🌊 BFI-Toolkit

A lightweight and flexible Python package for estimating Baseflow and Baseflow Index (BFI) from streamflow time series.

This toolkit provides a simple, efficient way to:

• Separate baseflow using forward/backward recession analysis
• Compute optimized decay parameters (k)
• Calculate BFI — a key hydrologic metric for understanding watershed storage and runoff behavior.

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📄 Associated Research

This toolkit was developed as part of the work presented in:

Farmani, M. A., Tavakoly, A., Behrangi, A., Qiu, Y., Gupta, A., Jawad, M., Yousefi Sohi, H., Zhang, X., Geheran, M., Niu, G.-Y. (2025).
Improving Streamflow Predictions in the Arid Southwestern United States Through Understanding of Baseflow Generation Mechanisms.
Water Resources Research. https://doi.org/10.1029/2024WR039479

© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.

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🧠 Scientific Background

This toolkit builds on established methods for baseflow estimation using recession analysis and BFI calculation.
The underlying baseflow recession concept is based on:

van Dijk, A. I. J. M. (2010). Climate and terrain factors explaining streamflow response and recession in Australian catchments.
Hydrology and Earth System Sciences, 14(1), 159–169.
https://doi.org/10.5194/hess-14-159-2010

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📦 Installation

From PyPI (when released)

pip install bfi-toolkit

From source (development version)

git clone https://github.com/mfarmani95/BFI-Toolkit.git
cd BFI-Toolkit
pip install -e .

This installs the package in editable mode, so any code edits are reflected immediately without reinstalling.

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⚡️ Quick Start

import numpy as np
from bfi_toolkit import compute_bfi

# Example: simple synthetic streamflow data
streamflow = np.array([5, 5, 5, 6, 7, 6, 5.8, 5.6, 5.4, 5.2, 5, 4.9, 4.8])

result = compute_bfi(streamflow, day_after_peak=2,min_events=1,min_pairs=3, start_date="2020-01-01")

# day_after_peak : Number of days to exclude after each detected runoff peak.
# min_events : Minimum number of runoff events required.
# min_pairs :  Minimum number of Q–Q* pairs required.

print("Optimized k:", result["k"])
print("BFI:", result["bfi"])
print("Baseflow head:\n", result["baseflow"].head())

✅ Supported input types:

• Python list
• numpy.ndarray
• pandas.DataFrame (must contain column QQ)
• torch.Tensor (optional, if PyTorch installed)

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🧪 Development Setup

Option 1: with pip

git clone https://github.com/mfarmani95/BFI-Toolkit.git 
cd BFI-Toolkit
python -m venv .venv
source .venv/bin/activate   # (Windows: .venv\Scripts\activate)
pip install -r requirements-dev.txt

Option 2: with Conda (recommended)

git clone https://github.com/mfarmani95/BFI-Toolkit.git 
cd BFI-Toolkit
conda env create -f environment.yml
conda activate bfi-toolkit

Then:

make dev     # install dev dependencies
make test    # run tests

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🧰 Useful Makefile Commands

Command	Description
make install	Install package in editable mode
make dev	Install dev dependencies
make test	Run test suite with pytest
make format	Auto-format code with Black
make lint	Run static checks with Ruff
make build	Build distribution package
make upload-test	Upload to TestPyPI
make upload	Upload to PyPI

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🧠 Contributing

We welcome contributions from the community 🙌

1. Fork the repository
2. Create a feature branch
3. Commit your changes with clear messages
4. Add or update tests if needed
5. Submit a Pull Request

Before submitting:

make format
make lint
make test

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🧭 Features

• ✅ Supports multiple input formats (NumPy, Pandas, PyTorch, lists)
• 📉 Flexible dry period filtering with day_after_peak
• ⚙️ Optimized decay constant estimation (k)
• 💧 Baseflow separation using forward/backward recession
• 📊 Automatic BFI calculation
• 🧰 Clean modular structure (core, utils, optimization, baseflow)
• 🧪 Fully testable with pytest

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📝 Citation

If you use this toolkit in your research or operational projects, please cite:

Farmani, M. A. (2025). BFI-Toolkit: A lightweight Python package for estimating baseflow and Baseflow Index (BFI).
GitHub: https://github.com//BFI-Toolkit

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📜 License

This project is licensed under the MIT License.
You’re free to use, modify, and distribute it — just give credit where it’s due.

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🚀 Roadmap

• Add uncertainty quantification for k estimates
• Add multi-resolution time series support (weekly, monthly)
• Add visualization module (hydrograph plotting)
• Add unit conversion utilities for different discharge formats
• Publish as official PyPI package

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💧 Acknowledgements

Developed as part of ongoing hydrologic research by
Mohammad Ali Farmani — University of Arizona.

Inspired by the need for transparent, flexible, and efficient baseflow separation tools for hydrologic modeling and water resources applications.