Code and examples of how to estimate transpiration from eddy covariance data.
This is a companion repository to Nelson et al (2020) which compares three evapotranspiration partitioning methods to estimate transpiration from eddy covariance data. Each method is fully described in the following reference manuscripts:
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Pérez-Priego: Perez-Priego et al (2018). Partitioning Eddy Covariance Water Flux Components Using Physiological and Micrometeorological Approaches. Journal of Geophysical Research: Biogeosciences. https://doi.org/10.1029/2018JG004637
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Nelson: Nelson et al (2018). Coupling Water and Carbon Fluxes to Constrain Estimates of Transpiration: The TEA Algorithm. Journal of Geophysical Research: Biogeosciences. https://doi.org/10.1029/2018JG004727
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uWUE: Zhou et al (2016). Partitioning evapotranspiration based on the concept of underlying water use efficiency: ET PARTITIONING. Water Resources Research, 52(2), 1160–1175. https://doi.org/10.1002/2015WR017766
The code for each method can be found:
- the Pérez-Priego method is implemented in R here
- the TEA metod (Nelson 2018) is implemented as a python package here
- the uWUE method is implemented in python in this repository as zhou.py
Tutorials for the TEA, uWUE, and Pérez-Priego methods are contianed in this repository as jupyter notebooks. Each can be run within a browser without any installation via Binder:
- TEA
- Zhou
- Pérez-Priego
All code required to run the partitionin methods, as well as the tutorials, can be installed using the aAnaconda environment defined in environment.yml. Instructions for installing Anaconda can be found here.
To install the Anaconda environment, which includes software required to run the three pratitioning methods, first download the repository. If using Anaconda Navigator, follow the Importing an environment instructions. If using the command line, follow Creating an environment from an environment.yml file.
Once installed, activate the environment. The tutorials can then be run locally, for example:
jupyter notebook TEA_tutorial.ipynb
In order to run the Pérez-Priego method, the R packages found in install.R much be installed before the tutroial will run. This can be installed from the command line with:
Rscript install.R
Data from FLUXNET associated with the accompanying manuscript can be found in the Zenodo repository.
If you are unfamiliar with netcdf formats, the data can be exported to a csv using the ExportTutorial.ipynb
If you are unfamiliar with Jupyter notebooks (.ipynb), get started here.