RAPID

The Routing Application for Parallel computatIon of Discharge (RAPID) is a river network routing model. Given surface and groundwater inflow to rivers, this model can compute flow and volume of water everywhere in river networks made out of many thousands of reaches.

For further information on RAPID including peer-reviewed publications, tutorials, sample input/output data, sample processing scripts and animations of model results, please go to: http://rapid-hub.org/.

Installation with Docker

Installing RAPID 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.

Download RAPID

Downloading RAPID with Docker can be done using:

$ docker pull chdavid/rapid

The images for RAPID on Docker Hub support CPU architectures for both AMD64 and ARM64!

Install packages

The beauty of Docker is that there is no need to install anymore packages. RAPID is ready to go! To run it, just use:

$ docker run --rm --name rapid -it chdavid/rapid

Testing with Docker

Testing scripts are currently under development.

Note that the experienced users may find more up-to-date testing instructions in docker.test.yml.

Installation on Debian

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.

Download RAPID

First, make sure that git is installed:

$ sudo apt-get install -y --no-install-recommends git

Then download RAPID:

$ git clone https://github.com/c-h-david/rapid

Finally, enter the rapid directory:

$ cd rapid/

Install APT packages

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 gfortran

Install netCDF

The Network Common Data Form (NetCDF) was already installed with apt-get.

However, the environment should be updated using:

$ export NETCDF_LIB='-L /usr/lib -lnetcdff'
$ export NETCDF_INCLUDE='-I /usr/include'

Note that these four lines can also be added in ~/.bash_aliases so that the environment variables persist.

Install PETSc

The Portable, Extensible Toolkit for Scientific Computation (PETSc) can be installed using:

$ mkdir $HOME/installz
$ cd $HOME/installz
$ wget "https://ftp.mcs.anl.gov/pub/petsc/release-snapshots/petsc-3.13.6.tar.gz"
$ tar -xzf petsc-3.13.6.tar.gz
$ cd petsc-3.13.6
$ python3 ./configure PETSC_DIR=$PWD PETSC_ARCH=linux-gcc-c --download-fblaslapack=1 --download-mpich=1 --with-cc=gcc --with-fc=gfortran --with-clanguage=c --with-debugging=0
$ make PETSC_DIR=$PWD PETSC_ARCH=linux-gcc-c all > all.log
$ make PETSC_DIR=$PWD PETSC_ARCH=linux-gcc-c check > check.log

Note that the following can be used as a replacement for the above code block:

$ mkdir $HOME/installz
$ ./rapid_install_prereqs.sh --installz=$HOME/installz

Then, the environment should be updated using:

$ export PETSC_DIR=$HOME/installz/petsc-3.13.6
$ export PETSC_ARCH=linux-gcc-c
$ export PATH=$PATH:$PETSC_DIR/$PETSC_ARCH/bin

Note that these three lines can also be added in ~/.bash_aliases so that the environment variables persist.

Build RAPID

$ cd rapid/
$ cd src/
$ make rapid

Testing on Debian

Testing scripts are currently under development.

$ cd rapid/
$ cd tst/
$ gfortran -o tst_run_comp tst_run_comp.f90 $NETCDF_INCLUDE $NETCDF_LIB
$ gfortran -o tst_run_cerr tst_run_cerr.f90 $NETCDF_INCLUDE $NETCDF_LIB
$ gfortran -o tst_run_conv_Qinit tst_run_conv_Qinit.f90 $NETCDF_INCLUDE $NETCDF_LIB

Note that the experienced users may find more up-to-date testing instructions in github_actions_CI.yml.