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<p>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<sup>2</sup>), 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.</p>

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Investigation of 50 temperature-based models for estimating potential evapotranspiration (PET) in a semi-arid region

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月9日

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  • J. Ramachandran,
  • A. Anto Rashwin,
  • K. Arunadevi,
  • R. Lalitha,
  • S. Vallal Kannan,
  • K. Sivasubramanian,
  • Balaji Kannan

摘要

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 (R2), 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.