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How can you use biodiversity data to make the right decisions?

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Biome-BGC is working with a lots of ‘a priori’ unknown and hard to obtain model parameters. Therefore the parameterization is a critical step of using the model. Parameters can be estimated using inverse calibration techniques based on measurement data, which means that the model is being calibrated. Measurement data have to be collected with respect to the model in order to compare them. Comparison is based on misfit measure (e.g. a sort of likelihood value), which is the function of the difference between observed and modelled data. It is based on Bayesian calibration with Monte Carlo search. Each parameter is varied randomly within their ‘a priori’ range and the model is run several times using variable model parameters. Then the ‘a priori’ distribution is updated with model information (distribution of the likelihood function) to define ‘a posteriori’ density function. From the maximum of the ‘a posteriori’ density function optimal parameter values can be calculated and approved.

Who is it for?

Scientists interested in Biome-BGC and professional users of the model for calibrated simulation.

What is it for?

To change ‘a priori’ parameter set to more appropriate ‘a posteriori’ parameters for a fine tuned model performance having the best fit to observations.

How does it work?

GLUE requires a prior execution of a Biome-BGC Monte Carlo Experiment (MCE), that performs an independent parameter variation within ‘a priori’ parameter ranges. Parameters, range of parameter values, output variables and number of randomised repetition has to be set in Biome-BGC MCE workflow, that runs off-line because of the time consuming nature of MCE jobs (usually it takes several days on, for example the EDGeS@home desktop grid). Then one or several GLUE analysis can be launched based on the results of Biome-BGC MCE completed before.

Expected results

Parameter setting and calculated likelihood values for each simulation run instances.

Links to workflow and user documentation

Workflow on myExperiment

Documentation

Biome-BGC Monte Carlo Experiment workflow

Biome-BGC Monte Carlo Experiment workflow documentation

Biome-BGC Model-Data-Fusion framework

Example of use

Dotty plots of GLUE analysis for 10 parameters of Biome-BGC MuSo representing ‘a posteriori’ characteristic of these parameters. ‘A priori’ range of the parameters plotted along the X-axis, while the likelihood values are on the Y-axis in the case study of MACSUR grassland inter-comparison project. Red circle and arrow are pointed out narrower (more certain) ‘a posteriori’ parameter mean and range than ‘a priori’ one at the canopy light extinction coefficient parameter as an example.

biome-bgc-glue


FernTrees0127

biovel-video-screenshot

News

19 February 2015

At the final review of the project by the EC, one of the reviewers said: “Incredible work done with a community that is not unified. Remarkable work. It opens for new development in a near future. Hope for success. Good project. Happy that you have been financed three plus years ago.”

Read all about the project and its results in the Project Final Report or read the Executive Summary only.