Preprints
https://doi.org/10.5194/hess-2022-417
https://doi.org/10.5194/hess-2022-417
09 Jan 2023
 | 09 Jan 2023
Status: this discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.

Technical note: Improved handling of potential evapotranspiration in hydrological studies with PyEt

Matevž Vremec, Raoul A. Collenteur, and Steffen Birk

Abstract. Evapotranspiration (ET) is a crucial flux of the hydrological water balance, commonly estimated using (semi-)empirical formulas. The estimated flux may strongly depend on the used formula, adding uncertainty to the outcomes of hydrological models using ET. Climate change may cause additional uncertainty as each formula may respond differently to changes in meteorological input data. To include the effects of model uncertainty and climate change, and facilitate the use of these formulas in a consistent, tested, and reproducible workflow, we present PyEt. PyEt is an open-source Python package for the estimation of daily potential evapotranspiration (PET) using available meteorological data. It allows the application of twenty different PET methods on both time series (Pandas) and gridded datasets (xarray). Most of the implemented methods are benchmarked against literature values and tested with continuous integration to ensure the correctness of the implementation. This article provides an overview of PyEt's capabilities, including the estimation of PET with twenty PET methods for station, and gridded data, a simple procedure for calibrating the empirical coefficients in the alternative PET methods, and estimation of PET under warming and elevated atmospheric CO2 concentration. Further discussion on the advantages of using PyEt estimates as input for hydrological models, sensitivity/uncertainty analyses, and hind/forecasting studies, especially in data-scarce regions, is provided.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-417', Anonymous Referee #1, 27 Feb 2023
    • AC1: 'Reply on RC1', Matevž Vremec, 03 Apr 2023
  • RC2: 'Comment on hess-2022-417', Anonymous Referee #2, 27 Feb 2023
    • AC2: 'Reply on RC2', Matevž Vremec, 03 Apr 2023

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2022-417', Anonymous Referee #1, 27 Feb 2023
    • AC1: 'Reply on RC1', Matevž Vremec, 03 Apr 2023
  • RC2: 'Comment on hess-2022-417', Anonymous Referee #2, 27 Feb 2023
    • AC2: 'Reply on RC2', Matevž Vremec, 03 Apr 2023
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk
Matevž Vremec, Raoul A. Collenteur, and Steffen Birk

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Short summary
This paper introduced PyEt, a Python package for the estimation of daily potential evapotranspiration (PET). The package enables the inclusion of model uncertainty and climate change into the estimation of PET in a consistent, tested, and reproducible environment. With PyEt, users can estimate PET using 20 different methods for both 1D and 3D data, allowing a more sophisticated and comprehensive consideration of PET in hydrological studies, particularly those related to climate change.