Research PaperEvaluation of FAO56-PM, empirical, semi-empirical and gene expression programming approaches for estimating daily reference evapotranspiration in hyper-arid regions of Iran

- Modeling daily ETo using different models are reported here.
- Models were fed using original and estimated meteorological data.
- Gene expression programming was compared with the empirical models.
- Gene expression programming was superior in all studied cases.

Accurate estimation of the reference evapotranspiration (ETo) is needed in water resources planning and management, irrigation scheduling and efficient agricultural water management. The FAO56-PM combination model is usually applied as a benchmark model for calculating ETo and calibrating other ETo models. However, the need for large amount of meteorological variables is a major drawback of this model, especially in case of data scarcity. Therefore, application of ETo models relying on fewer meteorological parameters, as well as calculating ETo using estimated meteorological variables is recommended in literature. The present paper aims at assessing the performances of different ETo models using the recorded and estimated meteorological parameters and comparing the results with the corresponding gene expression programming (GEP) models (based on the same input parameters of the employed ETo models) in hyper-arid regions. Daily meteorological parameters from 5 hyper-arid locations of Iran (covering a period of 12 years) were used. The commonly used Hargreaves (HG), Priestley-Taylor (PT), Turc (Tr) and Kimberly-Penman (KP, for alfalfa reference crop) were established and calibrated using both the recorded and estimated solar radiation, relative humidity, and wind speed data. The obtained results revealed that the GEP models outperform the corresponding empirical and semi-empirical models in all three studied categorizes (temperature/humidity-, radiation-, and combination-based approaches). The results also showed that the calibrated PT (original) and Tr (with estimated relative humidity) models gave the most accurate results among the related groups.