Bayesian calibration of process-based forest models: bridging the gap between models and data.

By M. van Oijen, Jonathan Rougier, Ron P. Smith. Tree Physiology, 2005

399 citations recorded by the source corpus.

Subfields: Environmental Engineering, Global and Planetary Change, Nature and Landscape Conservation

View Paper: https://www.semanticscholar.org/paper/5ba15e32133f20dc392858767be03f8ae6191bbf

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

In Global and Planetary Change

In Nature and Landscape Conservation

Citations in the corpus

Bayesian calibration, comparison and averaging of six forest models, using data from Scots pine stands across Europe (2013, methodology): “The model structure was described by Van Oijen et al. (2005), more recent model applications are reported by Van Oijen & Thomson (2010) and Van Oijen et al. (2011), and the model is now also in use as the tree component of an agroforestry model (Van Oijen et al., 2010).”
Assessing functional connectivity using empirical data (2008, background): “…model, for example, also can be included in the same framework because the likelihood (probability of data observation given parameters) can be calculated as long as the simulation model is designed to produce the predicted pattern corresponding to the observed pattern (Van Oijen et al. 2005).”
Sensitivity and uncertainty analysis from a coupled 3-PG and soil organic matter decomposition model (2008, methodology): “To speed up computational time we randomly sampled each parameter from its prior distribution and evaluated its the likelihood by using the Metropolis-Hastings algorithm for Markov Chain Monte Carlo (MCMC) simulations as described by Van Oijen et al. (2005) and Robert and Casella (2004), allowing a ”
Statistical inference for stochastic simulation models--theory and application. (2011, methodology): “We recommend Andrieu et al. (2003) as a more thorough introduction to MCMC algorithms, and Van Oijen et al. (2005) as a good ecological example.”
Models for supporting forest management in a changing environment (2011, methodology): “…forest PBMs to be calibrated using Bayesian inversion, where measurements of model output variables like tree height are used to infer what biomass parameter values are plausible (Van Oijen et al., 2005), but this procedure is fairly complicated and does not remove all parameter uncertainty.”
Variance-based sensitivity analysis of a forest growth model (2012, background): “As there is no information about the prior probability distributions for each parameter, we assumed an independent uniform distribution for each parameter with bounds varying 30% either side of its reference value (Esprey et al., 2004; van Oijen et al., 2005).”
The REFLEX project: Comparing different algorithms and implementations for the inversion of a terrestrial ecosystem model against eddy covariance data (2009, methodology): “697 Van Oijen, M., J. Rougier, and R. Smith.”