Prediction of daily reference evapotranspiration by a multiple regression method based on weather forecast data

Prediction of daily reference evapotranspiration (ET0) is the basis of real-time irrigation scheduling. A multiple regression method for ET0 prediction based on its seasonal variation pattern and public weather forecast data was presented for application in East China. The forecasted maximum temperature (T-max), minimum temperature (T-min) and weather condition index (WCI) were adopted to calculate the correction coefficient by multilinear regression under five time-division regimes (10 days, monthly, seasonal, semi-annual and annual). The multiple regression method was tested for its feasibility for ET0 prediction using forecasted weather data as the input, and the monthly regime was selected as the most suitable. Average absolute error (AAE) and root mean square error (RMSE) were 0.395 and 0.522mm d(-1), respectively. ET0 prediction errors increased linearly with the increase in temperature prediction error. A temperature error within 3K is likely to result in acceptable ET0 predictions, with AAE and average absolute relative error (AARE) <0.142mm d(-1) and 5.8%, respectively. However, one rank error in WCI results in a much larger error in ET0 prediction due to the high sensitivity of the correction coefficient to WCI and the large relative error in WCI caused by one rank deviation. Improving the accuracy of weather forecasts, especially for WCI prediction, is helpful in obtaining better estimations of ET0 based on public weather data.