Applied Closed-end Furrow Irrigation Optimized Design Based on Field and Simulated Advance Data

Authors
W. B. Nie 1
L. J. Fei 1
X. Y. Ma 2
1 Institute of Water Resources, Xi’an University of Technology, Xi’an, 710048, People Republic of China.
2 Key Laboratory for Agricultural Soil and Water Engineering in Arid Area of Ministry of Education, Yangling, 712100, People Republic of China.
Abstract
Closed-end furrows are commonly used to irrigate crop in northern part of China. The irrigation performance of furrow in this area is often low. The objectives of this paper are to verify reliability of infiltration parameters and Manning roughness estimated with SIPAR_ID software and present an optimized method for design of closed-end furrow system. The study consisted of field experiments and numerical simulation. Field experiments were conducted in two villages of Yangling district in October 2007. Infiltration parameters and Manning roughness values were estimated with SIPAR_ID software. The estimated values were put into the WinSRFR software, and then the advance trajectory, flow depths in the upstream, and irrigation performance were simulated on each test furrow. The results showed that the simulated values with the WinSRFR software were in excellent agreement with the measured data. Therefore, the infiltration parameters and Manning roughness estimated with SIPAR_ID software were reliable. Later, an optimized model for design of closed-end furrow irrigation system was proposed, based on field data and using the project of Uniform design and the WinSRFR software. Its solution required the use of optimized methodology with genetic algorithm (GA), and the inflow discharge and cutoff time were the independent variables. The results showed that adequate and efficient irrigations can be obtained using closed-end furrows through a proper selection of inflow discharge and cutoff time.

Keywords

1. Alazba, A. A. 1997. Design Procedure for Border Irrigation. Irrig. Sci., 18 (1):33-43.
2. Ampas, V. and Baltas, E., 2009. Optimization of the Furrow Irrigation Efficiency. Global NEST J., 11(4): 566-574.
3. Azamathulla, H. M., Wu F.C.., Ghani, A. A., Narulkar, S.M., Zakaria, N.A. and Chang, C.K. 2008. Comparison between Genetic Algorithm and Linear Programming Approach for Real Time Operation. J. Hydro-Environ Res., 2(3):172-181.
4. Babayan, A. V., Kapelan, Z., Savic, D. A. and Walters, G. A. 2005. Least Cost Design of Robust Water Distribution Networks under Demand Uncertainty. J. Water Resour. Plann. Manage., 131(5):375–382.
5. Bautista, E., Clemmens, A.J., Strelkoff, T.S. and Schlegel, J. 2009a. Modern Analysis of Surface Irrigation Systems with WinSRFR. Agric. Water Manage., 96(7):1146-1154.
6. Bautista, E., Clemmens, A.J., Strelkoff, T.S. and Niblack, M. 2009b. Analysis of Surface Irrigation Systems with WinSRFR—Example Application. Agric. Water Manage., 96(7):1162-1169.
7. Clemmens, A.J. 2009. Errors in Surface Irrigation Evaluation from Incorrect Model Assumptions. J. Irrig. Drainage Eng. ASCE, 135(5): 556-565.
8. Eldeiry, A., Garcia, L., Ei-Zaher, A.S.A. and El-Sherbini Kiwan, M. 2005. Furrow Irrigation System Design for Clay Soils in Arid Regions. Appl. Eng. Agric., 21(3): 411-420.
9. Elferchichia, A., Gharsallaha, O., Nouirib, I., Lebdic, F. and Lamaddalenaa, N. 2009. The Genetic Algorithm Approach for Identifying the Optimal Operation of a Multi-reservoirs On-demand Irrigation System. Biosystems Engineering, 102(3):334-344.
10. EI-Hakim, O.,Clyma, W. and Richardson, E.V. 1988. Performance Functions of Border Irrigation Systems. J. Irrig. Drain Eng. ASCE, 114(1): 118–129.
11. Fang, K.T. and Ma, C.X. 2001. Orthogonal and Uniform Design of Experiment. Beijing: Science press. PP. 247.
12. Gillies, M. H. 2008. Managing the Effect of Infiltration Variability on the Performance of Surface Irrigation. PhD Thesis, Faculty of Engineering and Surveying, University of Southern Queensland, Australia, PP. 203-204.
13. Gillies, M. H., Smith, R. J. and Raine, S. R. 2008. Measurement and Management of Furrow Irrigation at the Field Scale. Irrigation Australia 2008-Share the Water, Share the Benefits: Irrigation Australia National Conference and Exhibition, Melbourne, Australia. http://eprints.usq.edu.au/4160/.
14. Haq, Z. U. and Anwar, A. A. 2010. Irrigation Scheduling with Genetic Algorithms. J. Irri. Drainage Eng. ASCE, 136(10): 704-714.
15. Holland, J. H. 1975. Adaptation in Natural and Artificial Systems. University of Michigan Press, Cambridge Mass, PP. 5-6.
16. Khatri, K. L and Smith, R. J. 2006. Real-time Prediction of Soil Infiltration Characteristics for the Management of Furrow Irrigation. Irrig. Sci., 25(1): 33-43.
17. Langat, P. K., Raine, S. R. and Smith, R. J. 2007. Errors in Predicting Furrow Irrigation Performance Using Single Measures of Infiltration. Irrig. Sci., 25(4):339-349.
18. Ma, J. J., Sun, X. H., Guo, X. H. and Li, Y. F. 2010. Multi-objective Fuzzy Optimization Model for Border Irrigation Technical Parameters. J. Drain. Irrig. Mach. Eng., 28(2):160-163,178.
19. Madsen, H. 2003. Parameter Estimation in Distributed Hydrological Catchment Modelling Using Automatic Calibration with Multiple Objectives. Adv. Wat. Res., 26(6): 205–216.
20. Mayer, D. G., Kinghorn, B. P. and Archer, A. A. 2005. Differential Evolution an Easy and Efficient Evolutionary Algorithm for Model Optimization. Agric. Syst., 83(3): 315–328.
21. Montesinos, P., Camacho, E. and Alvarez, S. 2001. Seasonal furrow irrigation model with genetic algorithms (OPTIMEC). Agric. Water Manage., 52(1):1-16.
22. Navabian, M., Liaghat, A. M., Smith, R. J. and Abbasi, F. 2009. Empirical Functions for Dependent Variables in Cutback Furrow Irrigation. Irrig. Sci., 27(3): 215-222.
23. Pereira, L. S. and Trout T. J. 1999. Irrigation Methods. CIGR Handbook of Agricultural Engineering. Published by The American Society of Agricultural Engineers, Michigan, PP. 297–379.
24. Rodriguez, J. A. and Martos J. C., 2010. SIPAR_ID: Freeware for Surface Irrigation Parameter Identification. Environ. Model. Softw., 25(11): 1487-1488.
25. Sanchez, C. A., Zerihun, D. and Farrell-Poe K.L. 2009. Management Guidelines for Efficient Irrigation of Vegetables Using Closed-end Level Furrows. Agric. Water Manage., 96(1): 43-52.
26. Shin, H. G. and Park, H. 2000. An Optimal Design of Water Distribution Networks with Hydraulic-connectivity. J. Water SRT - Aqua, 49(4): 219–227.
27. Storn, R. and Price, K. 1997. Differential Evolution: A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. J. Glob. Optimiz., 11(4): 341-359.
28. Valipour, M. and Montazar, A. A. 2012. An Evaluation of SWDC and WinSRFR Models to Optimize of Infiltration Parameters in Furrow Irrigation. Am. J. Sci. Res., 69: 128-142.
29. Wallender, W. W. and Rayej, M. 1987. Economic Optimization of Furrow Irrigation with Uniform and Non-uniform Soil. J. Irrig. Drain. Eng. ASCE, 33(5): 1605-1611.
30. Walker,W. R. and Skogeboer, G. V. 1987. Surface Irrigation: Theory and Practice. Prentice-Hall, Englewood Cliffs, NJ, 137 PP.
31. Zerihun, D., Feyen, J. and Reddy, J. M. 1997a. Empirical Functions for Dependent Furrow Irrigation Variables. 1. Methodology and Equations. Irrig. Sci., 17(3): 111-120.
32. Zerihun, D., Wang, Z., Feyen, J. and Reddy, J. M. 1997b. Empirical Functions for Dependent Furrow Irrigation Variables. 2. Application. Irrig. Sci., 17(3):121-126.
Journal of Agricultural Science and Technology
Volume 16, Issue 2
March and April 2014
Pages 395-408