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Bias in the mean scenario of seasonal ETo calculated based on the simple ETo methods is presented in Table 1. As the results indicate, the performance of the simple methods changes seasonally. The Hargreaves–Samani, Makkink, and Tabari methods underestimate the mean scenario and the rest of the methods overestimate it. In winter, the best performance in estimation of the mean scenario for seasonal ETo is produced by the Hargreaves–Samani, Tabari and Jensen–Haise methods, while the Makkink and Tabari methods perform the best in spring. The mean scenario for summer ETo is underestimated by most of the simple methods, among which Turc and Schendel show the smallest biases. For autumn, the Tabari and Makkink methods more closely reproduce the PMF-56-driven scenario. Overall, among all the simple methods, Schendel has the worst performance in estimation of monthly and seasonal ETo changes.

Table 1

Bias (%) in the mean climate change scenario of seasonal ETo calculated based on the simple ETo methods compared to the PMF-56 method

ETo methodWinterSpringSummerAutumn
Blaney–Criddle 87.21 39.85 26.54 51.38 
Hargreaves–Samani −23.95 −37.83 −42.42 −41.72 
Schendel 548.01 63.48 15.40 161.69 
Makkink −44.22 −12.52 −29.79 −17.62 
Turc 126.03 50.09 −12.93 24.14 
Jensen–Haise 28.29 80.97 68.60 66.59 
Tabari −28.04 −23.30 −44.59 −7.21 
ETo methodWinterSpringSummerAutumn
Blaney–Criddle 87.21 39.85 26.54 51.38 
Hargreaves–Samani −23.95 −37.83 −42.42 −41.72 
Schendel 548.01 63.48 15.40 161.69 
Makkink −44.22 −12.52 −29.79 −17.62 
Turc 126.03 50.09 −12.93 24.14 
Jensen–Haise 28.29 80.97 68.60 66.59 
Tabari −28.04 −23.30 −44.59 −7.21 

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