An R package facilitating sensitivity analysis, calibration and forward simulations with the LPJ-GUESS dynamic vegetation model
An R package facilitating sensitivity analysis, calibration and forward simulations with the LPJ-GUESS dynamic vegetation model
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DOI：
10.1016/j.envsoft.2018.09.004
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发表时间：
2019-01
期刊：
影响因子：
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通讯作者：
M. Bagnara;R. S. González;Stefan Reifenberg;J. Steinkamp;T. Hickler;C. Werner;C. Dormann;F. Hartig
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文献类型：
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作者：
M. Bagnara;R. S. González;Stefan Reifenberg;J. Steinkamp;T. Hickler;C. Werner;C. Dormann;F. Hartig
动态全球植被模型（dgvm）对于理解和预测生态系统对气候变化的植被、碳、氮和水动态具有重要意义。然而，它们的复杂性为模型分析和数据集成带来了挑战。一种解决方案是将dgvm与已建立的统计计算环境连接起来。在这里，我们介绍lpjguess，这是一个R包，它将广泛使用的DGVM LPJ-GUESS与R环境耦合在一起进行统计计算，使现有的R包和函数可以很容易地使用该模型进行复杂的分析。我们通过使用grlpjguess来执行几个其他费力的任务来展示该框架的优势：首先，一组单一模拟，然后是全局和局部灵敏度分析，使用马尔可夫链蒙特卡罗（MCMC）算法进行贝叶斯校准，以及多种气候情景的预测模拟。我们的例子强调了将地球和环境科学中的现有模型与最先进的计算环境（如R）相结合的机会。
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 introducerLPJGUESS, 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 usingrLPJGUESSto 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.