Connecting dynamic vegetation models to data - an inverse perspective


Hartig, Florian, Dyke, James and Hickler, Thomas et al. (2012) Connecting dynamic vegetation models to data - an inverse perspective Journal of Biogeography, 39, (12), pp. 2240-2252. (doi:10.1111/j.1365-2699.2012.02745.x).

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Description/Abstract

Dynamic vegetation models provide process-based explanations of the dynamics and the distribution of plant ecosystems. They offer significant advantages over static, correlative modelling approaches, particularly for ecosystems that are outside their equilibrium due to global change or climate change. A persistent problem, however, is their parameterization. Parameters and processes of dynamic vegetation models (DVMs) are traditionally determined independently of the model, while model outputs are compared to empirical data for validation and informal model comparison only. But field data for such independent estimates of parameters and processes are often difficult to obtain, and the desire to include better descriptions of processes such as biotic interactions, dispersal, phenotypic plasticity and evolution in future vegetation models aggravates limitations related to the current parameterization paradigm. In this paper, we discuss the use of Bayesian methods to bridge this gap. We explain how Bayesian methods allow direct estimates of parameters and processes, encoded in prior distributions, to be combined with inverse estimates, encoded in likelihood functions. The combination of direct and inverse estimation of parameters and processes allows a much wider range of vegetation data to be used simultaneously, including vegetation inventories, species traits, species distributions, remote sensing, eddy flux measurements and palaeorecords. The possible reduction of uncertainty regarding structure, parameters and predictions of DVMs may not only foster scientific progress, but will also increase the relevance of these models for policy advice.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1111/j.1365-2699.2012.02745.x
ISSNs: 0305-0270 (print)
Keywords: bayesian statistics, calibration, data assimilation, forest models, inverse modelling, model selection, parameterization, plant functional types, predictive uncertainty, process-based models
Subjects: G Geography. Anthropology. Recreation > GB Physical geography
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Organisations: Agents, Interactions & Complexity
ePrint ID: 337710
Date :
Date Event
21 August 2012e-pub ahead of print
December 2012Published
Date Deposited: 02 May 2012 08:01
Last Modified: 17 Apr 2017 17:14
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/337710

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