Scale dependent prediction of reference evapotranspiration based on Multi-Variate Empirical mode decomposition

This study proposes a novel method for estimation of reference evapotranspiration (ETo) by accounting the time scale of variability using the Multivariate Empirical Mode Decomposition (MEMD). First the ETo and the four predictor variables such as solar radiation, air temperature, relative humidity and wind velocity are decomposed into different intrinsic mode functions (IMFs) and a residue using MEMD. To model ETo, first the modes are modeled separately using the Stepwise Linear Regression (SLR) after identifying the significant predictors at different time scales based on the p-value statistics. Subsequently, the predicted modes are recombined to obtain ETo at the observation scale. The method is demonstrated by predicting the monthly ETo from Stratford station in United States. The results of the study clearly exhibited the superior performance of the proposed MEMD-SLR model when compared with that by M5 model tree, SLR and the EMD-SLR hybrid model.

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