Bayesian Methods for Quantifying and Reducing Uncertainty and Error in Forest Models
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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.
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.
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.
KeywordsCalibration Discrepancy Ensemble modelling Hierarchical modelling Risk analysis Visualization
I thank Peter Levy (CEH-Edinburgh) for his comments on this paper.
Compliance with Ethical Standards
Conflict of Interest
Dr. Van Oijen states that there are no conflicts of interests to declare.
Human and Animal Rights
This article does not contain any studies with human or animal subjects performed by any of the authors.
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