Evaluation of Five Equations for Short-Term Reference Evapotranspiration Forecasting Using Public Temperature Forecasts for North China Plain Academic Article uri icon


  • Accurate short-term forecasts of daily reference evapotranspiration (ET0) are essential for real-time irrigation scheduling. Many models rely on current and historical temperature data to estimate daily ET0. However, easily accessible temperature forecasts are relatively less reported in short-term ET0 forecasting. Furthermore, the accuracy of ET0 forecasting from different models varies locally and also across regions. We used five temperature-dependent models to forecast daily ET0 for a 7-day horizon in the North China Plain (NCP): the McCloud (MC), Hargreaves-Samani (HS), Blaney-Criddle (BC), Thornthwaite (TH), and reduced-set PenmanMonteith (RPM) models. Daily meteorological data collected between 1 January 2000 and 31 December 2014 at 17 weather stations in NCP to calibrate and validate the five ET0 models against the ASCE PenmanMonteith (ASCE-PM). Forecast temperatures for up to 7 d ahead for 1 January 201519 June 2021 were input to the five calibrated models to forecast ET0. The performance of the five models improved for forecasts at all stations after calibration. The calibrated RPM is the preferred choice for forecasting ET0 in NCP. In descending order of preference, the remaining models were ranked as HS, TH, BC, and MC. Sensitivity analysis showed that a change in maximum temperature influenced the accuracy of ET0 forecasting by the five models, especially RPM, HS, and TH, more than other variables. Meanwhile, the calibrated RPM and HS equations were better than the other models, and thus, these two equations were recommended for short-term ET0 forecasting in NCP.

published proceedings


author list (cited authors)

  • Zhang, L., Zhao, X., Ge, J., Zhang, J., Traore, S., Fipps, G., & Luo, Y.

complete list of authors

  • Zhang, Lei||Zhao, Xin||Ge, Jiankun||Zhang, Jiaqi||Traore, Seydou||Fipps, Guy||Luo, Yufeng

publication date

  • January 1, 2022 11:11 AM