Comparison of heuristic and empirical approaches for estimating reference evapotranspiration from limited inputs in Iran

Artificial intelligence and empirical ET0 models are evaluated.Local and regional scenarios were developed and tested.GEP outperforms the other applied models in ET0 estimation.Arid regions offer the lowest accuracies among the studied stations. Accurate estimation of reference evapotranspiration (ET0) values is of crucial importance in hydrology, agriculture and agro-meteorology issues. The present study reports a comprehensive comparison of empirical and semi empirical ET0 equations with the corresponding Heuristic Data Driven (HDD) models in a wide range of weather stations in Iran. Artificial Neural Networks (ANNs), Adaptive Neuro-Fuzzy Inference System (ANFIS), Support Vector Machine (SVM) and Gene Expression Programming (GEP) techniques are applied for modeling ET0 values considering different data management scenarios, and compared with corresponding Hargreaves-Samani (HS), Makkink (MK), Priestley-Taylor (PT), and Turc (T) ET0 models as well as their linear and non-linear calibrated versions along with the regression-based Copais algorithm. The obtained results confirm the superiority of GEP-based models. Further, the HDD models generally outperform the applied empirical models. Among the empirical models, the calibrated HS model found to give the most accurate results in all local and pooled scenarios, followed by the Copais and the calibrated PT models. In both local and pooled applications, the calibrated HS equation should be applied when no training data are available for the use of HDD models. The best results of the models correspond to the humid regions, while the arid regions provide the poorest estimates. This may be attributed to higher ET0 values associated with these stations and the high advective component of these locations.