Tasa diaria de evapotranspiración para una macrófita empleando variables meteorológicas
Evapotranspiration (ET) is usually the main output of hydrological cycle hence its quantification is important for water resources management. This paper presents a methodology for estimating indirectly the daily ET rates from a macrophyte under humid temperate weather. Simultaneously was implemente...
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| Autores principales: | , , |
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| Formato: | Artículo revista |
| Lenguaje: | Español |
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CURIHAM: Centro Universitario Rosario de Investigaciones Hidroambientales Facultad de Ciencias Exactas, Ingeniería y Agrimensura. Universidad Nacional de Rosario Director: Dr. Ing. Hernán Stenta Riobamba 245 bis, 2000 Rosario (Santa Fe), Argentina. Telefa
2011
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| Acceso en línea: | https://cuadernosdelcuriham.unr.edu.ar/index.php/CURIHAM/article/view/105 |
| Aporte de: |
| Sumario: | Evapotranspiration (ET) is usually the main output of hydrological cycle hence its quantification is important for water resources management. This paper presents a methodology for estimating indirectly the daily ET rates from a macrophyte under humid temperate weather. Simultaneously was implemented an experiment where the mean daily measured ET rate was 5.2 mm day-1. Because the ET rate is a function of climatic, soil and vegetation characteristics, and that usually only the first one are recorded routinely, the daily ET rates were studied based on 12 measured meteorological variables (including solar radiation, temperature, relative humidity, wind speed and atmospheric pressure). Single and multiple linear regression models was implemented to assess the correlation between measured ET rates and weather variables. The regression analysis indicates that solar radiation is the variable that best explains the process of ET (R2 = 0.54). When more variables are considered in addition to radiation, appear variables related to air temperature (minimum temperature, dew point temperature and maximum temperature). Use of multiple variables increased significantly the coefficient of determination (R2 = 0.72). Using more variables does not improve results |
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