Towards Bayesian uncertainty quantification for forestry models used in the UK GHG inventory for LULUCF

Van Oijen, Marcel; Thomson, Amanda ORCID: https://orcid.org/0000-0002-7306-4545. 2007 Towards Bayesian uncertainty quantification for forestry models used in the UK GHG inventory for LULUCF. In: 2nd International workshop on Uncertainty in Greenhouse Gas Inventories, Laxenburg, Austria, 27-28 September 2007. Laxenburg, Austria, IIASA, 209-216.

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Abstract/Summary

The GHG Inventory for the U.K. currently uses a simple carbon-flow model, CFLOW, to calculate the emissions and removals associated with forest planting since 1920. Here we aim to determine whether a more complex process-based model, BASFOR, could be used instead of CFLOW. The use of a more complex approach allows accounting for spatial heterogeneity in soils and weather, but places extra demands on uncertainty quantification. We show how Bayesian methods can be used to address this problem.

Item Type:	Publication - Conference Item (Paper)
Programmes:	CEH Programmes pre-2009 publications > Biogeochemistry > BG01 Measuring and modelling trace gas, aerosol and carbon > BG01.2 Carbon
UKCEH and CEH Sections/Science Areas:	Billett (to November 2013)
Additional Keywords:	Bayesian calibration, uncertainty quantification
NORA Subject Terms:	Agriculture and Soil Science Atmospheric Sciences
Date made live:	14 Feb 2008 16:18 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/2300

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