This study evaluates the performance of 50 empirical models for estimating potential evapotranspiration (PET) using daily meteorological data from a semi-arid region in Tamil Nadu, India. These temperature- and humidity-based models were compared against the FAO56 Penman–Monteith (PM) model, a globally accepted benchmark. Statistical indices such as the coefficient of determination (R 2 ), mean absolute error, standard error estimate, and long-term average ratio (rt) were employed to assess model accuracy and reliability. The results revealed that certain models—specifically Althoff (Water 11:2272, 2019), Pereira (Agric Water Manag 66:251–257), and Samani (J Irrig Drain Eng 126:265–267, 2000)—exhibited strong agreement with the FAO56 PM model, offering a robust balance between accuracy and simplicity. The Althoff et al. model achieved the highest ranking based on standardized performance indices. This study is significant in identifying cost-effective alternatives for PET estimation in data-scarce, semi-arid regions, which is essential for irrigation planning and water resource management. However, a key limitation of this research is its exclusion of radiation and wind-based models, which may provide enhanced accuracy under certain conditions. Future research should explore the integration and calibration of multiple climatic parameters to improve PET estimation further.
Ramachandran et al. (2026) studied this question.