The dvm-dos-tem (dvmdostem) model is a process based bio-geo-chemical ecosystem model that focuses on C and N dynamics as well as soil thermal dynamics in high latitude ecosystems.

For more information see the User Guide.

For questions and to get involved please see the Github Issues and Github Discussions.

For users who have Docker and Git installed and some familiarity with the command line.

More details are available in the User Guide - Basic Model Setup and Run.

The settings shown here will use the input data that is shipped with the code in the repository's demo-data/ directory. The run will output a single variable (GPP), and will run for 2 pixels.

Clone the repository and change into the directory:

$ git clone https://github.com/uaf-arctic-eco-modeling/dvm-dos-tem.git
$ cd dvm-dos-tem

Make yourself locations for input data and your modeling workflows. We don't actually use the input directory for this quickstart, but creating it and setting the env variable below prevents some warnings.

$ mkdir -p /home/whoever/my-stuff/input
$ mkdir -p /home/whoever/my-stuff/workflows

On some systems you will need to make Docker usable by non-root users. See here.

Build Docker images. For this demo we only build 2 of the images. For your needs you might want to build the other images. See ./docker-build-wrapper.sh --help for more info.

$ ./docker-build-wrapper.sh cpp-dev
$ ./docker-build-wrapper.sh dev

Setup your environment variables V_TAG, DDT_INPUT_CATALOG, and DDT_WORKFLOWS. You can do this in a .env file, your .bashrc, or by exporting them to your shell. For example using the .env file:

$ echo "DDT_INPUT_CATALOG=/home/whoever/my-stuff/input" >> .env
$ echo "DDT_WORKFLOWS=/home/whoever/my-stuff/workflows" >> .env
$ echo "V_TAG=$(git describe)" >> .env

Start the Docker containers:

$ docker compose up -d dvmdostem-dev

Obtain a shell in the container:

$ docker compose exec dvmdostem-dev bash

Compile the code:

develop@56ef79004e31:/work$ make

Setup a working directory for your model run and change into it. The setup_working_directory.py script bootstraps a new directory to hold your model run. This includes a few configuraiton files and a folder for the outputs.

devleop@56ef79004e31:/work$ setup_working_directory.py --input-data-path demo-data /data/workflows/sample_run
devleop@56ef79004e31:/work$ cd /data/workflows/sample_run

Adjust run mask, config, outputs as necessary. The default settings should run 1 or 2 pixels and produce outputs for GPP. There are more tools provided in the scripts/ directory for working with the config file, run mask, output specificaitons and parameters.

Start the model run:

develop@56ef79004e31:/data/workflows/sample_run$ ./dvmdostem --log-level monitor -p 100 -e 1000 -s 250 -t 115 -n 85

Visualize run:

develop@56ef79004e31:/data/workflows/sample_run$ /work/scripts/viewers/plot_output_var.py --yx 0 0 --file output/GPP_yearly_tr.nc

This project has a very basic CI workflow implemented using Github Actions. When a pull request is made to the master branch, an action is run that checks out the code, builds one of the Docker images, compiles the dvmdostem binary and runs the model using the demo data for a short number of years. This test does not confirm any of the scientific aspects of the model and does not exercise any of the supporting pre- and post-processing tools. Extending and improving the CI coverage remains an open project. We would like for future development to improve overall test coverage and setup more actions to run the tests. For more information about the testing and CI, see the Testing and Deployment of the User Guide.