Assessing Reference Evapotranspiration Estimation Considering Irrigation Scheduling Intervals

Abstract

Accurate estimation of reference evapotranspiration (ETo) is essential for irrigation planning in semi-arid Mediterranean regions. This study evaluated temperature-based and neural network models for estimating daily ETo and its accumulated values over multiple timescales, using data from two lysimeter stations in Albacete and Badajoz, Spain. Model performance was assessed against the FAO56 PM equation and against lysimeter measurements to quantify the joint effect of benchmark choice and temporal aggregation. Under FAO56 PM benchmarking, RRMSE for temperature-based models in Albacete decreased from about 0.18 for daily ETo to around 0.08 for monthly accumulated ETo, while complex models achieved daily RRMSE near 0.06–0.07, and all models exhibited RRMSE below 0.08 at monthly and longer scales. When lysimeter ETo was used as the benchmark, errors increased and became more variable in winter, with daily RRMSE often exceeding 0.22, indicating reduced lysimeter reliability under cold, calm conditions. Overall, extending the estimation interval from daily to multi-day periods markedly reduced errors and narrowed differences among models. These results show that, in the semi-arid Mediterranean environments studied, temperature-based models can provide operationally reliable estimates at irrigation-relevant timescales, while FAO56 PM offers a more robust primary benchmark than lysimeter measurements for winter irrigation planning.