Obtaining accurate data on reference crop evapotranspiration (ET0) is important for agricultural water management. A novel Gaussian exponential model (GEM) was developed in this study to predict ET0 with limited climatic data. The GEM was further compared with the M5 model tree (M5T), extreme learning machine (ELM), and boosted trees (BT) model under local and regional scenarios. Daily meteorological data during 1997-2016 from four stations in Northeast China were used to develop and validate the model. The results showed that the models considering solar radiation and relative humidity demonstrated considerably higher accuracy than those using other inputs. The GEM demonstrated higher accuracy among the four machine learning models for different stations. The accuracy of GEM under local scenarios was higher than that under regional scenarios with the root mean square error (RMSE) reducing by 0.025-0.046 mm/d, relative root mean square error (RRMSE) reducing by 0.879-2.022%, coefficient of efficiency (Ens) increasing by 0.008-0.026, the coefficients of determination (R2) increasing by 0.008-0.026, and mean absolute error (MAE) reducing by 0.015-0.033 mm/d. The GEM considering solar radiation had the highest accuracy with the global performance indicator (GPI) of 1.876. It can also be seen from the Taylor diagrams that the GEM has the the lowest standard deviation and mean square error and the highest correlation coefficient with the standard values. In general, the GEM considering solar radiation had the lowest error and the highest consistency and could be recommended for ET0 simulation for Northeast China.
Keywords: Gaussian exponential model; Limited climatic data; Local and regional scenarios; Machine learning models; Reference crop evapotranspiration.
© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.