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The RBBGCMuso Package
RBBGCMuso is an R package which supports the easy but powerful application of the Biome-BGCMuSo biogeochemical model in R environment. It also provides some additional tools for the model such as Biome-BGCMuSo optimized Monte-Carlo simulation and global sensitivity analysis. If you would like to use the framework, please read the following description.
Installation
You can install the RBBGCMuso package in several ways depending on the operating system you use. Up to now RBBGCMuso was tested only in Linux and MS Windows environment, so Mac OS X compatibility cannot be guaranteed yet. In MS Windows you can install the package from binary or from source installer. In Linux you can only install the software from source.
Installation in MS Windows
You can always install the latest RBBGCMuso by copying the following line into the R console (using R or RStudio):
source("https://raw.githubusercontent.com/hollorol/RBBGCMuso/master/installWin.R")Installation in Linux and Windows from Source
Note that in MS Windows you have to install the Rtools Windows software firts. If you would like to install the RBBGCMuso package from Source, you have two options.
- Clone this repository, then build and run the package (further information is available here: package build and install)
OR
- Install the devtools package first:
install.packages("devtools")Then copy the following line into the R session and execute it:
devtools::install_github("hollorol/RBBGCMuso/RBBGCMuso")Quick usage
Preparation
In order to use the RBBGCMuso framework, you have to set up the environment, as you would normally do if you use the model without the RBBGCMuso framework. It means that according to the Biome-BGCMuSo terminology you have to have the proper INI file set, the meteorology input file, and the ecophysiological file (EPC) as minimum input. Additional files might be used by the user including nitrogen deposition, management handlers, etc. Please read the corresponding documentation in the actual Biome-BGCMuSo User's Guide. In order to use RBBGCMuso you have to load the package with the following command:
library(RBBGCMuso)If you do not yet have a complete, operational model input dataset, you may want to use the so-called copyMusoExampleTo function (part of RBBGCMuso) which downloads a complete sample simulation to your hard drive:
copyMusoExampleTo()Once this command is executed in R it will invoke a small Graphical User Interface (GUI) where you can select the target site for the sample simulation. At present only "hhs" site is available, which is the abbreviation of the Hegyhátsál eddy covariance station in Hungary. After selecting the site (hhs in this example) the GUI will ask the user to specify a directory (in other word, folder) where the dataset will be stored. In this example we suppose that the user works under MS Windows, and he/she created a directory called C:\model as target directory. It means that after selection of the site the user will select the C:\model directory. Once the copyMusoExampleTo command is finished, the model input dataset and the model executable (called muso.exe and cygwin1.dll) are available in the C:\model folder. The user might check the content of the files using his/her favourite text editor (we propose Editpad Lite). Note that file extension might be hidden by Windows which might be an issue, so we propose to adjust Windows so that file extensions are visible. Visit this website to learn how to show file extensions in Windows.
The C:\model directory contains the following files:
Running the model
You can run the model in spinup, in normal, or in both phases (including the so-called transient run). Using the so-called calibMuso functcion you will be able to execute the the model in both spinup or normal phase.
Visualization of the model output
Perform Quick experiments
Study the effect of ecophysiological parameters using parameterSweep
Sensitivity analysis
Advanced usage
setupMuso
musoData
musoMapping
musoMappingFind
spinupMuso
normalMuso
calibMuso
plotMuso
plotMusoWithData
musoQuckEffect
musoMonte
musoSensi
Contact
E-mail: hollorol@gmail.com
Acknowledgements
The research was funded by the Széchenyi 2020 programme, the European Regional Development Fund and the Hungarian Government (GINOP-2.3.2-15-2016-00028).