A novel integrated method based on a machine learning model for estimating evapotranspiration in dryland

Evapotranspiration (ET) plays a vital role in the water cycle and energy cycle and serves as an important linkage between ecological and hydrological processes. Accurate estimation of ET based on data-driven methods is of great theoretical and practical significance for exploring soil evaporation, plant transpiration and the regional hydrological balance. Most existing estimation approaches were proposed based on multiple meteorological variables. This study proposed a novel hybrid estimation approach to estimate the monthly ET using only historical ET time series in the southeastern margins of the Tengger Desert, China. The approach consisted of three sections including data preprocessing, parameter optimization and estimation. The model evaluation demonstrated that the hybrid model based on the variational mode decomposition (VMD) method, grey wolf optimizer (GWO) algorithm and support vector machine (SVM) model achieved superior computational performance compared to the performance of other methods. The Nash–Sutcliffe coefficient of efficiency (NSCE) increased from 0.8588 to 0.8754 and the mean absolute percentage error (MAPE) decreased from 28.42% to 23.22% in the testing stage. Thus, we suggest that the hybrid VMD-GWO-SVM model will be the best choice for estimating ET in the absence of regional meteorological monitoring.