Metadata-Version: 2.4
Name: smm-toolkit
Version: 0.1.4
Summary: 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.
Author-email: Mohammad Ali Farmani <mohammadalifarmani95@gmail.com>
Project-URL: Homepage, https://github.com/mfarmani95/SMM-Project
Project-URL: Documentation, https://pypi.org/project/smm-toolkit/
Project-URL: Source, https://github.com/mfarmani95/SMM-Project
Project-URL: Issue Tracker, https://github.com/mfarmani95/SMM-Project/issues
Project-URL: Related Paper, https://doi.org/10.5194/hess-29-547-2025
Keywords: hydrology,soil moisture,SMM,drydown analysis,land surface modeling,hydrologic modeling,Noah-MP,hydroclimate,timescales
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Hydrology
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.21
Requires-Dist: pandas>=1.3
Requires-Dist: xarray>=2023.1.0
Requires-Dist: netCDF4
Requires-Dist: scipy>=1.8
Requires-Dist: matplotlib>=3.5
Requires-Dist: PyYAML>=6.0
Dynamic: license-file

# 🛰️ Soil Moisture Memory (SMM) Toolkit

[![TestPyPI](https://img.shields.io/badge/TestPyPI-smm--toolkit-blue)](https://test.pypi.org/project/smm-toolkit/)
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[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
[![DOI](https://img.shields.io/badge/DOI-10.5194/hess--29--547--2025-blue)](https://doi.org/10.5194/hess-29-547-2025)

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](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:

```bash
pip install smm-toolkit

