The Reproducible Routing Rituals (RRR) is a Python and bash shell toolbox that combines many repetitive pre and post-processing tasks that are common to studying the movements of water on and underneath the land surface. Such tasks include the preparation of files corresponding to:
- River network details (connectivity, parameters, sort, coordinates, subset)
- Contributing catchments information (area, coordinates)
- Reformatted land surface model outputs
- Coupling of LSM outputs and catchments to estimate water inflow into rivers
- Observed gauge data
- Analysis of these and associated data from a hydrological perpective
Vector-based ("blue line") river networks and associated contributing catchments can be used from the following datasets:
- The enhanced National Hydrography Dataset (NHDPlus, versions 1 and 2)
- The Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS)
- The Multi-Error-Removed Improved-Terrain (MERIT) Basins
Surface and subsurface runoff are obtained using model outputs from:
- The Global Land Data Assimilation System (GLDAS)
- The North American Land Data Assimilation System (NLDAS)
Water inflow from the land surface models and into the hydrographic networks are formatted for use within:
- The Routing Application for Parallel computatIon of Discharge (RAPID)
Observed gauges are gathered from:
- The National Water Information System (NWIS)
Hydrological data analysis is done for the above datasets, as well as model outputs from:
- The Routing Application for Parallel computatIon of Discharge (RAPID)
RRR is specifically designed to work hand-in-hand with RAPID. Further information on both RAPID and RRR can be found on the the RAPID website at: http://rapid-hub.org/.
Installing RRR is by far the easiest with Docker. This document was written and tested using Docker Community Edition which is available for free and can be installed on a wide variety of operating systems. To install it, follow the instructions in the link provided above.
Note that the experienced users may find more up-to-date installation instructions in Dockerfile.
Downloading RRR with Docker can be done using:
$ docker pull chdavid/rrr
The beauty of Docker is that there is no need to install anymore packages. RRR is ready to go! To run it, just use:
$ docker run --rm -it chdavid/rrr
Testing scripts are currently under development.
Note that the experienced users may find more up-to-date testing instructions in docker.test.yml.
This document was written and tested on a machine with a clean image of Debian 11.7.0 ARM64 installed, i.e. no update was performed, and no upgrade either. Similar steps may be applicable for Ubuntu.
Note that the experienced users may find more up-to-date installation instructions in github_actions_CI.yml.
First, make sure that git is installed:
$ sudo apt-get install -y --no-install-recommends git
Then download RRR:
$ git clone https://github.com/c-h-david/rrr
Finally, enter the RRR directory:
$ cd rrr/
Software packages for the Advanced Packaging Tool (APT) are summarized in
requirements.apt
and can be installed with apt-get. All packages can be installed at once using:
$ sudo apt-get install -y --no-install-recommends $(grep -v -E '(^#|^$)' requirements.apt)
Alternatively, one may install the APT packages listed in requirements.apt one by one, for example:
$ sudo apt-get install -y --no-install-recommends python3.9
Also make sure that python3 points to python3.9:
$ sudo rm -f /usr/bin/python3
$ sudo ln -s /usr/bin/python3.9 /usr/bin/python3
Python packages from the Python Package Index (PyPI) are summarized in
requirements.pip
and can be installed with pip. But first, let's make sure that the latest
version of pip is installed
$ wget https://bootstrap.pypa.io/pip/get-pip.py
$ sudo python3 get-pip.py --no-cache-dir `grep 'pip==' requirements.pip` `grep 'setuptools==' requirements.pip` `grep 'wheel==' requirements.pip`
$ rm get-pip.py
All packages can be installed at once using:
$ sudo pip3 install --no-cache-dir -r requirements.pip
Alternatively, one may install the PyPI packages listed in requirements.pip one by one, for example:
$ sudo pip3 install dbf==0.99.2
Testing scripts are currently under development.
Note that the experienced users may find more up-to-date testing instructions in github_actions_CI.yml.