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Investigating the trends of reference evapotranspiration (ETo) is fundamental importance for water resource management in agriculture, climate variability analysis, and other hydroclimate-related projects. Moreover, it would be useful for understanding the sensitivity of such trends to basic meteorological variables, as the modifications of these variables due to climate change are more easily predictable. This study aims to analyze ETo trends and sensitivity in relation to different explanatory meteorological factors. The study used a 17 year-long dataset of meteorological variables from a station located in Piazza Armerina, Sicily, a region characterized by a Mediterranean climate. First, the FAO-Penman-Monteith method was applied for estimation of ETo. Next, the Mann-Kendall test with serial autocorrelation removal by Trend-free pre-whitening (TFPW) was applied to analyze ETo trends and the basic meteorological variables on which they depend. Sen’s slope was also used to examine the magnitude of the trend of monthly ETo and its related meteorological variables. According to the obtained results, ETo only showed a downward trend of 0.790 mm per year in November, while no trend is shown in other months or on seasonal and annual time scales. Solar radiation (November and Autumn) and rainfall (Autumn) showed a downward trend. The other meteorological variables (minimum temperature, maximum temperature, mean temperature, wind speed, and relative humidity) showed an upward trend both at monthly and seasonally scale in the study area. The highest and lowest sensitivity coefficients of ETo in the study area are obtained for specific humidity and wind speed, respectively. Specific humidity and wind speed give the highest (44.59%) and lowest (0.9%) contribution to ETo trends in the study area. These results contribute to understanding the potential and possible future footprint of climate change on evapotranspiration in the study area.

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Large-scale photovoltaic (PV) power plants may affect the hydrological cycle in all its components. Among the various components, evapotranspiration is one of the most important. As a preliminary step for assessing the impacts of PV plants on evapotranspiration, in this study, we performed an evaluation study of methods for estimating reference evapotranspiration (ETo). FAO and ASCE recommend the Penman–Monteith (PM) method for the estimation of ETo when the data for all involved variables are available. However, this is often not the case, and different empirical methods to estimate ETo, requiring mainly temperature data, need to be used. This study aimed at assessing the performance of different temperature- and radiation-based empirical ETo estimation methods against the standardized PM ETo method in an experimental photovoltaic power plant in Piazza Armerina, Sicily, Italy, where a meteorological station and a set of sensors for soil moisture were installed. The meteorological data were obtained from the Lab from July 2019 to end of January 2022. By taking the ETo estimations from the PM method as a benchmark, the study assessed the performance of various empirical methods. In particular, the following methods were considered: Hargreaves and Samani (HS), Baier and Robertson (BR), Priestley and Taylor (PT), Makkink (MKK), Turc (TUR), Thornthwaite (THN), Blaney and Criddle (BG), Ritchie (RT), and Jensen and Haise (JH) methods, using several performance metrics. The result showed that the PT is the best method, with a Nash–Sutcliffe efficiency (NSE) of 0.91. The second method in order of performance is HS, which, however, performs significantly worse than PT (NSE = 0.51); nevertheless, this is the best among methods using only temperature data. BG, TUR, and THN underestimate ETo, while MKK, BG, RT, and JH showed overestimation of ETo against the PM ETo estimation method. The PT and HS methods are thus the most reliable in the studied site.