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Biome-BGC SA 1.3 (1)

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Biome-BGC is a process-based biogeochemical model that can be used to simulate carbon, nitrogen and water fluxes of different terrestrial ecosystems. Two models have been implemented: the Biome-BGC v4.1.1 Max Planck Institute model, and the newly developed Biome-BGC MuSo 3.0 model. Performance, success or failure of these models are highly dependent on parameter settings and variation. Due to the high number of parameters (around 40 and 60 for 4.1.1 MPI and MuSo respectively) and the non-line...

Created: 2014-10-04

Credits: Ferenc HORVATH Dora Krasser Peter Ittzes Zoltan BARCZA BioVeL

Biome-BGC CARBON 1.2 (4)

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Biome-BGC is a process-based biogeochemical model that can be used to simulate carbon, nitrogen and water fluxes of different terrestrial ecosystems. A new version of the model, called Biome-BGC MuSo was developed to perform more realistic simulations in terms of soil hydrology, and improved ecosystem management options essentially (Hidy et al. 2012; Hidy & Barcza 2014). The Biome-BGC CARBON service executes a single simulation run at a given geographic location under that distinctive environ...

Created: 2013-06-04 | Last updated: 2015-06-12

Credits: Ferenc HORVATH Dora Krasser Peter Ittzes Zoltan BARCZA BioVeL

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Biome-BGC MCE 1.4.1 (4)

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Biome-BGC is a process-based biogeochemical model that can be used to simulate carbon, nitrogen and water fluxes of different terrestrial ecosystems. Two models were implemented: Biome-BGC 4.1.1 MPI (Trusilova et al. 2009) and Biome-BGC MuSo 3.0 (Hidy & Barcza 2014). Its has about 40 and 60 various parameters. The only way to estimate or set these parameters is "calibration" (data-model-harmonization or model-data-fusion), which demands a hugh amount of computational capacity. Monte Carlo Exp...

Created: 2013-06-04 | Last updated: 2014-10-04

Credits: Ferenc HORVATH Zoltan BARCZA Dora Krasser Peter Ittzes BioVeL

Biome-BGC GLUE 1.3 (2)

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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. Parameteres 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 di...

Created: 2014-10-02 | Last updated: 2014-10-09

Credits: Peter Ittzes Ferenc HORVATH Dora Krasser Zoltan BARCZA Doborl BioVeL