Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Methods for assessment of models
22 Oct 2014
Review status
This discussion paper is a preprint. It has been under review for the journal Geoscientific Model Development (GMD). The revised manuscript was not accepted.
A user-friendly forest model with a multiplicative mathematical structure: a Bayesian approach to calibration
M. Bagnara1,2, M. Van Oijen3, D. Cameron3, D. Gianelle4, F. Magnani2, and M. Sottocornola1,5 1Fondazione Edmund Mach, Research and Innovation Centre, Sustainable Agro-ecosystems and Bioresources Department, via E. Mach, 1, S. Michele all'Adige (TN), Italy
2Department of Agricultural Sciences, University of Bologna, Viale Fanin 46, Bologna, Italy
3Centre for Ecology and Hydrology, CEH-Edinburgh, Bush Estate, Penicuik EH26 0QB, UK
4FOXLAB, Research and Innovation Centre, via E. Mach, 1, S. Michele all'Adige (TN), Italy
5Department of Chemical and Life Sciences, Waterford Institute of Technology, Cork road, Waterford, Ireland
Abstract. Forest models are being increasingly used to study ecosystem functioning, through the reproduction of carbon fluxes and productivity in very different forests all over the world. Over the last two decades, the need for simple and "easy to use" models for practical applications, characterized by few parameters and equations, has become clear, and some have been developed for this purpose. These models aim to represent the main drivers underlying forest ecosystem processes while being applicable to the widest possible range of forest ecosystems. Recently, it has also become clear that model performance should not be assessed only in terms of accuracy of estimations and predictions, but also in terms of estimates of model uncertainties. Therefore, the Bayesian approach has increasingly been applied to calibrate forest models, with the aim of estimating the uncertainty of their results, and of comparing their performances.

Some forest models, considered to be user-friendly, rely on a multiplicative or quasi-multiplicative mathematical structure, which is known to cause problems during the calibration process, mainly due to high correlations between parameters. In a Bayesian framework using a Markov Chain Monte Carlo sampling this is likely to impair the reaching of a proper convergence of the chains and the sampling from the correct posterior distribution.

Here we show two methods to reach proper convergence when using a forest model with a multiplicative structure, applying different algorithms with different number of iterations during the Markov Chain Monte Carlo or a two-steps calibration. The results showed that recently proposed algorithms for adaptive calibration do not confer a clear advantage over the Metropolis–Hastings Random Walk algorithm for the forest model used here. Moreover, the calibration remains time consuming and mathematically difficult, so advantages of using a fast and user-friendly model can be lost due to the calibration process that is needed to obtain reliable results.

Citation: Bagnara, M., Van Oijen, M., Cameron, D., Gianelle, D., Magnani, F., and Sottocornola, M.: A user-friendly forest model with a multiplicative mathematical structure: a Bayesian approach to calibration, Geosci. Model Dev. Discuss.,, 2014.
M. Bagnara et al.
M. Bagnara et al.


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