidity

    Hourly or half-hourly

    Eddy flux technology

  • ModelLeaf-level PhotosynthesisSub-modelCanopy-level PhotosynthesisSub-modelSystem-level C balanceSub-model

  • Table 1 Parameters information

  • Develop prior distribution

    Apply Metropolis-Hasting algorithm

    a) generate candidate p from sample spaceb) input to model and calculate cost functionc) select according to decision criteriond) repeat

    Construct posterior distributionBayesian inversion

  • Conditional Bayesian inversionBayesian inversionBayesian inversionBayesian inversion

  • Fig. 2 Decrease of cost function with each step of conditional inversion

  • ConclusionsConditional inversion can substantially increase the number of constrained parameters.

    Cost function and information loss decrease with each step of conditional inversion.

  • Measurement errors and parameter identifiability

  • TECO biogeochemical model

  • No. of parameter

    8

    12

    8

    3

  • Exit rates

    Chart1

    0.031490785600

    0.61414424160.00205712480

    3.56718157470.59387609780.0002417883

    8.80781010918.54529630512.1907225842

    12.751846202721.821504865936.0553453341

    13.255550928619.949521322921.3286265218

    10.37939250877.82308726960.4248219834

    6.29771359161.19724661760.0002417883

    2.82766556690.06598623310

    1.02751624440.00142416330

    0.316977673100

    0.089593502700

    0.02306367400

    0.007096515100

    0.002809037200

    0.000147844100

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    2.0-SD

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    c1

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