Bayesian methods for quantifying and reducing uncertainty and error in forest models

Van Oijen, Marcel. 2017 Bayesian methods for quantifying and reducing uncertainty and error in forest models. Current Forestry Reports, 3 (4). 269-280. https://doi.org/10.1007/s40725-017-0069-9

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Official URL: https://doi.org/10.1007/s40725-017-0069-9

Abstract/Summary

Purpose of review: Forest models are tools for analysis and prediction of productivity and other services. Model outputs can only be useful if possible errors in inputs and model structure are recognized. However, errors cannot be quantified directly, making uncertainty inevitable. In this paper, we aim to clarify terminological confusion around the concepts of error and uncertainty and review current methods for addressing uncertainty in forest modelling. Recent findings: Modellers increasingly recognize that all uncertainties—in data, model inputs and model structure—can be represented using probability distributions. This has stimulated the use of Bayesian methods for quantifying and reducing uncertainty and error in models of forests and other vegetation. The Achilles’ heel of Bayesian methods has always been their computational demand, but solutions are being found. Summary: We conclude that future work will likely include (1) more use of Bayesian methods, (2) more use of hierarchical modelling, (3) replacement of model spin-up by Bayesian calibration, (4) more use of ensemble modelling and Bayesian model averaging, (5) new ways to account for model structural error in calibration, (6) better software for Bayesian calibration of complex models, (7) faster Markov chain Monte Carlo algorithms, (8) more use of model emulators, (9) novel uncertainty visualization techniques, (10) more use of graphical modelling and (11) more use of risk analysis.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1007/s40725-017-0069-9
UKCEH and CEH Sections/Science Areas:	Atmospheric Chemistry and Effects (Science Area 2017-)
ISSN:	2198-6436
Additional Keywords:	calibration, discrepancy, ensemble modelling, hierarchical modelling, risk analysis, visualization
NORA Subject Terms:	Ecology and Environment Data and Information
Date made live:	20 Nov 2017 15:40 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/518404

Item Type:

Publication - Article

Digital Object Identifier (DOI):

https://doi.org/10.1007/s40725-017-0069-9

UKCEH and CEH Sections/Science Areas:

Atmospheric Chemistry and Effects (Science Area 2017-)

ISSN:

2198-6436

Additional Keywords:

calibration, discrepancy, ensemble modelling, hierarchical modelling, risk analysis, visualization

NORA Subject Terms:

Ecology and Environment
Data and Information