Short-term forecasting of daily reference evapotranspiration using the Penman-Monteith model and public weather forecasts

Abstract

Short-term daily reference evapotranspiration (ETo) forecasts are required to facilitate real-time irrigation decision making. We forecasted daily 7-day-ahead ETo using the Penman–Monteith (PM) model and public weather forecasts. Public weather forecast data, including daily maximum and minimum temperatures, weather types and wind scales, for six stations located in a wide range of climate zones of China were collected. Weather types and wind scales were converted to sunshine duration and wind speed to forecast ETo. Meanwhile, daily meteorological data for the same period and locations were collected to calculate ETo, which served as reference standard for evaluating forecasting performance. The results showed that the forecasting performance for the minimum temperature was the best, followed by maximum temperature, sunshine duration and wind speed. Also, it was found that using public weather forecasts and the PM model improved the forecasting performance of daily ETo compared to those obtained when using the HS model with temperature forecasts as the only input data, and this improvement was because the weather type and wind scale forecasts also have positive influence on ETo forecasting. Further, the greatest impact on ETo forecasting error was found to be caused by the errors in sunshine duration and wind speed, followed by maximum and minimum temperature forecasts.