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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.

Project description

🛰️ Soil Moisture Memory (SMM) Toolkit

TestPyPI PyPI License: MIT DOI

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.


🌿 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

🧰 Installation

You can install the package directly from PyPI:

pip install smm-toolkit

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