A Method for Calculating Reference Evapotranspiration on Daily Time Scales

Abstract

Measures of reference evapotranspiration are essential for applications of agricultural management and water resources engineering. Using numerous esoteric variables, one can calculate daily reference evapotranspiration using the Modified Penman-Monteith methods. In 1985, Hargreaves developed a simplified method for estimating reference evapotranspiration. Similarly, Droogers and Allen improved upon Hargreaves’ method in 2002. Both methods provide excellent estimates of average daily rates for a given month, based on monthly climatology. The Hargraeves method also estimates daily rates based on daily data, though the Modified Hargreaves approach developed by Droogers and Allen is largely accepted as a stronger metric. Here efforts are made to improve the functionality of Droogers and Allen’s approach and to adapt it to provide daily estimates of reference evapotranspiration based on daily weather. The Hargreaves and Modified Hargeaves are used to calculate daily reference evapotranspiration based on daily data. The coefficients in these equations are then optimized to reduce the root mean squared difference between each estimate and the baseline value calculated by the Modified Penman-Monteith approach. The adapted method for daily reference evapotranspiration proves promising; estimating rates near a root mean squared difference of 1.07 mm/day. These results are validated with data from 1976-1980; here the root mean squared difference is 1.06 mm/day. Results are evaluated spatially and temporally. Weaknesses are seen in the estimates around clearly-defined summers. Further weaknesses are seen in pole-ward regions. Still, at the 1% significance level, the daily optimization of the Modified Hargreaves equation is found to be the best replica of the Modified Penman-Monteith method, globally. Finally, specific caveats and further avenues of research are noted. Overall, the daily Modified-Hargreaves method is advocated for general use in global studies where daily data and variation is of the utmost concern.