Evaluation of Empirical Equations and Machine Learning Models for Daily Reference Evapotranspiration Prediction Using Public Weather Forecasts

Although the studies on model prediction of daily ETo based on public weather forecasts have been widely used, these studies lack the comparative evaluation of different types of models and do not evaluate the seasonal variation in model prediction of daily ETo performance; this may result in the selected model not being the best model. In this study, to select the best daily ETo forecast model for the irrigation season at three stations (Yinchuan, Tongxin, and Guyuan) in different climatic regions in Ningxia, China, the daily ETos of the three sites calculated using FAO Penman-Monteith equations were used as the reference values. Three empirical equations (temperature Penman-Monteith (PMT) equation, Penman-Monteith forecast (PMF) equation, and Hargreaves-Samani (HS) equation) were calibrated and validated, and four machine learning models (multilayer perceptron (MLP), extreme gradient boosting (XGBoost), light gradient boosting machine (LightGBM), and gradient boosting with categorical features support (CatBoost)) were trained and validated against daily observed meteorological data (1995-2015 and 2016-2019). Based on public weather forecasts and daily observed meteorological data (2020-2021), the three empirical equations (PMT, PMF, and HS) and four machine learning models (MLP, XGBoost, LightGBM, and CatBoost) were compared in terms of their daily ETo prediction performance. The results showed that the daily ETo performance of the seven models in the irrigation season with a lead time of 1-7 days predicted by the three research sites decreased in the order of spring, autumn, and summer. PMT was the best model for the irrigation seasons (spring, summer, and autumn) at station YC; PMT and CatBoost with C3 (Tmax, Tmin, and Wspd) as the inputs were the best models for the spring, autumn irrigation seasons, and summer irrigation seasons at station TX, respectively. PMF, CatBoost with C4 (Tmax, Tmin) as input, and PMT are the best models for the spring irrigation season, summer irrigation season, and autumn irrigation season at the GY station, respectively. In addition, wind speed (converted from the wind level of the public weather forecast) and sunshine hours (converted from the weather type of the public weather forecast) from the public weather forecast were the main sources of error in predicting the daily ETo by the models at stations YC and TX(GY), respectively. Empirical equations and machine learning models were used for the prediction of daily ETo in different climatic zones and evaluated according to the irrigation season to obtain the best ETo prediction model for the irrigation season at the study stations. This provides a new idea and theoretical basis for realizing water-saving irrigation during crop fertility in other arid and water-scarce climatic zones in China.