Performance of simple temperature-based evaporation methods compared with a time series of pan evaporation measures from a standard 20 m2 tank

Abstract

Evaporation and evapotranspiration is crucial part of hydrological and water resource management studies e.g. water footprinting. Proper methods for estimating evaporation/potential evapotranspiration using limited climatic data are critical if the availability of climatic data is extremely limited. In a large scale studies are very often used generalized (modelled or gridded) input data. For a large scale water footprint studies is also important to find methods as simple as possible with quantifiable error. In our study, nine simple temperature-based empirical equations were compared with a long term time series of real evaporation data from a 20 m2 tank at Hlasivo station. In the first step, we used real temperature measured at Hlasivo station for validation of equations. In the second step, the gridded temperature data (interpolated datasets) derived from the meteorological stations were used. For both datasets, the differences between observed and predicted values were categorized into three groups of accuracy and the statistical indices of each equation were calculated. Very good results were achieved with the Hamon equation from 1961 and the Oudin equation for both datasets with index of agreement (d) higher than 0.9, cross-correlation coefficient (R2) around 0.7 and root mean square error (RMSE) around 0.5 mm∙(24 h)−1 The Kharrufa equation, which was developed for semi-arid or arid areas, also provides results with sufficient accuracy. Comparison of the results with similar studies showed a lower accuracy of very simple equations against more complex equations, which have RMSE lower than 0.25 mm∙(24 h)−1. But for some kind of studies, quantifiable errors with sufficient accuracy can be more important than the absolute accuracy.

Optimal Calibration of Evaporation Models against Penman–Monteith Equation

“…They are derived by regression between the observed evaporation and mean daily air temperature, using statistical regression to find both linear and exponential models. The model relations presented in this section are compared in the paper [34] with measurements on 20 m 2 evaporation pan placed in the meteorological station Hlasivo near the city of Tábor 49 • C290 N, 14 • C450 E) in the South Bohemian Region. This station is operated by Výzkumný ústav vodohospodářský T. G. Masaryka (VUV, T. G. Masaryk Water Research Institute) and was built in 1957 and has a 20 m 2 evaporation tank, GGI-3000 pan and Class-A pan.…”

“…The coefficient 0.34 was found empirically and it is possible to refine it with respect to the site-specific conditions. For example, in [34], the form E K = 0.25 p T 1.3 a is given with the formula being calibrated for the conditions of the Hlasivo weather station in the South Bohemian Region. In this study, the form ( 16) with coefficient 0.34 is used.…”

Srovnání různých metod stanovení vodní stopy výroby elektrické energie ve vodní elektrárně Orlík – případová studie

“…Analýzou dat z této výparoměrné stanice byly odvozeny další rovnice [16,17]. Výsledky rovnic využívající jako popisnou proměnnou pouze teplotu vzduchu byly porovnány na datech ze stanice Hlasivo z období květen až říjen v letech 1957 až 2012, přičemž tato analýza [18]…”

“…By analyzing data from this station, more equations were derived [16,17]. The results of equations using only air temperature as a descriptive variable were compared with the data sourced from Hlasivo station in May to October for the period 1957-2012; these showed [18] that even these equations, using only air temperature as an independent variable, show sufficient accuracy of the resulting values. Each of the equations performs better at different temperature ranges.…”

Artificial neural network and energy budget method to predict daily evaporation of Boudaroua reservoir (northern Morocco)

“…Furthermore, the physical parameters of the lakes and reservoirs, such as size, shape and depth, affect the pan evaporation (Antonopoulos, Gianniou and Antonopoulos, 2016). Evaporation is determined using direct methods by installing more precise and costly devices, including primarily pan evaporimeter and indirect methods, including empirical or semi-empirical models such as Penman-Monteith, Priestley-Taylor, De Bruin-Kejiman, Jensen-Haise, Makkink, energy budget method, and mass transfer method (Hussein, 2017;Ansorge and Beran, 2019;Wang et al, 2019) that rely on meteorological measurements such as solar radiation, air temperature, relative humidity and wind speed (Jhajharia et al, 2009), while the applications of these techniques are often limited by data availability and completeness (Majidi et al, 2015). Many researchers have significantly utilised artificial intelligence approaches to facilitate the estimation of evaporation rate and other weather variables like rainfall (Shafaei et al, 2016) and relative humidity (El Azhari et al, 2022).…”