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. we first evaluated the accuracy of daily temperature forecasts for the next 15 days based on four years of weather forecast data collected from 2,381 stations. Subsequently, based on different input variables from this stations, we developed three ETo estimation cases and five ETo forecast cases. For each case, five time series DL models derived from improvements to RNNs were employed (i.e., LSTM, BiLSTM, GRU, CNN-BiLSTM, CNN-BiLSTM-Attention). The results can be concluded as below. The results revealed that the differences in ETo estimating performance between the DL models were significantly smaller than the variations between different training strategies.With the average Root Mean Square Error (RMSE) of the five DL models decreasing from 0.55 mm d-1 to 0.48 mm d-1. Furthermore, when we directly use a larger volume of weather forecast data to train the models, the forecasting accuracy of ETo has been significantly improved, and among the five DL models, the GRU performed the best. Specifically, the RMSE values for the GRU model's future ETo forecasts on the 1st, 4th, 7th, and 15th days have decreased from 0.70, 0.87, 1.00, and 1.33 mm d-1 to 0.51, 0.56, 0.61, and 0.67 mm d-1, 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). In the "research" folder, there are three main sections: the Estimating Model, the Forecasting Model, and the Data. The Data section includes a portion of the data we use for model training, comprising a complete set of meteorological data necessary for calculating reference evapotranspiration. The Estimating Model section contains models designed for estimating reference evapotranspiration, while the Forecasting Model section includes models used for forecasting reference evapotranspiration. 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.