During the course of your training in the Wood River Valley (WRV) groundwater-flow model, you will learn to run and analyze the calibrated model and adapt the model for a water use scenario. The groundwater-flow model is described in the associated model report and model archive. Processing programs and unprocessed data sets for the model are bundled together in an R package named wrv—the file repository for this package is located on GitHub. Included in the wrv package is MODFLOW-USG version 1.3, a U.S. Geological Survey (USGS) computer code that solves the groundwater-flow equation.

Hardware

Requires an internet connection and about 16 gigabytes of memory.

Software

If R (version 3.3 or greater) is not already installed on your computer, download and install the latest binary distribution from the Comprehensive R Archive Network (CRAN). Installation instructions for installing R are located at https://jfisher-usgs.github.io/r-workshop/software.html#r. R should be accessible from the start menu, desktop shortcut, or command shell. Assuming you installed R on a 64-bit operating system (OS), two versions of R are made available, that is, a 32-bit and 64-bit version. The 64-bit version of R (R x64) is preferred. An introduction to the R-programming language is provided at https://jfisher-usgs.github.io/r-workshop.

Startup an R session. Extend the capabilities of R by installing the R-package wrv. You can install the stable version of wrv from the Geological Survey R Archive Network (GRAN), and its dependencies from CRAN, by pasting the following commands into your R console:

repos <- c(GRAN = "https://owi.usgs.gov/R", CRAN = "https://cloud.r-project.org/")
update.packages(ask = FALSE, repos = repos)
install.packages("wrv", repos = repos, dependencies = TRUE)

You will only need to install R packages once on your computer. To ensure that everything got installed properly, load the wrv package in your current R session using

library("wrv")

Files

Files used in this training class were placed online in the wrv-training repository. Create a copy of the latest version of the repository on your computer using R. To do so, first specify where you want to place the repository in your local file system. Type the following commands substituting in the desired path; for example, I changed the path from "<path/to/repository>" to "D:/WORK/Repos/wrv-training" making sure to use the forward slash (/) to separate folder names:

path <- "<path/to/repository>"
dir.create(path, recursive = TRUE)

Next, copy the remote repository to your computer, using

git2r::clone("https://github.com/jfisher-usgs/wrv-training.git", path)

All subsequent R instructions require the working directory be set to the local wrv-training repository every time you start an R session. A working directory allows us to use relative paths instead of full absolute paths in our R scripts. An absolute path points to the same location in a file system, regardless of the current working directory. By contrast, a relative path starts from some given working directory, avoiding the need to provide the full absolute path. Use the setwd R function to set the working directory to the local wrv-training repository specified above.

setwd(path)

Contact

This website is a work in progress. Please consider reporting bugs and asking for clarification on the Issues page. Or you can just contact me via email at jfisher@usgs.gov