Vonk (2025) SPEI: A Python package for calculating and visualizing drought indices

Identification

• Journal: The Journal of Open Source Software
• Year: 2025
• Date: 2025-07-29
• Authors: Martin A. Vonk
• DOI: 10.21105/joss.08454

Research Groups

• Department of Water Management, Faculty of Civil Engineering and Geosciences, Delft University of Technology, The Netherlands
• Artesia B.V., The Netherlands

Short Summary

The paper introduces SPEI, a Python package designed to streamline the calculation and visualization of various meteorological, hydrological, and agricultural drought indices from time series data.

Objective

• To provide a flexible, open-source software tool for computing and visualizing standardized and non-standardized drought indices by leveraging the Python scientific ecosystem (Pandas, SciPy, and Matplotlib).

Study Configuration

• Spatial Scale: Not applicable (Software package; example data provided for the Netherlands).
• Temporal Scale: Not applicable (Software package; supports time series typically $\ge 30$ years with aggregation scales of 1, 3, 6, 12, 24, or 48 months).

Methodology and Data

• Models used:
  • Standardized indices: Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Groundwater Index (SGI), Standardized Streamflow Index (SSFI/SSI), and Standardized Soil Moisture Index (SSMI).
  • Non-standardized indices: Rainfall Anomaly Index (RAI), Modified RAI (mRAI), Climdex indices, and KNMI precipitation deficit.
  • Computational tools: Pandas (time series handling), SciPy (probability distribution fitting and Z-score conversion), and Matplotlib (visualization).
• Data sources: Example implementation uses daily precipitation and potential evaporation data from the Royal Netherlands Meteorological Institute (KNMI).

Main Results

• Developed a package that automates the process of fitting continuous probability distributions (e.g., Gamma for SPI, Fisk for SPEI) to time series and converting them into Z-scores.
• Implemented visualization tools, including multi-scalar heatmaps to identify the persistence and build-up of multi-year droughts and threshold-based drought identification (fixed and variable).
• Enabled the use of over 200 univariate continuous distributions available via SciPy for flexible index calculation.

Contributions

• Offers a unified and flexible Python-based framework that reduces the complexity of calculating multiple drought indices.
• Integrates advanced distribution fitting and time-series aggregation (handling varying month lengths) into a single accessible tool for the scientific community.

Funding

• Not specified in the provided text.

Citation

@article{Vonk2025SPEI,
  author = {Vonk, Martin A.},
  title = {SPEI: A Python package for calculating and visualizing drought indices},
  journal = {The Journal of Open Source Software},
  year = {2025},
  doi = {10.21105/joss.08454},
  url = {https://doi.org/10.21105/joss.08454}
}