Evaluation of Evaporation from Water Reservoirs in Local Conditions at Czech Republic

• Published in: Hydrology 2021-12, Vol.8 (4), p.153
• Main Authors: Melišová, Eva, Vizina, Adam, Hanel, Martin, Pavlík, Petr, Šuhájková, Petra
• Format: Article
• Language: English
• Subjects: Catchment area; Climate change; Climate models; Discriminant analysis; ERA5-Land data; Evaporation; Geomorphology; Humidity; Hydrologic cycle; Hydrology; Learning algorithms; Machine learning; observed data; Precipitation; R language; Radiation; regression; Regression analysis; Regression models; Remote sensing; Reservoirs; Runoff; Statistical analysis; Statistical methods; Statistics; Time series; Variables; Water; water reservoir; Water reservoirs; Water storage
• ISSN: 2306-5338; 2306-5338
• DOI: 10.3390/hydrology8040153

Evaporation is an important factor in the overall hydrological balance. It is usually derived as the difference between runoff, precipitation and the change in water storage in a catchment. The magnitude of actual evaporation is determined by the quantity of available water and heavily influenced by climatic and meteorological factors. Currently, there are statistical methods such as linear regression, random forest regression or machine learning methods to calculate evaporation. However, in order to derive these relationships, it is necessary to have observations of evaporation from evaporation stations. In the present study, the statistical methods of linear regression and random forest regression were used to calculate evaporation, with part of the models being designed manually and the other part using stepwise regression. Observed data from 24 evaporation stations and ERA5-Land climate reanalysis data were used to create the regression models. The proposed regression formulas were tested on 33 water reservoirs. The results show that manual regression is a more appropriate method for calculating evaporation than stepwise regression, with the caveat that it is more time consuming. The difference between linear and random forest regression is the variance of the data; random forest regression is better able to fit the observed data. On the other hand, the interpretation of the result for linear regression is simpler. The study introduced that the use of reanalyzed data, ERA5-Land products using the random forest regression method is suitable for the calculation of evaporation from water reservoirs in the conditions of the Czech Republic.