wrv: An R Package for Groundwater Flow Model Construction, Wood River Valley Aquifer System, Idaho

Abstract:

Groundwater models are one of the main tools used in the hydrogeological sciences to assess resources and to simulate possible effects from future water demands and changes in climate. The hydrological inputs to groundwater models can be numerous and can vary in both time and space. Difficulties associated with model construction are often related to extensive datasets and cumbersome data processing tasks. To mitigate these difficulties, a graphical user interface (GUI) is often employed to aid the input of data for creating models. Unfortunately, GUI software presents an obstacle to reproducibility, a cornerstone of research. The considerable effort required to document processing steps in a GUI program, and the rapid obsoleteness of these steps with subsequent versions of the software, has prompted modelers to explicitly write down processing steps as source code to make them ‘easily’ reproducible.
This research describes the R package wrv, a collection of datasets and functions for pre- and post-processing the numerical groundwater flow model of the Wood River Valley aquifer system, south-central Idaho. R largely facilitates reproducible modeling with the package vignette; a document that is a combination of content and source code. The code is run when the vignette is built, and all data analysis output (such as figures and tables) is created on the fly and inserted into the final document. The wrv package includes two vignettes that explain and run steps that (1) create package datasets from raw data files located on a publicly accessible repository, and (2) create and run the groundwater flow model. MODFLOW-USG, the numerical groundwater model used in this study, is executed from the vignette, and model output is returned for exploratory analyses. The ability of R to perform all processing steps in a single workflow is attributed to its comprehensive list of features; that include geographic information system and time series functionality.