A. Tegos, A. Efstratiadis, N. Malamos, N. Mamassis, and D. Koutsoyiannis, Evaluation of a parametric approach for estimating potential evapotranspiration across different climates, IRLA2014 – The Effects of Irrigation and Drainage on Rural and Urban Landscapes, Patras, doi:10.13140/RG.2.2.14004.24966, 2014.
Potential evapotranspiration (PET) is key input in water resources, agricultural and environmental modelling. For many decades, numerous 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 in data-scarce areas, since it requires simultaneous observations of four meteorological variables (temperature, sunshine duration, humidity, wind velocity). For this reason, parsimonious models with minimum input data requirements are strongly preferred. Typically, these have been developed and tested for specific hydroclimatic conditions, but when they are applied in different regimes they provide much less reliable (and in some cases misleading) estimates. Therefore, it is essential to develop generic methods that remain parsimonious, in terms of input data and parameterization, yet they also allow for some kind of local adjustment of their parameters, through calibration. In this study we present a recent parametric formula, based on a simplified formulation of the original Penman-Monteith expression, which only requires mean daily or monthly temperature data. The method is evaluated using meteorological records from different areas worldwide, at both the daily and monthly time scales. The outcomes of this extended analysis are very encouraging, as indicated by the substantially high validation scores of the proposed approach across all examined data sets. In general, the parametric model outperforms well-established methods of the everyday practice, since it ensures optimal approximation of PET.
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Our works that reference this work:
|1.||A. Tegos, H. Tyralis, D. Koutsoyiannis, and K. H. Hamed, An R function for the estimation of trend signifcance under the scaling hypothesis- application in PET parametric annual time series, Open Water Journal, 4 (1), 66–71, 6, 2017.|