pySWATPlus is an open-source Python package that provides a programmatic interface to the SWAT+ model, allowing users to run simulations and conduct custom experiments.
- Run
SWAT+simulations by modifying model parameters through thecalibration.calfile.. - Evaluate model performance against observed data using widely recognized statistical indicators.
- Perform sensitivity analysis on model parameters using the
SALibPython package. - Calibrate model parameters through multi-objective optimization and evolutionary algorithms using the
pymooPython package. - Execute sensitivity analysis and model calibration through high-level interfaces with built-in parallel computation support.
- Analyze outputs from model simulations, sensitivity analyses, and calibrations.
To install from PyPI repository:
pip install pySWATPlusTo install the latest development version from GitHub:
pip install git+https://github.com/swat-model/pySWATPlus.gitTo install from source in editable mode within your desired conda environment:
# Activate your Conda environment
conda activate <environment_name>
# Install required tools and clone the repository
pip install build
cd C:\Users\Username\Folder # Replace with your actual path
git clone https://github.com/swat-model/pySWATPlus.git
cd pySWATPlus
# Build the package
python -m build
# Install in editable mode
pip install --editable .The installation is successful if no error is raised when importing the module using the following command:
import pySWATPlusFor a guide to setting up first SWAT+ project and other functionalities with examples, refere to the pySWATPlus documentation.
If you use pySWATPlus in your research, please cite it using the following concept DOI, which always points to the latest version:
@software{joan_salo_pyswatplus_latest,
author = {Joan Saló and
Debasish Pal and
Oliu Llorente},
title = {swat-model/pySWATPlus},
year = 2025,
publisher = {Zenodo},
doi = {10.5281/zenodo.14889319},
url = {https://doi.org/10.5281/zenodo.14889319},
note = {This DOI always points to the latest version of pySWATPlus.},
}We acknowledge the University of Oulu and ICRA research center for their support and the collaborative environment that made this project possible.