What_variables_matter_in_CR_for_evaporation

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.