Determining the Best Input Combination and Size of Data Length for Daily Evapotranspiration Modelling

Evapotranspiration, is a major part of the hydrologic cycle and plays an important role in water resource development and management, especially in the arid and semi-arid climatic regions such as Iran where water resources are limited. Evapotranspiration can be measured directly by lysimeter, or estimated by the empirical formulas. Using lysimeter is expensive and time consuming; also empirical formulas are unsuitable for all variations of time and place. It is difficult to obtain an accurate formula for ETO estimation that is suitable to encompass all environments, because evapotranspiration is an incidental, nonlinear, complex and unsteady process. Soft computing models are able to handle noisy data from a dynamic and nonlinear system such as evapotranspiration. But, they do not have the ability of pre-processing before model development. In this study, the Gamma Test (GT) technique is applied to find the best input combination and number of sufficient data points for evapotranspiration modelling under arid conditions. It has been found that the minimum required variables to construct a good nonlinear model under arid conditions are the minimum and maximum air temperature and wind speed data.

Determining the Best Input Combination and Size of Data Length for Daily Evapotranspiration Modelling Keywords:

ANFIS model , Empirical formula , Evapotranspiration