pydrght 0.2.1

A comprehensive Python package for drought analysis, with standardized indices (SPI, SPEI, SSFI) and copula-based bivariate MSDI for multivariate drought characterization.

PyDRGHT

A comprehensive Python package for drought analysis, with standardized indices (SPI, SPEI, SSFI) and copula-based bivariate MSDI for multivariate drought characterization.

PyDRGHT provides tools for univariate and multivariate drought assessment, combining standardized indices, classical indices, copula-based methods, and frequency analysis.

PyDRGHT is built for monthly hydrometeorological time series and supports any dataset that can be converted to a pd.Series.

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Features

• Distribution Fitting Methods

  • PyDRGHT provides two independent frameworks for fitting probability distributions required in standardized drought indices (SPI, SPEI, SSFI, etc.):
  • Maximum Likelihood Estimation (MLE): Implemented using scipy.stats continuous distributions
  • Unbiased Probability Weighted Moments (UB-PWM): Based on L-moments and unbiased PWMs

• Standardized indices (based on SPI methodology with different variables):

  • SPI (Standardized Precipitation Index)
  • SPEI (Standardized Precipitation Evapotranspiration Index)
  • SSFI (Standardized Streamflow Index)
  • SGI (Standardized Groundwater Index)
  • SSMI (Standardized Soil Moisture Index)

• Classical drought indices:

  • RDI (Reconnaissance Drought Index)
  • RAI (Rainfall Anomaly Index)
  • PNI (Percent of Normal Index)
  • CZI (China-Z Index)
  • DI (Deciles Index)

• Multivariate drought indices:

  • MSDI (Multivariate Standardized Drought Index) – both empirical and copula-based approaches
  • TSDI (Trivariate Standardized Drought Index) - empirical approach

• Drought characteristics:

  • Duration, severity, intensity, frequency
  • Start and end of drought events
  • Interarrival times between droughts

• Frequency analysis:

  • Univariate frequency analysis
  • Copula-based BFA (Bivariate Frequency Analysis)

• Additional tools:

  • Potential Evapotranspiration (PET) via Hargreaves and Thornthwaite methods
  • Dependence modeling with copulas (Archimedean, Elliptical, Extreme-value families)
  • Advanced plotting utilities with automatic drought-severity shading, visually highlighting values below 0 (mild dryness), −1 (moderate drought), and −2 (severe drought).

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Installation

pip install pydrght

Or from source:

git clone https://github.com/terzitb/pydrght.git
cd pydrght
pip install -e .

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Quickstart

import pandas as pd
from pydrght import SI
from scipy.stats import gamma

# Load example precipitation data
data = pd.read_csv("data.csv", index_col=0, parse_dates=True)

# 12-month SPI (parametric, 3-p Gamma distribution)
spi = SI(data["PRECIPITATION"], ts=12)
spi_param = spi.fit_parametric(gamma, is_2p=False)

print(spi_param.head())

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Package Structure

Module / Subpackage	Description
pydrght.BFA	Bivariate Frequency Analysis for drought severity & duration modeling
pydrght.CZI	China-Z Index (CZI)
pydrght.DChar	Drought Characteristics (duration, severity, frequency, etc.)
pydrght.DI	Deciles Index (DI)
pydrght.Dist	Distribution fitting
pydrght.MSDI	Multivariate Standardized Drought Index (MSDI)
pydrght.PNI	Percent of Normal Index (PNI)
pydrght.RAI	Rainfall Anomaly Index (RAI)
pydrght.RDI	Reconnaissance Drought Index (RDI)
pydrght.SI	Standardized Index class for SPI, SPEI, SSFI, SGI, SSMI
pydrght.TSDI	Trivariate Standardized Drought Index
pydrght.copulas	Copula classes: ClaytonCopula, FrankCopula, GumbelCopula, GaussianCopula, GalambosCopula, PlackettCopula
pydrght.pet	Potential Evapotranspiration (PET) methods: hargreaves, thornthwaite
pydrght.methods	Unbiased-PWM L-moment estimators and CDF functions for: Gamma, Generalized Logistic and, Pearson III
pydrght.indices	Standardized index subclasses using default probability distributions: SPI, SPEI, SGI, SSFI and, SSMI
pydrght.utils	Utility functions: uni_emp, multi_emp, accu, tri_emp,plot_index_with_severity
pydrght/examples	Example datasets (.csv) and usage notebooks (.ipynb)

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Examples & Data

Check the examples/ folder for:

• Sample datasets (data.csv, spi.csv, dchar.csv)
• Jupyter notebooks (example_SI.ipynb, example_MSDI.ipynb, etc.)

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References

• McKee, T. B., Doesken, N. J., & Kleist, J. (1993). The relationship of drought frequency and duration to time scales. Proceedings of the 8th Conference on Applied Climatology, 179–184.

• Vicente-Serrano, S. M., Beguería, S., & López-Moreno, J. I. (2010). A multiscalar drought index sensitive to global warming: The Standardized Precipitation Evapotranspiration Index. Journal of Climate, 23(7), 1696–1718. DOI: 10.1175/2009JCLI2909.1

• Shukla, S., & Wood, A. W. (2008). Use of a standardized runoff index for characterizing hydrologic drought. Geophysical Research Letters, 35(2), L02405. DOI: 10.1029/2007GL032487

• Sklar, A. (1959). Fonctions de répartition à n dimensions et leurs marges. Publications de l'Institut de Statistique de l'Université de Paris, 8, 229–231. DOI: 10.2139/ssrn.4198458

• Gibbs, W. J., & Maher, J. V. (1967). Rainfall deciles as drought indicators. Bureau of Meteorology, Australia.

• Hayes, M. J. (1999). Drought Indices. National Drought Mitigation Center, University of Nebraska-Lincoln.

• van Rooy, M. P. (1965). A rainfall anomaly index (RAI) independent of time and space. Notos, 14, 43–48.

• Hänsel, S., Schucknecht, A., & Matschullat, J. (2016). The Modified Rainfall Anomaly Index (mRAI)—is this an alternative to the Standardised Precipitation Index (SPI) in evaluating future extreme precipitation characteristics? Theoretical and Applied Climatology, 123, 827–844. DOI: 10.1007/s00704-015-1389-y

• Tsakiris, G., & Vangelis, H. (2005). Establishing a drought index incorporating evapotranspiration. European Water, 9/10, 3–11.

• Hayes, M., Svoboda, M., Wall, N., & Widhalm, M. (2011). The Lincoln Declaration on Drought Indices: Universal Meteorological Drought Index Recommended. Bulletin of the American Meteorological Society, 92(4), 485–488. DOI: 10.1175/2010BAMS3103.1

• Farahmand, A., & AghaKouchak, A. (2015). A generalized framework for deriving nonparametric standardized drought indicators. Advances in Water Resources, 76, 140–145. DOI: 10.1016/j.advwatres.2014.11.012

• Hao, Z., & AghaKouchak, A. (2013). Multivariate standardized drought index: a parametric multi-index model. Advances in Water Resources, 57, 12–18. DOI: 10.1016/j.advwatres.2013.03.009

• Hao, Z., & AghaKouchak, A. (2014). A nonparametric multivariate multi-index drought monitoring framework. Journal of Hydrometeorology, 15(1), 89–101. DOI: 10.1175/jhm-d-12-0160.1

• Wu, H., Hayes, M. J., Weiss, A., & Hu, Q. (2001). An evaluation of the Standardized Precipitation Index, the China-Z Index and the statistical Z-Score. International Journal of Climatology, 21(6), 745–758. DOI: 10.1002/joc.658

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License

PyDRGHT is licensed under the MIT License – see the LICENSE file for details. Please cite the package if you use it in your work.

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Citation

If you use PyDRGHT in your research, please cite the following article:

Terzi, T.B., 2026. PyDRGHT: A comprehensive python package for drought analysis. Environmental Modelling & Software 197, 106847. https://doi.org/10.1016/j.envsoft.2025.106847

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