Implementation of a regional parametric model for potential evapotranspiration assessment

N. Malamos, A. Tegos, I. L. Tsirogiannis, A. Christofides, and D. Koutsoyiannis, Implementation of a regional parametric model for potential evapotranspiration assessment, IrriMed 2015 – Modern technologies, strategies and tools for sustainable irrigation management and governance in Mediterranean agriculture, Bari, doi:10.13140/RG.2.1.3992.0725, 2015.



Potential evapotranspiration (PET) is key input in water resources, agricultural and environmental modelling. For many decades, several approaches have been proposed for the consistent estimation of PET at several time scales of interest. The most recognized is the Penman‐Monteith formula, which is yet difficult to apply, since it requires simultaneous measurements of four meteorological variables (temperature, sunshine duration, humidity, wind velocity). For this reason, simplified approaches prove very useful in absence of a complete data set and are strongly preferred. In the present study, we implement a recent parametric formula to model PET in the Arta plain, located in the Region of Epirus ‐ Greece, which is based on a simplified formulation of the original Penman‐Monteith expression and requires only mean hourly, daily or monthly temperature data, depending on the desired time step. The methodology is generic, yet parsimonious in terms of the input data, with its parameters adjusted through calibration, to the available PET data. A spatial analysis concerning the regionalization of the parameters and PET estimates of the proposed methodology by implementing interpolation techniques is performed. The results are very satisfactory, illustrating that the proposed framework is efficient and constitutes a reliable alternative in the assessment of potential evapotranspiration field

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Our works that reference this work:

1. A. Tegos, N. Malamos, A. Efstratiadis, I. Tsoukalas, A. Karanasios, and D. Koutsoyiannis, Parametric modelling of potential evapotranspiration: a global survey, Water, 9 (10), 795, doi:10.3390/w9100795, 2017.