Forecast of Daily Reference Crop Evapotranspiration Using Universal Deep Learning Models in China
Description
Evapotranspiration is a key process in ecosystems, and accurate prediction of short-term daily reference crop evapotranspiration (ETo) is crucial for real-time irrigation decision-making and regional water resource allocation. Due to the complex nonlinear relationship between meteorological factors and ETo, deep learning models are often trained using meteorological observation data from several to dozens of stations to estimate ETo at the station scale. These ETo estimation models are then driven by weather forecast data to achieve ETo prediction. In this study, we collected a massive amount of public weather forecast data across the country over a four-year period using an automated program. Leveraging this data, along with location and seasonal information, we trained a high-precision ETo forecasting model applicable to the entire country.we forecasted national-scale ETo for a 15-day lead time using five deep learning (DL) models: LSTM, Bi-LSTM, GRU, CNN-BiLSTM, and CNN-BiLSTM-Attention. Five training sets were employed containing diverse input information, including historical climate data, weather forecast data, location, and seasonal information. Public weather forecast data were collected from 2,381 stations across China, spanning from January 2020 to April 2024, which included daily maximum (Tmax) and minimum temperatures (Tmin). The results revealed that the differences in ETo forecasting performance between the DL models were significantly smaller than the variations between training sets. By integrating location and seasonal information into the training set, we found a notable decrease in the RMSE of ETo forecasts for lead times of 1, 4, 7, and 15 days, shifting from 0.70, 0.84, 0.97, and 1.27 mm/day to 0.63, 0.79, 0.92, and 1.22 mm/day, respectively. Furthermore, when we directly use massive weather forecast data to train the model, the RMSE of ETo forecasts for the same lead times demonstrated a significant reduction, with the decrease percentages of 30.4%, 34.7%, 38.5%, and 47.2%, respectively. We have provided a portion of weather forecast data and historical meteorological data for your reference and use. These data cover multiple key meteorological indicators such as temperature, humidity, wind speed, and precipitation, with high accuracy and reliability. Additionally, we offer a pre-trained model based on deep learning algorithms, specifically designed for predicting short-term daily reference crop evapotranspiration (ETo). You can utilize this model free of charge to enhance your real-time irrigation decision-making and regional water resource allocation efforts. By providing these data and the model, we aim to facilitate and assist your related research and practices.