eu-cbm-hat 0.7.1

eu_cbm_hat is a python package for running carbon budget simulations.

EU-CBM-HAT

The forest carbon model eu_cbm_hat is a python package that enables the assessment of forest CO2 emissions and removals under scenarios of forest management, natural disturbances, forest-related land use changes.

EU-CBM-HAT depends on the libcbm model developed by Forest Carbon Accounting team of the Canadian Forest Service. Both python modules use pandas data frames to transform and load data.

Documentation

The model was described in a 2022 JRC Technical Report called The JRC Forest Carbon Model: description of EU-CBM-HAT

See also

• Installation instructions below

• Software documentation at https://bioeconomy.gitlab.io/eu_cbm/eu_cbm_hat/eu_cbm_hat.html

Licence

This program is free software: you can redistribute it and/or modify it under the terms of the European Union Public Licence, either version 1.2 of the License, or (at your option) any later version. See LICENCE.txt and NOTICE.txt for more information on the licence of components.

Dependencies

• libcbm is a C++ library with python binding developed by the Canadian Forest Service. It is bundled into the libcbm_py python package available at https://github.com/cat-cfs/libcbm_py

• eu_cbm_data contains the model's input and output data located at https://gitlab.com/bioeconomy/eu_cbm/eu_cbm_data . In 2022, this is a private repository subject to ongoing research.

• eu_cbm_aidb contains the "Archive Index Databases" in a separate repository located at https://gitlab.com/bioeconomy/eu_cbm/eu_cbm_aidb

Installation

If you have never used python before and if you are on Windows, you might want to install Anaconda on your system, it will help you with managing packages dependencies. You also need to install git in order to install python packages from git repositories.

Install eu_cbm_hat using pip, the package installer for python in the shell (or conda console).

pip install eu_cbm_hat
# or
python -m pip install eu_cbm_hat

Install libcbm using pip. Note: currently only version 1 is supported. Update to version 2 is under discussion in issue 53:

python -m pip install https://github.com/cat-cfs/libcbm_py/archive/refs/heads/1.x.tar.gz

Over time it's important to regularly upgrade the 2 packages with:

python -m pip install --upgrade eu_cbm_hat
python -m pip install --upgrade https://github.com/cat-cfs/libcbm_py/archive/refs/heads/1.x.tar.gz

In case you need to install the latest development version, use the --upgrade parameter and install from the main branch of the gitlab repository:

python -m pip install --upgrade --force-reinstall https://gitlab.com/bioeconomy/eu_cbm/eu_cbm_hat/-/archive/main/eu_cbm_hat-main.tar.gz

By default, the data is located in your home folder. You can display the default location where the data should be with these commands in python:

>>> import eu_cbm_hat
>>> eu_cbm_hat.eu_cbm_data_dir
>>> eu_cbm_hat.eu_cbm_aidb_dir

	On Unix	On windows
Data	~/eu_cbm/eu_cbm_data/	C:\Users\user_name\eu_cbm\eu_cbm_data
Archive Index Database	~/eu_cbm/eu_cbm_aidb/	C:\Users\user_name\eu_cbm\eu_cbm_aidb

The model will work once these folders exist on your system. Optionally, you can define the environment variables EU_CBM_DATA and EU_CBM_AIDB to tell the model where the data and AIDB are located.

Copy test data to your local eu_cbm_data folder (location defined above in python in eu_cbm_hat.eu_cbm_data_dir):

>>> from eu_cbm_hat.tests.copy_data import copy_test_data
>>> copy_test_data()

Clone the repository containing the AIDB (with a deploy token) inside your home folder in the parent directory of the path given by eu_cbm_hat.eu_cbm_aidb_dir. Back to the shell (or conda console):

git clone https://gitlab.com/bioeconomy/eu_cbm/eu_cbm_aidb.git

Before running the model, you need to create AIDB symlinks at a python prompt:

>>> from eu_cbm_hat.core.continent import continent
>>> for country in continent: country.aidb.symlink_all_aidb()

Installation for development purposes

Skip this section if you do not intend to change the code of the model. For development purposes, these instruction leave the capability to modify the code of the model and submit changes to the git repositories composing the model. Extensive installation instructions are available for two different platforms:

• Installation on Linux
• Installation on Windows

Running the model

Run the test country ZZ at a python prompt:

from eu_cbm_hat.core.continent import continent
runner = continent.combos['reference'].runners['ZZ'][-1]
runner.num_timesteps = 30
runner.run(keep_in_ram=True, verbose=True, interrupt_on_error=True)

Run a scenario combination

Run a scenario combination for all EU countries at once (see the documentation on combos for how to specify them):

cd $HOME/eu_cbm/eu_cbm_hat/scripts/running/
ipython -i run_scenario_combo.py -- --combo_name reference --last_year 2050
ipython -i run_scenario_combo.py -- --combo_name pikssp2 --last_year 2070
ipython -i run_scenario_combo.py -- --combo_name pikfair --last_year 2070

Process the output data for a list of scenario combinations provided as an argument. Compute the harvest expected and provided as well as the sink in an aggregated way:

cd $HOME/eu_cbm/eu_cbm_hat/scripts/post_processing
ipython -i process_scenario_combo.py -- --combo_names reference pikssp2 pikfair

Inspect the model output

Inspect the output of the model

# Input events sent to libcbm
events_input = runner.input_data["events"]
# Events stored in the output including the ones related to the harvest
# allocation tool HAT
events_output = runner.output["events"]
# Available volumes used by the Harvest Allocation Tool
output_extras = runner.output.extras

# Load tables without classifiers
area = runner.output.load('area', with_clfrs=False)
params = runner.output.load('parameters', with_clfrs=False)
flux = runner.output.load('flux', with_clfrs=False)
state = runner.output.load('state', with_clfrs=False)

# Load classifiers with their actual values
classifiers = runner.output.classif_df
classifiers["year"] =  runner.country.timestep_to_year(classifiers["timestep"])

# Merge tables
index = ['identifier', 'year']
flux_dist = (params
             .merge(area, 'left', on = index) # Join the area information
             .merge(flux, 'left', on = index)
             .merge(state, 'left', on = index) # Join the age information
             .merge(classifiers, 'left', on = index) # Join the classifiers
             )

Testing

All dependencies are clearly stated in .gitlab-ci.yml and the setup.py files at the root of the repository. In fact those 2 files are used to automatically install and test the install each time we make a change to the model. The test consist in unit tests as well as running a mock country called "ZZ". You can see the output of these runs (successful or not) in the CI-CD jobs page on gitlab.

Definitions and specification

• A specification for an Harvest Allocation Tool (HAT) is available at docs/harvest_allocation_specification.md

• Input files (disturbances, yield, inventory) defined in eu_cbm_data contain scenarios for the activities (afforestation, deforestation, reforestation, disturbances in forest remaining forest, wood use specified in the silviculture and product_types.csv tables)

Extra documentation

More documentation is available at: https://bioeconomy.gitlab.io/eu_cbm/eu_cbm_hat/eu_cbm_hat.html

This documentation is simply generated in .gitlab-ci.yml with:

$ pdoc -o public ./eu_cbm_hat