Keywords: reproducible research, reproducible reporting, groundwater hydrology, groundwater modelling
Webpages:
https://rogiersbart.github.io/RMODFLOW/,
https://rogiersbart.github.io/RMT3DMS/ Recently there have been different calls for reproducibility in computational hydrology (
e.g. Hutton et al. 2016, Fienen and Bakker (2016), Skaggs, Young, and Vrugt (2015)). The use of open-source languages like
R and
python, and collaborative coding tools like
Git may offer a solution (Fienen and Bakker 2016), but only in combination with literate programming (Knuth 1984) the full potential for reproducible research can be reached. With tools like utils::Sweave (Leisch 2002) and
knitr (Xie 2015),
R has been at the forefront of reproducible research in the last few years, and provides a very interesting environment for reproducible research in computational hydrology.
The
Environmetrics task view provides a list of different contributed packages relating to surface water hydrology and soil science, but the number of packages dealing with subsurface hydrology remains limited to date. There are packages for creating specific types of plots, like
hydrogeo which provides Piper diagram (Piper 1944) plotting, or packages for very specific purposes like
quarrint or
kwb.hantush.
In order to bring the potential of
R to computational subsurface hydrology, in the last few years I have been compiling the
RMODFLOW and
RMT3DMS packages. These provide interfaces with two of the most-widely used open source codes for groundwater flow and contaminant transport modelling: MODFLOW (Harbaugh 2005) and MT3DMS (Zheng and Wang 1999). Different model input and output file reading functions have been implemented, and different pre- and post-processing tools are available. For visualization of the model data, S3 methods making use of
ggplot2 were implemented as well. The current capabilities of the packages will be demonstrated and examples of reproducible workflows will be provided.
References Fienen, Michael N., and Mark Bakker. 2016. “HESS opinions: Repeatable research: What hydrologists can learn from the Duke cancer research scandal.”
Hydrology and Earth System Sciences 20 (9): 3739–43. doi:
10.5194/hess-20-3739-2016.
Harbaugh, Arlen W. 2005.
MODFLOW-2005, the Us Geological Survey Modular Ground-Water Model: The Ground-Water Flow Process.
Hutton, C, T Wagener, J Freer, D Han, C Duffy, and B Arheimer. 2016. “Most computational hydrology is not reproducible, so is it really science?”
Water Resources Research 52: 7548–55. doi:
10.1002/2016WR019285.
Knuth, Donald E. 1984. “Literate Programming.”
Comput. J. 27 (2). Oxford, UK: Oxford University Press: 97–111. doi:
10.1093/comjnl/27.2.97.
Leisch, Friedrich. 2002. “Sweave: Dynamic Generation of Statistical Reports Using Literate Data Analysis.” In
Compstat: Proceedings in Computational Statistics, edited by Wolfgang Härdle and Bernd Rönz, 575–80. Heidelberg: Physica-Verlag HD. doi:
10.1007/978-3-642-57489-4_89.
Piper, Arthur M. 1944. “A Graphic Procedure in the Geochemical Interpretation of Water-Analyses.”
Eos, Transactions American Geophysical Union 25 (6): 914–28. doi:
10.1029/TR025i006p00914.
Skaggs, T.H., M.H. Young, and J.A. Vrugt. 2015. “Reproducible Research in Vadose Zone Sciences.”
Vadose Zone Journal 14 (10): 0. doi:
10.2136/vzj2015.06.0088.
Xie, Yihui. 2015.
Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC.
http://yihui.name/knitr/.
Zheng, Chunmiao, and Patrick Wang. 1999. “MT3DMS, A modular three-dimensional multi-species transport model for simulation of advection, dispersion and chemical reactions of contaminants in groundwater systems; documentation and user’s guide.”
U.S. Army Engineer Research and Development Center Contract Report SERDP-99-1, Vicksburg, MS, 202+.
