Improving irrigation management by modelling the irrigation schedule

Aelst, P. Van; Ragab, R.; Feyen, Jan; Raes, Dirk

doi:10.1016/0378-3774(88)90148-5

Cited by 14 publications

(2 citation statements)

References 6 publications

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“…La simulación utilizando modelos hidrológicos se ha demostrado útil para llevar a cabo esta tarea. Han sido muchos los modelos empleados para simular los diferentes componentes del ciclo hidrológico en la agricultura de riego, desde los funcionales (Doorenbos y Pruitt, 1977;Doorenbos y Kassam, 1979;Allen et al, 1998), hasta los mecanísticos (Abbott et al,1986;van Aelst et al, 1988;Williams et al, 1989). Recientemente se han combinado con los modelos de simulación, técnicas tales como la teledetección (Roberts et al, 1992;Shih y Jordan, 1993;D'urso et al, 1999;Kite, 2000; Kite y Droogers, 2000) y los sistemas de información geográfica (SIG) (Calixte et al, 1992;Lal et al, 1993;Mallawaarachchi et al, 1996;Beinroth et al, 1998;Hartkamp et al, 1999) que facilitan la adquisición de información y el análisis espacial.…”

Section: Introductionunclassified

Aplicación de un modelo de simulación a la evaluación de una zona regable

Lorite¹,

Mateos²,

Castiel³

2003

ing.agua

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La mejora en la gestión del riego en las zonas regables españolas es cada día más necesaria. Para contribuir a esta tarea, se ha elaborado un modelo de simulación que permite el análisis y la evaluación del manejo del agua en la zona regable del Genil – Cabra, en la provincia de Córdoba. Con este modelo y un indicador de funcionamiento se ha logrado hacer una primera evaluación de la zona, tanto por cultivos como espacialmente. El contraste entre el agua aplicada en el riego y la dotación económicamente óptima calculada por el modelo indicó que en el año 1996/97, la zona se regó deficitariamente, aplicándose algo menos de la mitad (45%) de los requerimientos óptimos calculados. El análisis espacial ha posibilitado determinar zonas en donde el aporte de agua por parte del agricultor está más próximo a las necesidades óptimas de los cultivos.

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Section: Introductionunclassified

Aplicación de un modelo de simulación a la evaluación de una zona regable

Lorite¹,

Mateos²,

Castiel³

2003

ing.agua

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“…In shallow water table regions, the CGWR is an important part of the water source for crop root zones. Its estimation is important for optimizing crop water requirements (Xue et al, 2008), studying the crop water balance (Vincke and Thiry, 2008) and crop growth (Han et al, 2015), modeling crop irrigation schedules (Van Aelst et al, 1988), predicting soil salinity (Askri et al,2010), modeling root water uptake (Šimůnek and Hopmans, 2009), and analyzing the interaction between groundwater and plants (Yin et al, 2015).…”

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Estimating the Contribution of Groundwater to the Root Zone of Winter Wheat Using Root Density Distribution Functions

Zhu

Ren

Horton

et al. 2018

Vadose Zone Journal

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For winter wheat (Triticum aestivum L.) that grows during the rainless season, the contribution of groundwater to the root zone (CGWR) is an important water source for growth. Accurately estimating the CGWR is important for making decisions on irrigation and discharge for winter wheat fields and preventing water pollution. Because winter wheat slows and even stops root growth over winter, so the fixed root density distribution function that is suitable for soybean [Glycine max (L.) Merr.] may not suit winter wheat calculations. Therefore, when estimating the CGWR of winter wheat with the numerical model HYDRUS-1D, the root density distribution function should first be determined from two types: fixed or piecewise root density distribution functions. Based on field observations and local weather data for 2004-2005 and 2005-2006, HYDRUS-1D was evaluated with different root density distribution functions by comparing simulated and measured root zone soil water contents. The evaluated model with the most suitable distribution function was used to estimate the daily CGWR for six winter wheat hydrological growth seasons. For all seasons, winter wheat growth was assumed to be at its optimal state. The main results were: (i) a piecewise root density distribution function was the most suitable for winter wheat; (ii) simulated seasonal CGWRs were 154, 128, and 136 mm in the dry, normal, and wet seasons, respectively; and (iii) the CGWR for winter wheat transpiration was about 58, 47, and 69% of the total in dry, normal, and wet seasons, respectively. Overall, we concluded that accurate description of the root density distribution was helpful to estimate the CGWR.Abbreviations: CGWR, contribution of groundwater to the root zone; LAI, leaf area index; WTD, water table depth.

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