🌲 GOBLIN_CBM_runner, a CBM CFS3 interface for the GOBLIN model

Based on the GOBLIN (General Overview for a Backcasting approach of Livestock INtensification) LifeCycle Analysis tool, the cbm_runner package generates the data requried for the CBM CFS3 (libcbm_py) tool. It also interfaces with the tool directly, generating results in a single dataframe for all scenarios.

The outputs are related to biomass, and dead organic matter. These are summed into a total ecosystem value.

The estimated volumns are all in t of C.

Installation

Install from git hub.

pip install "goblin_cbm_runner@git+https://github.com/GOBLIN-Proj/goblin_cbm_runner.git@main" 

Install from PyPI

pip install goblin_cbm_runner

Usage

The Runner class takes the total afforestation area and divides it evenly across years (calibration year - target year).

from goblin_cbm_runner.default_runner.runner import Runner
from goblin_cbm_runner.resource_manager.cbm_runner_data_manager import DataManager
import pandas as pd
import os


def main():
    # path to data
    path = "./data/runner_input"
    results_path = "./data/runner_results"

    # afforestation data for each scenario
    afforest_data = pd.read_csv(
        os.path.join(path, "cbm_afforestation.csv"), index_col=0
    )

    # basic configuration file
    config = os.path.join(path, "cbm_factory.yaml")

    # scenario_data
    sc_data = pd.read_csv(os.path.join(path, "scenario_dataframe.csv"))

    # calibration and end point
    calibration_year = 2020

    # instance of the DataManager class
    data_manager = DataManager(calibration_year = calibration_year,
                            config_file_path=config,
                            scenario_data=sc_data,
                            afforest_data=afforest_data)
    
    # instance of the Runner class
    runner = Runner(data_manager)

    # afforeation data
    runner.get_afforestation_dataframe().to_csv(os.path.join(results_path, "c_afforestation.csv"))

    # generation of aggregated results
    runner.run_aggregate_scenarios().to_csv(os.path.join(results_path, "c_aggregate.csv"))

    # generation of annual flux results
    runner.run_flux_scenarios().to_csv(os.path.join(results_path, "c_flux.csv"))


if __name__ == "__main__":
    main()

CBM Disturbance Sort Types Note

libcbm_py is not a direct conversion of the CBM-CFS3 model, and there are some small differences.

One key difference is the available sort types for stand disturbances. Some sorting options in CBM-CFS3 are not available in libcbm_py, while others may function slightly differently.

In particular, the "Sort by time since softwood component was last harvested" (4) and "Sort by time since hardwood component was last harvested" (13) are missing from libcbm_py. These sorts help prioritize stands based on past harvests, which can be important for disturbance modeling.

Below, we provide the full set of sort types from CBM-CFS3, followed by the available sorts in libcbm_py, along with suggested approximations for missing sorts.

Sort Types from CBM-CFS3

The following table lists all disturbance sort types available in CBM-CFS3:

Available Sort Types in libcbm_py

Below are the disturbance sort types that are implemented in libcbm_py:

{
    1: "PROPORTION_OF_EVERY_RECORD",
    2: "MERCHCSORT_TOTAL",
    3: "SORT_BY_SW_AGE",
    5: "SVOID",
    6: "RANDOMSORT",
    7: "TOTALSTEMSNAG",
    8: "SWSTEMSNAG",
    9: "HWSTEMSNAG",
    10: "MERCHCSORT_SW",
    11: "MERCHCSORT_HW",
    12: "SORT_BY_HW_AGE",
}

License

This project is licensed under the terms of the MIT license.