SPEI

SPEI is a simple Python package to calculate drought indices for hydrological time series. This package uses popular Python packages such as Pandas and Scipy to make it easy and versatile for the user to calculate the drought indices. Pandas Series are great for dealing with time series; providing interpolation, rolling average, and other manipulation options. SciPy enables us to use all different kinds of distributions to fit the data. Different popular drought indices are supported such as the SPI (Standardized Precipitation Index), SPEI (Standardized Precipitation Evaporation Index), and SGI (Standardized Groundwater Index).

If you use this software for either the visualization and/or analysis, please cite this package via our article in the Journal of Open Source Software:

Vonk, M. A. (2025). SPEI: A Python package for calculating and visualizing drought indices. Journal of Open Source Software, 10(111), 8454. doi.org/10.21105/joss.08454.

Or cite a specific version in the Zenodo archive:

Vonk, M. A. (XXXX). SPEI: A simple Python package to calculate and visualize drought indices (vX.X.X). Zenodo. doi.org/10.5281/zenodo.10816740.

Available Drought Indices

The package is not limited to only these five drought indices. If any of the distributions in the Scipy library is valid on the observed hydrological series, the drought index can be calculated.

*For the calculation of potential evaporation, take a look at pyet. This is another great package that also uses pandas Series to calculate different kinds of potential evaporation time series.

Installation

To get the latest stable version install using:

pip install spei

To get the development version download or clone the GitHub repository to your local device. Install using:

pip install -e <download_directory>

Literature

This list of scientific literature is helpful as a reference to understand the context and application of drought indices.

1. Lloyd-Hughes, B. and M.A. Saunders (2002) - A Drought Climatology for Europe. DOI: 10.1002/joc.846
2. Vicente-Serrano, S.M., S. Beguería and J.I. López-Moreno (2010) - A Multi-scalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index. DOI: 10.1175/2009JCLI2909.1
3. Bloomfield, J.P. and B.P. Marchant (2013) - Analysis of groundwater drought building on the standardised precipitation index approach. DOI: 10.5194/hess-17-4769-2013
4. Babre, A., A. Kalvāns, Z. Avotniece, I. Retiķe, J. Bikše, K.P.M. Jemeljanova, A. Zelenkevičs and A. Dēliņa (2022) - The use of predefined drought indices for the assessment of groundwater drought episodes in the Baltic States over the period 1989–2018. DOI: 10.1016/j.ejrh.2022.101049
5. Vicente-Serrano, S. M., J. I. López-Moreno, S. Beguería, J. Lorenzo-Lacruz, C. Azorin-Molina, and E. Morán-Tejeda (2012). Accurate Computation of a Streamflow Drought Index. Journal of Hydrologic Engineering. American Society of Civil Engineers. DOI: 10.1061/(asce)he.1943-5584.0000433
6. Tijdeman, E., K. Stahl and L.M. Tallaksen (2020) - Drought characteristics derived based on the Standardized Streamflow Index: A large sample comparison for parametric and nonparametric methods. DOI: 10.1029/2019WR026315
7. Carrão. H., Russo, S., Sepulcre-Canto, G., Barbosa, P.: An empirical standardized soil moisture index for agricultural drought assessment from remotely sensed data. DOI: 10.1016/j.jag.2015.06.011s

Publications

These are scientific publications that use and cite this Python package:

Adla, S., Šaponjić, A., Tyagi, A., Nagi, A., Pastore, P., & Pande, S. (2024). Steering agricultural interventions towards sustained irrigation adoption by farmers: socio-psychological analysis of irrigation practices in Maharashtra, India. Hydrological Sciences Journal, 69(12), 1586–1603. https://doi.org/10.1080/02626667.2024.2376709

van Mourik, J., Ruijsch, D., van der Wiel, K., Hazeleger, W., & Wanders, N. (2025). Regional drivers and characteristics of multi-year droughts. Weather and Climate Extremes, 48, 100748. https://doi.org/10.1016/j.wace.2025.100748

Segura-Barrero, R., Lauvaux, T., Lian, J., Ciais, P., Badia, A., Ventura, S., Bazzi, H., Abbessi, E., Fu, Z., Xiao, J., Li, X., & Villalba, G. (2025). Heat and Drought Events Alter Biogenic Capacity to Balance CO2 Budget in South-Western Europe. Global biogeochemical cycles, 39(1), e2024GB008163. https://doi.org/10.1029/2024GB008163

Panigrahi, S., Vidyarthi, V.K. (2025). Assessing the Suitability of SPI and SPEI in Steppe Hot and Arid Climatic Zones in India. In: Sefelnasr, A., Sherif, M., Singh, V.P. (eds) Water Resources Management and Sustainability. Water Science and Technology Library, vol 114. Springer, Cham. https://doi.org/10.1007/978-3-031-80520-2_12

Ashcroft L, Ritman M, Bridgman H, Thornton K, Di Gravio G, Oates W, Belfield R, Belfield E. (2025). A climatology of meteorological droughts in New England, Australia, 1880–2022. Journal of Southern Hemisphere Earth Systems Science 75, ES25013. https://doi.org/10.1071/ES25013

Patidar, R., S. M. Pingale, D. Khare, and S. Choudhary (2025). “ Assessing Spatio-Temporal Meteorological Drought Dynamics Across Indian Agro-Climatic Zones: A Long-Term Perspective.” International Journal of Climatology 45, no. 16: e70146. https://doi.org/10.1002/joc.70146

Lekarkar, K., Rakovec, O., Kumar, R., Dondeyne, S., and van Griensven, A. (2025). Soil moisture droughts in Belgium during 2011–2020 were the worst in five decades, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-4526,

Butterfield, B. A., Furtado, J. C., Salazar, M. L., & Kuster, E. L. (2025). Assessing climate change and whiplash events in national parks. National Weather Center Research Experiences for Undergraduates Program.

Saponaro, V., Dalmonech, D., Vangi, E., Puchi, P. F., Rezaie, N., D’Andrea, E., Tomelleri, E., & Collalti, A. (2026). Climate change, more than management, drives short- and long-term changes in iWUE in a sub-Alpine beech forest. Journal of Forestry Research, 37(1), 16. https://doi.org/10.1007/s11676-025-01942-8