Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM)

Abstract

As the standard method to compute reference evapotranspiration (ET0), Penman-Monteith (PM) method requires eight meteorological input variables, which makes it difficult to apply in data scarce regions. To overcome this problem, a hybrid bi-directional long short-term memory (Bi-LSTM) model was developed to forecast short-term (1–7-day lead time) daily ET0. The model was trained, validated and tested using three meteorological variables for the period of 2006–2018 at selected three meteorological stations located in the semi-arid region of central Ningxia, China. The performance of the hybrid Bi-LSTM model to forecast short-term daily ET0 was evaluated against daily ET0 calculated by the Penman-Monteith method using the statistical metrics namely, mean absolute error (MAE), root mean square error (RMSE), Pearson's correlation coefficient (R) and Nash-Sutcliffe efficiency (NSE). The results showed that the hybrid Bi-LSTM model with a combination of three meteorological inputs (maximum temperature, minimum temperature and sunshine duration) provides the best forecast performance for short-term daily ET0 at the selected meteorological stations. When averaged across stations, the statistical performance at different forecast lead time were as follows; 1-day lead time: RMSE = 0.159 mm day−1, MAE = 0.039 mm day−1, R = 0.992, NSE = 0.988; 4-day lead time: RMSE = 0.247 mm day−1, MAE = 0.075 mm day−1, R = 0.972, NSE = 0.985 and 7-day lead time: RMSE = 0.323 mm day−1, MAE = 0.089 mm day−1, R = 0.943, NSE = 0.982. Moreover, the hybrid Bi-LSTM model consistently improved the forecast performance of short-term daily ET0 compared to the adjusted Hargreaves-Samani (HS) method and the general Bi-LSTM model. The hybrid Bi-LSTM model developed in this study is currently integrated into the modern intelligent irrigation system of 30 ha of Lycium barbarum plantation in central Ningxia in China, a region with limited meteorological data. It is recommended however that the hybrid Bi-LSTM should be evaluated across a wide range of climatic conditions in different regions of the world.