Using a Bayesian framework and global sensitivity analysis to identify strengths and weaknesses of two process-based models differing in representation of autotrophic respiration

Minunno, F.; van Oijen, M.; Cameron, D.R.; Cerasoli, S.; Pereira, J.S.; Tomé, M.. 2013 Using a Bayesian framework and global sensitivity analysis to identify strengths and weaknesses of two process-based models differing in representation of autotrophic respiration. Environmental Modelling & Software, 42. 99-115. https://doi.org/10.1016/j.envsoft.2012.12.010

Before downloading, please read NORA policies.

Preview

Text
N500100PP.pdf - Accepted Version
Download (1MB) | Preview

Official URL: http://dx.doi.org/10.1016/j.envsoft.2012.12.010

Abstract/Summary

Process-based models are powerful tools for sustainable and adaptive forest management. Bayesian statistics and global sensitivity analysis allow to reduce uncertainties in parameters and outputs, and they provide better insight of model behaviour. In this work two versions of a process-based model that differed in the autotrophic respiration modelling were analysed. The original version (3PGN) was based on a constant ratio between net and gross primary production, while in a new version (3PGN*) the autotrophic respiration was modelled as a function of temperature and biomass. A Bayesian framework, and a global sensitivity analysis (Morris method) were used to reduce parametric uncertainty, to highlight strengths and weaknesses of the models and to evaluate their performances. The Bayesian approach allowed also to identify the weaknesses and strengths of the dataset used for the analyses. The Morris method in combination with the Bayesian framework helped to identify key parameters and gave a deeper understanding of model behaviour. Both model versions reliably predicted average stand diameter at breast height, average stand height, stand volume and stem biomass. On the contrary, the models were not able to accurately predict net ecosystem production. Bayesian model comparison showed that 3PGN*, with the new autotrophic respiration model, has a higher conditional probability of being correct than the original 3PGN model.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1016/j.envsoft.2012.12.010
Programmes:	CEH Topics & Objectives 2009 - 2012 > Biogeochemistry > BGC Topic 2 - Biogeochemistry and Climate System Processes > BGC - 2.4 - Develop model frameworks to predict future impact of environmental drivers ...
UKCEH and CEH Sections/Science Areas:	Billett (to November 2013)
ISSN:	1364-8152
Additional Information. Not used in RCUK Gateway to Research.:	The attached document is the author’s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling & Software , 42. 99-115. 10.1016/j.envsoft.2012.12.010 www.elsevier.com/
Additional Keywords:	3-PG, net primary production, respiration, Bayesian calibration, Bayesian model comparison, Morris screening, carbon cycle, uncertainty analysis, global sensitivity analysis
NORA Subject Terms:	Ecology and Environment Data and Information
Date made live:	20 Feb 2013 16:24 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/500100

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1016/j.envsoft.2012.12.010

Programmes:

CEH Topics & Objectives 2009 - 2012 > Biogeochemistry > BGC Topic 2 - Biogeochemistry and Climate System Processes > BGC - 2.4 - Develop model frameworks to predict future impact of environmental drivers ...

UKCEH and CEH Sections/Science Areas:

Billett (to November 2013)

ISSN:

1364-8152

Additional Information. Not used in RCUK Gateway to Research.:

The attached document is the author’s version of a work that was accepted for publication in Environmental Modelling & Software. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Environmental Modelling & Software , 42. 99-115. 10.1016/j.envsoft.2012.12.010 www.elsevier.com/

Additional Keywords:

3-PG, net primary production, respiration, Bayesian calibration, Bayesian model comparison, Morris screening, carbon cycle, uncertainty analysis, global sensitivity analysis

NORA Subject Terms:

Ecology and Environment
Data and Information

Date made live:

20 Feb 2013 16:24 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/500100