Short-Term Forecasting of Daily Reference Crop Evapotranspiration Based on Calibrated Hargreaves–Samani Equation at Regional Scale

This study focuses on enhancing real-time irrigation decisions and stream flow forecasts using short-term daily forecasts of reference evapotranspiration (ETo). While conventional approaches rely on historical observations for daily forecasting, developed countries have transitioned to issuing ETo forecasts derived from Numerical Weather Prediction or General Circulation Models (GCM) outputs. In this study, a similar approach was applied to predict short-term ETo forecasts for Pakistan using GCM output, combining data from the Pakistan Meteorological Department in-situ observation data and GCM forecast data from the Copernicus data source. ETo is calculated using the Hargreaves Samani (HS) equation, calibrated, and parameterized for the newly defined agro-climatic region by K-means clustering of ETo and soil moisture. Results indicate that the modified HS performs well in all climate regions except arid and humid regions, where errors are attributed to temperature forecast issues at high altitudes and the HS model neglecting wind speed and relative humidity effects. The integration of temperature data in the modified HS generates temperature-ETo correlation coefficients exceeding 0.92 in all agro-climatic regions. The method demonstrated accurate daily ETo forecasts for real-time irrigation predictions, particularly valuable in regions with sparse meteorological networks. The HS equation was calibrated for different agro-climatic regions using methods like fuzzy logic, pressure ratio, and curve fitting. The modified HS equation, integrated with numerical weather forecasts and a geographic information system, provides weekly forecasts and 10-day lead time ETo forecasts for district level and defined agro-climatic regions in Pakistan, with acceptable error ranges. Furthermore, the study determines a robust Pearson correlation (0.79) and a root-mean-square error of 0.54 with a 95% significant level, contributing significantly to predictive capabilities in the field of evapotranspiration forecasting.