An R package facilitating sensitivity analysis, calibration and forward simulations with the LPJ-GUESS dynamic vegetation model. (January 2019)

• Record Type: Journal Article
• Authors: Bagnara, Maurizio; Silveyra Gonzalez, Ramiro; Reifenberg, Stefan; Steinkamp, Jörg; Hickler, Thomas; Werner, Christian; Dormann, Carsten F.; Hartig, Florian
• Is Part Of: Environmental modelling & software. Volume 111(2019)
• Page Start: 55
• Page End: 60
• Publication Date: 2019-01
• Subjects: rLPJGUESS -- Dynamic global vegetation model (DGVM) -- LPJ-GUESS -- Model calibration -- Sensitivity analysis -- Climate impact modelling; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2018.09.004
• Languages: English
• ISSNs: 1364-8152

Abstract

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: first, 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. Highlights: Dynamic vegetation models must be coupled to statistical computing environments. rLPJGUESS is an R package designed to couple LPJ-GUESS with the R software. We demonstrate the utility of rLPJGUESS using a complete modelling case study. rLPJGUESS makes complex statistical methods readily available to LPJ-GUESS. rLPJGUESS provides an approach easily applicable to other DGVMs.