Multiple Linear Regression Models with Limited Data for the Prediction of Reference Evapotranspiration of the Peloponnese, Greece

The aim of this study was to investigate the utility multiple linear regression (MLR) for estimation reference evapotranspiration (ETo) Peloponnese, Greece, two representative months winter and summer during 2016–2019. Another objective test number inputs needed satisfactorily accurate estimates via MLR. Datasets from sixty-two meteorological stations were exploited. available independent variables sunshine hours (N), mean temperature (Tmean), solar radiation (Rs), net (Rn), wind speed (u2), vapour pressure deficit (es ? ea), altitude (Z). Sixteen MLR models tested compared corresponding ETo computed by FAO-56 Penman–Monteith (FAO PM) in a previous study, statistical indices error agreement. MLR5 model with five input outperformed other (RMSE = 0.28 mm d?1, adj. R2 98.1%). Half (two six inputs) exhibited very satisfactory predictions. Models one (e.g., N, Rn) also promising. However, u2 as sole variable presented worst performance, probably because its relationship cannot be linearly described. results indicate that has potential produce good predictive based on literature standards.