Florian Hartig Theoretical Ecology & Ecological Data Science

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An R package facilitating sensitivity analysis, calibration and forward simulations with the LPJ-GUESS dynamic vegetation model

Maurizio Bagnara, Ramiro Silveyra Gonzalez, Stefan Reifenberg, Jörg Steinkamp, Thomas Hickler, Christian Werner, Carsten F. Dormann, Florian Hartig

Environmental Modelling & Software, 111(C), 55–60 (2019)
Cite this
@article{bagnara2019r,
  author = {Maurizio Bagnara and Ramiro Silveyra Gonzalez and Stefan Reifenberg and Jörg Steinkamp and Thomas Hickler and Christian Werner and Carsten F. Dormann and Florian Hartig},
  title = {An R package facilitating sensitivity analysis, calibration and forward simulations with the LPJ-GUESS dynamic vegetation model},
  journal = {Environmental Modelling & Software},
  volume = {111},
  number = {C},
  pages = {55–60},
  year = {2019},
  doi = {10.1016/j.envsoft.2018.09.004},
}

DOI: 10.1016/j.envsoft.2018.09.004
Cited by 19 (Google Scholar) · 17 (OpenAlex), as of 07 September 2026

View article (DOI)

Abstract

Dynamic global vegetation models (DGVMs) are of crucial importance for understanding and predicting vegetation, carbon, nitrogen and water dynamics of ecosystems in response to climate change. Their complexity, however, creates challenges for model analysis and data integration. A solution is to interface DGVMs with established statistical computing environments. Here we introduce rLPJGUESS, an R package that couples the widely used DGVM LPJ-GUESS with the R environment for statistical computing, making existing R packages and functions readily available to perform complex analyses with this model. We demonstrate the advantages of this framework by using rLPJGUESS to perform several otherwise laborious tasks: a set of single simulations, followed by global and local sensitivity analyses, a Bayesian calibration with a Markov-Chain Monte Carlo (MCMC) algorithm, and a predictive simulation with multiple climate scenarios. Our example highlights the opportunities of interfacing existing models in earth and environmental sciences with state-of-the-art computing environments such as R.

What the paper shows and why it matters (AI-generated)

Running a dynamic global vegetation model like LPJ-GUESS for anything beyond a single simulation used to mean writing custom scripts around its own configuration-file format — a real barrier to sensitivity analysis, calibration or scenario work. rLPJGUESS removes that barrier by coupling the model directly to R, demonstrated here on global and local sensitivity analysis, Bayesian MCMC calibration and multi-scenario prediction in one framework; later global sensitivity analyses of land-carbon-sink uncertainty have built directly on the workflow it established.