Desempeño y predicción de la evapotranspiración de referencia utilizando siete métodos alternativos, en el estado de Sinaloa, México
Resumen
Este estudio tuvo como objetivo evaluar el desempeño diario de siete métodos alternativos de ETo (; la letra i se refiere a métodos alternativos) y predecir la ETo acumulada anual con el método estándar de Penman-Monteith con datos limitados (EToPMP), en el estado de Sinaloa, México. Se obtuvieron datos diarios de 11 estaciones meteorológicas en Sinaloa para el período 1969–2018 de la Comisión Nacional del Agua y el Servicio Meteorológico Nacional (CONAGUA–SMN), incluyendo temperatura máxima, temperatura mínima y evaporación (Eva). Además, de la estación meteorológica San Juan de CONAGUA–SMN, se obtuvieron los siguientes datos: velocidad del viento, temperatura media, radiación solar incidente, presión atmosférica y humedad relativa. Los siete métodos alternativos de ETo () calculados incluyeron: Romanenko (EToRo), Priestley–Taylor (EToPT), McGuinness y Bordne (EToMB), Hargreaves (EToH75), evaporación en tanque (EToTE), Hargreaves (EToH85) y Oudin (EToOu). También se calculó la ETo media diaria utilizando el método de Penman-Monteith (EToPM), con datos limitados. Se calcularon el error absoluto medio (MAE) y el error cuadrático medio (RMSE) entre EToPM y los siete métodos de . Se utilizaron modelos de regresión lineal y no lineal multiples para predecir la EToPMP (variable dependiente) a partir de los siete métodos de (variables independientes). En general, los con mejor rendimiento fueron EToTE (MAE de 0.360 mm día⁻1 a 2.294 mm día⁻1 y RMSE de 0.594 mm día⁻1 a 3.094 mm día⁻1). La EToPT fue la única que contribuyó a la explicación de todos los modelos de EToPMP. Los modelos desarrollados en este estudio pueden contribuir a mejorar la eficiencia del riego agrícola en Sinaloa, estado considerado el granero de México.
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Fang, S.L., Lin, Y.S., Chang, S.C., Chang, Y.L., Ysai, B.Y. and Kuo, B.J. (2024). Using Artificial Intelligence Algorithms to Estimate and Short-Term Forecast the Daily Reference Evapotranspiration with Limited Meteorological Variables. Agriculture, (14), 510. https://doi.org/10.3390/agriculture14040510.
Flores, C.L.M., Arzola, G.J.F., Ramírez, S.M. and Osorio, P.A. (2012). Global climate change impacts in the Sinaloa state, Mexico. Cuad. Geogr., (21), 115–129. http://www.scielo.org.co/scielo.php?script=sci_arttext&pid=S0121-215X2012000100009.
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Galindo, R.J.G. and Alegría, H. (2018). Toxic effects of exposure to pesticides in farm workers in Navolato, Sinaloa (Mexico). Rev. Int. Contam. Ambient., (34), 505–516. https://doi.org/10.20937/rica.2018.34.03.12.
Gao, F., Feng, G., Ouyang, Y., Wang, H., Fisher, D., Adeli, A. and Jenkins, J. (2017). Evaluation of Reference Evapotranspiration Methods in Arid, Semiarid, and Humid Regions. J. Am. Water Resour. Assoc., (53), 791–808. https://www.srs.fs.usda.gov/pubs/ja/2017/ja_2017_ouyang_008.pdf.
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Guijarro, J.A. (2024). Package ‘climatol’ version 4.1.0: climate tools (series homogenization and derived products). Repository CRAN, 41 p. https://cran.r-project.org/web/packages/climatol/climatol.pdf.
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Publicado
2025-07-31
Cómo citar
Llanes Cárdenas, O., Norzagaray Campos, M., Pérez González, E., Ávila Díaz, J. A., & Arciniega Galaviz, M. A. (2025). Desempeño y predicción de la evapotranspiración de referencia utilizando siete métodos alternativos, en el estado de Sinaloa, México. RIDE Revista Iberoamericana Para La Investigación Y El Desarrollo Educativo, 16(31). https://doi.org/10.23913/ride.v16i31.2532
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