Overview

What is pyTENAX?

PyTENAX contains a set of methods to apply The TEmperature-dependent Non-Asymptotic statistical model for eXtreme return levels (TENAX).

pyTENAX is essentially the Pythonized version of the TENAX MATLAB code for this model. The link to original repository in Cross-Language Implementations.

The model is based on a parsimonious non-stationary and non-asymptotic theoretical framework that incorporates temperature as a covariate to estimate changes in precipitation return levels.

The model is presented in:
Marra, F., Koukoula, M., Canale, A., & Peleg, N. (2023).
Predicting extreme sub-hourly precipitation intensification based on temperature shifts.
Hydrology and Earth System Sciences Discussions, 2023, 1-23.
https://doi.org/10.5194/hess-28-375-2024

Cross-Language Implementations

Original TENAX model has been developed in MATLAB:
TEmperature-dependent Non-Asymptotic statistical model for eXtreme return levels (TENAX)
Source code: https://doi.org/10.5281/zenodo.8332232

Feature

PYTHON

MATLAB

Ordinary Events

✅

✅

Magnitude Model

  • Estimate 4 parameters

✅

✅

  • Alpha value test on scale param.

✅

✅

  • Fixed-b parameter estimation

✅

❌

  • Exponential-b

✅ (beta testing)

❌

Temperature Model

  • Generalized Gaussian dist.

✅

✅

  • Skewed Normal (β fitted)

✅ (beta testing)

❌

Developer community

Current pyTENAX developers:

  • Petr Vohnicky (PhD student at the University of Padova; petr.vohnicky@unipd.it)

  • Ella Thomas (Research Assistant at the University of Padova)

  • Jannis Hoch (Senior Hydrologist at Fathom)

  • Rashid Akbary (PhD student at the University of Padova)

  • Yaniv Goldschmidt (Research Assistant at the University of Padova)

We would like to express our gratitude to Riccardo Ciceri (riccardo.ciceri@studenti.unipd.it) for his contribution to the initial development phase of pyTENAX.

Important notes

pyTENAX also includes SMEV class (Simplified Metastatistical Extreme Value)

For more information about SMEV, please see manuscripts:
Francesco Marra, Davide Zoccatelli, Moshe Armon, Efrat Morin.
A simplified MEV formulation to model extremes emerging from multiple nonstationary underlying processes.
Advances in Water Resources, 127, 280-290, 2019
https://doi.org/10.1016/j.advwatres.2019.04.002
Francesco Marra, Marco Borga, Efrat Morin.
A unified framework for extreme sub-daily precipitation frequency analyses based on ordinary events.
Geophys. Res. Lett., 47, 18, e2020GL090209. 2020.
https://doi.org/10.1029/2020GL090209
We have used pythonized version of SMEV code from:
https://github.com/luigicesarini/pysmev
The original code of SMEV written in Matlab is available from:
https://doi.org/10.5281/zenodo.3971557