Document Type : Original Article
Authors
1 Graduated with a PhD in Water Resources Engineering, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran
2 Ph. D Student of Water Resources Engineering, Department of Water Science and Engineering, Faculty of Agriculture, University of Birjand, Birjand, Iran
Abstract
Evapotranspiration is one of the most important parameters, knowing it is essential for estimating plant water consumption and designing irrigation systems. The purpose of this research is to compare the performance of gene expression programming methods and empirical relationships in estimating daily reference evapotranspiration over a 20-year period (2001-2020) in three climates: arid (Birjand), Mediterranean (Gorgan), and very humid (Rasht). In order to compare the results of the gene expression algorithm in predicting reference evapotranspiration, 6 scenarios were defined according to the meteorological parameters affecting reference evapotranspiration. Also, in this research, empirical methods (Makkink, Pristly and Taylor, FAO Blaney Criddle, FAO Penman-Monteith, Hargreaves, Hargreaves- Samani, Irmak-Rs, and Irmak- Rn) were used to estimate daily reference evapotranspiration. Finally, the best model was selected based on the evaluation criteria RMSE, MAE, NSE, and R2. The results showed that in Birjand and Gorgan stations, scenario a provided a more favorable prediction due to considering the parameters of minimum temperature, maximum temperature, average air temperature, relative humidity, wind speed, sunshine hours, and reference evapotranspiration. At Rasht station, scenario b provided a more favorable prediction than other scenarios. Also, the Irmak-Rs method and the Hargreaves method, with a high R2 value, relatively good NSE, and lower RMSE and MAE values compared to other methods, can be a suitable alternative to the Penman-Monteith-FAO method on a daily scale in Mediterranean, very humid, and arid climates. Also, the Hargreaves- Samani method had the lowest accuracy (R2=0.5) in estimating reference evapotranspiration at all stations.
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