The SRFR 5 Modeling System for Surface Irrigation

Bautista, E.; Schlegel, James L.; Clemmens, A. J.

doi:10.1061/(asce)ir.1943-4774.0000938

Cited by 16 publications

(11 citation statements)

References 17 publications

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“…The simple post-irrigation volume balance (PIVB) [54][55][56] was used to solve the Kostiakov equation parameters (k, α). WinSRFR software [3,4,57] provides a hydraulic simulation tool for solving the PIVB. The input data for WinSRFR software for each furrow irrigation event included the irrigation water requirement, system geometry (furrow length, space, and cross section), average furrow slope, roughness coefficient (in this study, n = 0.04 [21]), inflow rate, cut-off time, downstream conditions (in this study, blocked-ended furrow), and advance and recession times at each measurement distance within each furrow (in this study, every 5 m along the furrow length).…”

Section: Kostiakov Modelmentioning

confidence: 99%

An improved approach to estimating the infiltration characteristics in surface irrigation systems

et al. 2020

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The most common methods for estimating the infiltration function are measurements through a double-ring infiltrometer (DRI) and empirical models. Infiltration data always exhibit different kinds of scatter, which affect the accuracy of the estimated infiltration function. This study presents a new methodology to calibrate the infiltration function. The suggested approach is based on combining the DRI method with the changes in the measured soil water content. Furrow irrigation experiments were conducted to estimate the infiltration function using different methods and to investigate the effect of data scatter on the reliability of the estimated infiltration function. Furrow elevations were observed, and for each irrigation event advance times, recession times, and inflow rates were observed. The infiltration depths were measured as a function of the change in the soil water content before and after irrigation event. Infiltration parameters were estimated using DRI treatment, empirical model (Kostiakov model), and suggested approach. Measured and simulated infiltration depths using the described methods were compared. The results show that the infiltration depths estimated using a DRI were lower than the observed infiltration depths, while the infiltration depths estimated using the empirical model were higher than the observed infiltration depths. The results indicate that the infiltration function estimated using the recommended approach was more accurate and reasonable than the infiltration function estimated using the DRI, and empirical (Kostiakov model) methods. In addition, the proposed approach can reduce the required measurements during the irrigation event, and can also reduce the potential scatter in the estimated infiltration function that results from soil variability and measurement errors.

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Section: Kostiakov Modelmentioning

confidence: 99%

An improved approach to estimating the infiltration characteristics in surface irrigation systems

et al. 2020

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“…The upgraded software SRFR 5 is a program for modeling surface irrigation systems that is a central component of WinSRFR, which is a software package for the hydraulic analysis of surface irrigation systems. SRFR 5 was discussed by Bautista et al [15], providing details about computational methods and new features.…”

Section: Surface Irrigation Systemsmentioning

confidence: 99%

Challenges for Optimum Design of Surface Irrigation Systems

El-Hazek

2016

JSRR

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There are many challenges to be faced in order to achieve the optimum use of available resources against the increasing food and other demands of the world population with the shrinking resources. Optimization can be considered as the ensemble of steps which must be taken to find the minimum, or maximum, of a function that is usually called an objective function. This paper provides snapshots of research and practical implementation of optimum design concepts and criteria of surface irrigation systems. Technical papers and reports are reviewed addressing challenges and different aspects of this issue, in addition to optimum allocation systems of surface water and ground water. It is concluded that the optimum irrigation may be defined more broadly as maximization of overall benefits, including nonmonetary benefits as water quality protection, food security, increased employment, and resettlement of populations. Linear programming, dynamic programming and genetic algorithms techniques are still very popular to get optimal surface irrigation systems. Surface irrigation parameters identification represents an important way to get optimal surface irrigation systems. Optimal conjunctive use of surface and ground water resources may be needed in some cases, such as water deficits in arid and semi-arid regions and uncertainty.

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“…POZAL software was specifically developed for this work and applies the complete hydraulic model of the onedimensional equations of free surface flow (Saint-Venant equations), using the finite differences method according to the MacCormack scheme (Dholakia et al 1998;García-Navarro et al 1992), by dividing the field into 100 equal parts. More popular programs, like WinSRFR (Bautista et al 2015) or SIRMOD (Walker, 1998) could be used here instead of POZAL. In that case, the iterative process must be applied manually, taking a few hours of work.…”

Section: Read Datamentioning

confidence: 99%

“…A small slope in the advance direction can improve performance (Khanna and Malano, 2006), and the selection of best slope requires careful analysis for every case (Khanna et al, 2003). In onedimensional approach, this best slope can be obtained with a simulation tool, as SIRMOD (Walker, 1998) or WinSRFR (Bautista et al, 2015). or with non-dimensional graphs (González-Cebollada et al, 2011).…”

Section: Introduction and Objectivesmentioning

confidence: 99%

Optimization of Field Topography in Surface Irrigation

González‐Cebollada

Moret‐Fernández²,

Buil-Moure

et al. 2016

J. Irrig. Drain Eng.

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This work presents and applies a new methodology to find the optimal topography of a surface irrigation field, achieving a theoretically uniform surface irrigation. For any variant on surface irrigation (basin, border or furrow, with open or blocked end), the method's result is a particular curved topographical shape of a field. This shape distributes water evenly over the field, so that distribution uniformity is theoretically 100% and deep percolation disappears. The methodology is applied to two theoretical cases: a 1-D blocked-end field and a 2-D square field with corner inflow. For each case, the methodology reaches a particular topography where distribution uniformity is near 100%. To put into practice this methodology, the optimized topography (which has a curved shape) must be approached to a set of slopes. A real example is shown where a real field was laser-levelled with two consecutive slopes to fit the optimized topography, previously calculated with the methodology here presented. The irrigation was evaluated before and after the optimization. The results indicate an increase of distribution uniformity from 82% to 96%.

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The SRFR 5 Modeling System for Surface Irrigation

Cited by 16 publications

References 17 publications

An improved approach to estimating the infiltration characteristics in surface irrigation systems

An improved approach to estimating the infiltration characteristics in surface irrigation systems

Challenges for Optimum Design of Surface Irrigation Systems

Optimization of Field Topography in Surface Irrigation

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