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Evapotranspiration\n\n[![codacy-coverage-reporter](https://github.com/pyet-org/pyet/actions/workflows/ci.yml/badge.svg)](https://github.com/pyet-org/pyet/actions/workflows/ci.yml)\n\u003ca href=\"https://pypi.org/project/pyet/\"\u003e\u003cimg src=https://img.shields.io/pypi/v/pyet.svg\u003e\u003ca\u003e\n\u003ca href=\"https://mit-license.org/\"\u003e\u003cimg src=https://img.shields.io/pypi/l/pyet.svg\u003e\u003ca\u003e\n[![Codacy Badge](https://app.codacy.com/project/badge/Grade/e49f23e356f441688422ec32cfcf6aaa)](https://www.codacy.com/gh/phydrus/pyet/dashboard?utm_source=github.com\u0026amp;utm_medium=referral\u0026amp;utm_content=phydrus/pyet\u0026amp;utm_campaign=Badge_Grade)\n[![Codacy Badge](https://app.codacy.com/project/badge/Coverage/e49f23e356f441688422ec32cfcf6aaa)](https://www.codacy.com/gh/phydrus/pyet/dashboard?utm_source=github.com\u0026utm_medium=referral\u0026utm_content=phydrus/pyet\u0026utm_campaign=Badge_Coverage)\n\u003ca href=\"https://pyet.readthedocs.io/en/latest/?badge=latest\"\u003e\u003cimg src=\"https://readthedocs.org/projects/pyet/badge/?version=latest\"\u003e\u003ca\u003e\n\u003ca href=\"https://doi.org/10.5281/zenodo.5896800\"\u003e\u003cimg src=https://zenodo.org/badge/DOI/10.5281/zenodo.5896800.svg\u003e\u003ca\u003e\n\npyet is an open source python package for calculating reference and potential Evapotranspiration (PET) for 1D (pandas.Series)\nand 3D (xarray.DataArrray) data. Currently, eighteen methods for calculating daily PET are implemented:\n\n| Method name       | pyet function     | T      | RH         | R      | u2     | Lat.   | El.    | Benchmarked?      |\n|:------------------|:------------------|:-------|:-----------|:-------|:-------|:-------|:-------|:------------------|\n| Penman            | penman            | \u0026check; $^a$ | \u0026check; $^{b,c}$ | \u0026check; $^d$ | \u0026check;      | \u0026check; $^d$ | \u0026check; $^e$ | \u0026check;           |\n| Penman-Monteith   | pm                | \u0026check; $^a$ | \u0026check; $^{b,c}$ | \u0026check; $^d$ | \u0026check;      | \u0026check; $^d$ | \u0026check; $^e$ | \u0026check;           |\n| ASCE-PM           | pm_asce           | \u0026check; $^a$ | \u0026check; $^{b,c}$ | \u0026check; $^d$ | \u0026check;      | \u0026check; $^d$ | \u0026check; $^e$ | \u0026check;           |\n| FAO-56            | pm_fao56          | \u0026check; $^a$ | \u0026check; $^{b,c}$ | \u0026check; $^d$ | \u0026check;      | \u0026check; $^d$ | \u0026check; $^e$ | \u0026check;           |\n| Priestley-Taylor  | priestley_taylor  | \u0026check;      | \u0026check; $^h$     | \u0026check; $^h$ | -      | \u0026check; $^h$ | \u0026check; $^e$ | \u0026check;           |\n| Kimberly-Penman   | kimberly_penman   | \u0026check; $^a$ | \u0026check; $^{b,c}$ | \u0026check; $^d$ | \u0026check;      | \u0026check; $^d$ | \u0026check; $^e$ | -                 |\n| Thom-Oliver       | thom_oliver       | \u0026check; $^a$ | \u0026check; $^{b,c}$ | \u0026check; $^d$ | \u0026check;      | \u0026check; $^d$ | \u0026check; $^e$ | -                 |\n| Blaney-Criddle    | blaney_criddle    | \u0026check;      | - $^i$     | - $^i$ | - $^i$ | \u0026check;      | -      | \u0026check;           |\n| Hamon             | hamon             | \u0026check;      | -          | -      | -      | \u0026check;      | -      | \u0026check;           |\n| Romanenko         | romanenko         | \u0026check;      | \u0026check;          | -      | -      | -      | -      | \u0026check;           |\n| Linacre           | linacre           | \u0026check; $^j$ | -          | -      | -      | -      | \u0026check;      | \u0026check;           |\n| Haude             | haude             | \u0026check;      | \u0026check; $^k$     | -      | -      | -      | -      | \u0026check;           |\n| Turc              | turc              | \u0026check;      | \u0026check;          | \u0026check;      | -      | -      | -      | \u0026check;           |\n| Jensen-Haise      | jensen_haise      | \u0026check;      | -          | \u0026check; $^l$ | -      | \u0026check; $^l$ | -      | \u0026check;           |\n| McGuinness-Bordne | mcguinness_bordne | \u0026check;      | -          | -      | -      | \u0026check;      | -      | \u0026check;           |\n| Hargreaves        | hargreaves        | \u0026check; $^m$ | -          | -      | -      | \u0026check;      | -      | \u0026check;           |\n| FAO-24 radiation  | fao_24            | \u0026check;      | \u0026check;          | \u0026check;      | \u0026check;      | -      | \u0026check; $^e$ | -                 |\n| Abtew             | abtew             | \u0026check;      | -          | \u0026check;      | -      | -      | -      | \u0026check;         |\n| Makkink           | makkink           | \u0026check;      | -          | \u0026check;      | -      | -      | \u0026check; $^e$ | \u0026check;           |\n| Oudin             | oudin             | \u0026check;      | -          | -      | -      | \u0026check;      | -      | -                 |\n\n$^a$ $T_{max}$ and $T_{min}$ can also be provided. $^b$ $RH_{max}$ and $RH_{min}$ can also be provided. $^c$ If actual vapor pressure is provided, RH is not needed.  $^d$ Input for radiation can be (1) Net radiation, (2) solar radiation or (3) sunshine hours. If (1), then latitude is not needed. If (1, 3) latitude and elevation is needed. $^e$ One must provide either the atmospheric pressure or elevation. $^f$ The PM method can be used to estimate potential crop evapotranspiration, if leaf area index or crop height data is available. $^g$ The effect of $CO_2$ on stomatal resistance can be included using the formulation of Yang et al. 2019.  $^h$ If net radiation is provided, RH and Lat are not needed. $^i$ If method==2, $u_2$, $RH_{min}$ and sunshine hours are required. $^j$ Additional input of $T_{max}$ and $T_{min}$, or $T_{dew}$. $^k$ Input can be $RH$ or actual vapor pressure. $^l$ If method==1, latitude is needed instead of $R_s$. $^m$ $T_{max}$ and $T_{min}$ also needed.\n\n## Examples and Documentation\n\nExamples of using *pyet* can be found in the example folder:\n\n*   [Example 1](\u003chttps://pyet.readthedocs.io/en/dev/examples/01_example_zamg.html#\u003e): Estimating PET using pandas.Series\n\n*   [Example 2](\u003chttps://pyet.readthedocs.io/en/dev/examples/02_example_zamg_netcdf.html\u003e): Estimating PET using xarray.DataArray\n\n*   [Example 3](\u003chttps://pyet.readthedocs.io/en/dev/examples/03_example_knmi.html\u003e): Benchmarking Makkink\n  against [KNMI data](https://www.knmi.nl/over-het-knmi/about)\n\n*   [Example 4](\u003chttps://pyet.readthedocs.io/en/dev/examples/04_example_coagmet.html\u003e): Benchmarking FAO56\n  against [CoAgMET data](https://coagmet.colostate.edu/)\n\n*   [Example 5](\u003chttps://pyet.readthedocs.io/en/dev/examples/05_example_calibration.html\u003e): Calibrating the Romanenko and Abtew method against the PM-FAO56\n\n*   [Example 6](\u003chttps://pyet.readthedocs.io/en/dev/examples/06_worked_examples_McMahon_etal_2013.html\u003e): Worked examples for estimating meteorological\n  variables and potential evapotranspiration after McMahon et al., 2013\n\n*   [Example 7](\u003chttps://pyet.readthedocs.io/en/dev/examples/07_example_climate_change.html\u003e): Example for estimating potential evapotranspiration under\n  warming and elevated $CO_2$ concentrations following Yang et al., (2019)\n\n*   [Example 8](\u003chttps://pyet.readthedocs.io/en/dev/examples/08_crop_coefficient.html\u003e): Determining the crop coefficient function with Python\n\n*   [Example 9](\u003chttps://pyet.readthedocs.io/en/dev/examples/09_CMIP6_data.html\u003e): Estimating PET using CMIP data\n\n*   [Example 10](\u003chttps://pyet.readthedocs.io/en/dev/examples/10_example_paper.html\u003e): Notebook supporting PyEt manuscript\n\nDocumentation is hosted on [ReadTheDocs](https://pyet.readthedocs.io).\n\nAfter defining the input data, evapotranspiration is estimated using only one\nline of python code:\n\n`\u003e\u003e\u003e pyet.pm_fao56(tmean, wind, rn=rn, tmax=tmax, tmin=tmin, rh=rh, elevation=elevation)`\n\n## Benchmarking\n\nMost of the methods implemented in *pyet* are benchmarked against literature values from the [FAO Irrigation and\ndrainage paper 56](https://www.fao.org/3/x0490e/x0490e00.htm), [McMahon et al., 2013 (supplementary)](https://hess.copernicus.org/articles/17/4865/2013/) and [Schrödter, 1985](https://link.springer.com/book/10.1007/978-3-642-70434-5). In addition, two comparative analysis between daily PE estimated with *pyet* and other organizations is\nmade:\n\n*   `pyet.pm_fao56` against daily PET estimated with ASCE Penman-Monteith from [CoAgMET](https://coagmet.colostate.edu/) (\n  Colorado State University),\n\n*   `pyet.makkink` against daily PET estimated with Makkink from The Royal Netherlands Meteorological\n  Institute ([KNMI](https://www.knmi.nl/over-het-knmi/about)).\n\n## Data dimensions\n\nAs of version v1.2., *pyet* is compatible with both Pandas.Series and xarray.DataArray, which means you can now estimate\npotential evapotranspiration for both point and gridded data.\n\n## Bug reports and Questions\n\npyet is in active development, and bug reports are welcome as [GitHub\nIssues](https://github.com/phydrus/pyet/issues).\nGeneral questions or discussions are possible through\n[GitHub Discussions](https://github.com/phydrus/pyet/discussions).\n\n## Installation\n\nThe *pyet* package is available from the Pypi package index and can be installed\nas follows::\n\n`\u003e\u003e\u003e pip install pyet`\n\nTo install in developer mode, use the following syntax:\n\n`\u003e\u003e\u003e pip install -e .`\n\n## Citing\n\nIf you use *pyet* in one of your studies, please cite the *pyet* EGU abstract:\n\n* Vremec, M., Collenteur, R. 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