Bagnara, Maurizio and Gonzalez, Ramiro Silveyra and Reifenberg, Stefan and Steinkamp, Joerg and Hickler, Thomas and Werner, Christian and Dormann, Carsten F. and Hartig, Florian (2019) An R package facilitating sensitivity analysis, calibration and forward simulations with the LPJ-GUESS dynamic vegetation model. ENVIRONMENTAL MODELLING & SOFTWARE, 111. pp. 55-60. ISSN 1364-8152, 1873-6726
Full text not available from this repository. (Request a copy)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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | PARAMETER UNCERTAINTIES; GAP; rLPJGUESS; Dynamic global vegetation model (DGVM); LPJ-GUESS; Model calibration; Sensitivity analysis; Climate impact modelling |
| Subjects: | 500 Science > 580 Botanical sciences |
| Divisions: | Biology, Preclinical Medicine > Institut für Pflanzenwissenschaften > Lehrstuhl für Ökologie und Naturschutzbiologie (Prof. Dr. Peter Poschlod) |
| Depositing User: | Dr. Gernot Deinzer |
| Date Deposited: | 28 Apr 2020 06:18 |
| Last Modified: | 28 Apr 2020 06:18 |
| URI: | https://pred.uni-regensburg.de/id/eprint/27950 |
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