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Eisa Maroufpoor

Eisa Maroufpoor

Academic rank: Professor
ORCID:
Education: PhD.
ScopusId: 36682969100
Faculty: Faculty of Agriculture
Address: Department of Water Engineering, University of Kurdistan Sanandaj,Iran PoBOX: 416 Tel: 871 6627722-25 ext. 320 Fax: 871 6620550
Phone: 08733620552

Research

Title
Estimation of wind drift and evaporation losses of sprinkler irrigation systems using dimensional analysis
Type
JournalPaper
Keywords
Buckingham's π method Moving lateral Solid-set
Year
2023
Journal Agricultural Water Management
DOI
Researchers Younes Aminpour ، darya dehghan ، Enrique Playan ، Eisa Maroufpoor

Abstract

Wind Drift and Evaporation Losses (WDEL) critically determine the application efficiency of sprinkler irrigation systems. In specific conditions, even half of the irrigation water can evaporate or drift out of the irrigated area. The purpose of this study was to develop a WDEL estimation equation based on dimensional analysis (Buckingham's π method), considering a wide set of independent variables. An equation was sought that could be used for various sprinkler technologies and meteorological conditions in semiarid areas. Our research used experimental data from the literature, obtained in northeastern Spain and northwestern Iran. The complete data set consisted of 153 WDEL observations obtained at different operating pressures, nozzle diameters, irrigation durations, day and night irrigations, elevations from the soil surface and meteorological conditions. The experiments involved solid-sets and moving laterals, impact sprinklers, gear driven sprinklers and rotating spray plate sprinklers. The equation developed in this research included four technical independent variables (main nozzle diameter, auxiliary nozzle diameter, operating pressure and nozzle elevation) and four meteorological independent variables (air temperature and relative humidity, wind speed and solar radiation). The equation resulted in improved estimation respect to the equations previously derived from part of the experimental data set. The proposed equation was calibrated using 70% of the experimental data and validated using 30% of the experimental data. When the proposed equation was applied to the complete experimental data set, the determination coefficient was 0.81 and the root mean square error was 3.49%. The proposed equation represents a clear improvement respect to the equations reported in the literature in terms of the number and range of the independent variables and the predictive capacity.