PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration

PyEt v1.3.1: a Python package for the estimation of potential evapotranspiration

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 formula used, adding uncertainty to the outcomes of environmental studies using ET. Climate change may cause additional uncer...

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Bibliographic Details
Published in:Geoscientific Model Development Vol. 17; no. 18; pp. 7083 - 7103
Main Authors: Vremec, MatevÅ, Collenteur, Raoul A, Birk, Steffen
Format: Journal Article
Language:English
Published: Katlenburg-Lindau Copernicus GmbH 24-09-2024
Copernicus Publications
Subjects:
Analysis
Carbon dioxide
Carbon dioxide concentration
Climate change
Climate models
Climatic changes
Datasets
Digital Object Identifier
Documentation
Earth science
Environmental studies
Evapotranspiration
Evapotranspiration estimates
Hindcasting
Humidity
Hydrologic models
Hydrology
Meteorological data
Potential evapotranspiration
Product design
Radiation
Reproducibility
Software
Software development
Time series
Uncertainty
Uncertainty analysis
Water balance
Water balance (Hydrology)
Weather
Workflow
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Description
Summary: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 formula used, adding uncertainty to the outcomes of environmental studies using ET. Climate change may cause additional uncertainty, as the ET estimated by 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 20 different PET methods on both time series and gridded datasets. The majority 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 20 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 and uncertainty analyses, and hindcasting and forecasting studies (especially in data-scarce regions) is provided.
ISSN:1991-9603
1991-959X
1991-962X
1991-9603
1991-962X
DOI:10.5194/gmd-17-7083-2024