What_variables_matter_in_CR_for_evaporation

Description

The Complementary Relationship (CR) of evaporation recognizes that low evaporation rates lead to low humidity in the lower atmosphere, and high rates lead to high humidity. Conversely, moist air implies high evaporation rate and vice versa. Discussion of the CR usually focuses on the functional form of relationships among actual evaporation E, apparent potential evaporation Ep, and wet surface evaporation E0. This project removes the functional form of the relationship by training an artificial neural network to predict evaporation rates (i.e., minimize mean square error) using data from 171 FLUXNET sites on a monthly basis. The FLUXNET data are used to calculate potential CR variables like Ep, E0, the Priestley-Taylor parameter (alpha), and Epm, the maximum possible value of Ep. Variable combinations that result in low mean square error are considered better. Results show that Ep and E0 alone can give adequate performance. However, including Epm results in significant improvement, as does parameterizing alpha as a function of temperature. The conclusion is that Ep, E0, and Epm are all essential parts of the CR, and CR versions that consider alpha a function of temperature are preferred. Note that all the variable lists require exactly the same raw data such as temperature, wind speed, air pressure, net radiation, ground heat flux, and measured E (used as the reference values). While the list of variables is clarified by this study, it does not imply any particular functional form for the CR.

Steps to reproduce

The data files are in csv format and contain far more columns than are used in this analysis. There is also a file ("Fluxnet_measurement_canopy_heights.csv") containing IGBP classes, measurement heights, and canopy heights for the sites. These site data came directly from the suplement provided by Wang et al. (2020, Determinants of the asymmetric parameter in the generalized complementary principle, Water Resour. Res., doi: 10.1029/2019WR026570). A jupyter notebook (Python) is included (the ipynb file). To run the notebook, place all the data files in a folder and change dir_name in the first cell of the notebook so that it points to your data. The software works well using Google Colab to run the code (https://colab.research.google.com/). We thank FLUXNET and the researchers who collected the data around the world.

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