smm-toolkit 0.1.4.2

A Python toolkit for computing Soil Moisture Memory (SMM) based on drydown analysis and exponential decay fitting, as described in Farmani et al. (2025), Hydrology and Earth System Sciences.

🛰️ Soil Moisture Memory (SMM) Toolkit

This repository provides a Python package for computing Soil Moisture Memory (SMM), as applied in the paper:

Farmani, M. A., Behrangi, A., Gupta, A., Tavakoly, A., Geheran, M.,
“Do land models miss key soil hydrological processes controlling soil moisture memory?”
Hydrology and Earth System Sciences (HESS), 29, 547–564, 2025.
https://doi.org/10.5194/hess-29-547-2025
© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.

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🌿 Overview

The SMM Toolkit detects and analyzes soil moisture drydowns and computes short-term soil moisture memory timescales (Ts) from time series of soil moisture and precipitation.

Key features:

• 📈 Automatic drydown detection
• 🧪 Exponential curve fitting and R² filtering
• 🕒 Short-term timescale (Ts) computation for positive increments
• 📊 Plotting and result export
• 🧰 YAML-based configuration for reproducible runs
• ⚡ PyPI installable & CI tested

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🧰 Installation

You can install the package directly from PyPI:

pip install smm-toolkit

Or from TestPyPI for development testing:

pip install -i https://test.pypi.org/simple/ smm-toolkit

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🚀 Quick Start

import logging
from pathlib import Path
from smm.main import SMM
from smm.utils.logger import setup_logger

if __name__ == "__main__":
    setup_logger()
    config_path = Path("/path/to/config.yml")
    logging.info(f"Starting SMM analysis using config: {config_path}")
    SMM(config_path)

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🧾 Example YAML Configuration

data:
  sm_file: "/path/to/SM.nc"
  prcp_file: "/path/to/precip_2019-01.nc"
  var_sm: "SM_Var_Name"
  var_prcp: "PREC_Var_Name"
  lat: 431          # can be index or actual coordinate
  lon: -111.2       # can be index or actual coordinate

parameters:
  thickness: 0.1
  threshold_factor: 0.12
  min_length: 3
  max_zeros: 0
  max_consecutive_positives: 1
  max_gap_days: 6
  r2_threshold: 0.8
  dim: "time"
  precip_length_unit: mm
  precip_time_unit: s
  sm_timestep: day

output:
  save_dir: "path/to/output"
  plot: true
  save_csv: true

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🧪 Development Setup

git clone https://github.com/mfarmani95/SMM-Toolkit.git
cd SMM-Toolkit
python -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
pip install -e .

For conda users:

conda env create -f environment.yml
conda activate smm-toolkit

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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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📜 Citation

If you use this toolkit, please cite:

Farmani, M. A., Behrangi, A., Gupta, A., Tavakoly, A., Geheran, M. (2025).
Do land models miss key soil hydrological processes controlling soil moisture memory?
Hydrology and Earth System Sciences, 29, 547–564.
DOI: 10.5194/hess-29-547-2025

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📜 License

This project is licensed under the MIT License.
© 2025 Mohammad A. Farmani.

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💧 Acknowledgements

Developed as part of hydrologic research at the University of Arizona.
Inspired by the need for reproducible, transparent, and flexible soil moisture memory analysis tools for land–atmosphere interaction studies.