Connecting dynamic vegetation models to data – an inverse perspective

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.

Bayesian integration of flux tower data into a process-based simulator for quantifying uncertainty in simulated output

“…Field measurements of PBS parameters are difficult or impossible to obtain, leading to incomplete knowledge of site-specific parameters for the occurring species. In practice, practitioners often rely on the literature for values of the PBS parameters (Hartig et al, 2012;Mäkelä et al, 2000).…”

Non-Gaussian data assimilation of satellite-based leaf area index observations with an individual-based dynamic global vegetation model

“…Recently, data assimilation (DA) methods which incorporate observation data into models have been applied to terrestrial biosphere models to reduce the uncertainties in the state variables and model parameters Peng et al, 2011). Previous studies have successfully applied the ensemble Kalman filter (e.g., Evensen, 2003;Williams et al, 2005;Quaife et al, 2008;Stöckli et al, 2011) or adjoint method (e.g., Kaminski et al, 2013;Kato et al, 2013) to the "static" vegetation models, but studies with the "dynamic" global vegetation models (DGVMs) are still limited Peng et al, 2011), although Hartig et al (2012) pointed out the importance.…”

Comparing correlative and process-based modelling approaches in a boreal forest identifies important areas for model development

“…Snell et al (2014), who advocate using DGVMs such as LPJ-GUESS to simulate range shifts, are aware of this issue. They propose Bayesian methods for parameterization (van Oijen et al 2005;Hartig et al 2012) and point to "next-generation DGVMs" (Scheiter et al 2013) which simulate plant individuals with potentially unique trait combinations.…”