Water Resources Management

, Volume 33, Issue 3, pp 1053–1068 | Cite as

A Simulation-Optimization Model for Conjunctive Use of Canal and Pond in Irrigating Paddy Fields

  • Hamideh NooryEmail author
  • Mona Deyhool
  • Farhad Mirzaei


In this study, a new methodology is presented for conjunctive use of canal and pond to assess the role of ponds in saving local water and optimizing use of them in supplying irrigation water requirement of paddy fields. Evapotranspiration, runoff, irrigation water requirement, irrigation interval and pond water balance were simulated and then number of canal intake, storage volume and released volume of pond were optimized by binary genetic algorithm. Minimizing the number of canal intake and limitations of no deficit irrigation and storage volume of pond was defined as objective function and constraints, respectively. The effectiveness of the proposed methodology is examined through applying it for Fashtam pond in Sangar, Guilan province in Northern part of Iran. The results showed that 60, 54 and 46% of irrigation requirement of paddy fields were supplied by precipitation and runoff water saved in Fashtam pond and the rate of overflow of pond to maximum pond volume were 100, 74 and 34% in wet, normal and dry years, respectively. The model develops a scenario to increase pond capacity in selected case study for Fashtam pond located in Guilan province in Iran. It demonstrates how supply of irrigation to paddy fields has been increased to 100, 91 and 61% in wet, normal and dry years, respectively through simulation-optimization approach.


Irrigation water Optimized allocation Precipitation harvesting Runoff 


Compliance with Ethical Standards

Conflict of Interest



  1. Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. FAO, Rome, 300(9), D05109Google Scholar
  2. Anbumozhi V, Matsumoto K, Yamaji E (2001) Towards improved performance of irrigation tanks in semi-arid regions of India: modernization opportunities and challenges. Irrig Drain Syst 15(4):293–309CrossRefGoogle Scholar
  3. Arnold JG, Williams JR, Srinivasan R, King KW (2000) Soil and water assessment tool manual. USDA Agricultural Research Service, TexasGoogle Scholar
  4. Bakhtfirooz A (2010) Role of ponds in recharge of growndwater in north of Iran. National conference in management of water resource (in Persian)Google Scholar
  5. Camnasio E, Becciu G (2011) Evaluation of the feasibility of irrigation storage in a flood detention pond in an agricultural catchment in northern Italy. Water Resour Manag 25:1489–1508. CrossRefGoogle Scholar
  6. Chen S, Shao D, Li X, Lei C (2016) Simulation-optimization modeling of conjunctive operation of reservoirs and ponds for irrigation of multiple crops using an improved artificial bee Colony algorithm. Water Resour Manag 30(9):2887–2905CrossRefGoogle Scholar
  7. Chien C, Fang W (2012) Modeling irrigation return flow for the return flow reuse system in paddy fields. Paddy Water Environ 10(3):187–196CrossRefGoogle Scholar
  8. Fujihara Y, Oda M, Horikawa N (2011) Hydrologic analysis of rain fed rice area using a simple semi-distributed water balance model. Water Resour Manag 25:2061–2080CrossRefGoogle Scholar
  9. Ghorbani Sarhangi Z, Shahnazari A, Azi Pashakolae S, Mortezapour M (2012) Optimum area and volume of ponds in north of Iran. International River Engineering conference, AhvazGoogle Scholar
  10. Hayashi M, van der Kamp G (2007) Water level changes in ponds and lakes: the hydrological processes. In: Plant disturbance ecology: the process and the response, pp 311–339Google Scholar
  11. Holland JH (1975) Adaptation in natural and artificial systems. MIT Press, CambridgeGoogle Scholar
  12. Jung Choe L, Jin Choe K, Su Han M, Kyeong Kim M, Kun Choi S, Son Bang H, Eo J, Eun Na Y, Hyung Kim M (2016) Benthic macroinvertebrate biodiversity improved with irrigation ponds linked to a rice paddy field. Entomological Research 46(1):70–79CrossRefGoogle Scholar
  13. Kuo SF, Merkley GP, Liu CW (2000) Decision support for irrigation project planning using a genetic algorithm. Agric Water Manag 45(3):243–266CrossRefGoogle Scholar
  14. Loeve R, Dong B, Zhoa JH, Zhang SJ, Molden D (2001) Operation of Zhanghe irrigation system. In: Barker R, Loeve R, Li YH, Tuong TP (eds) Water saving irrigation for rice: proceedings of an international workshop. Wuhan, China. International Water Management Institute, ColomboGoogle Scholar
  15. Meenu R, Rehana S, Mujumdar PP (2013) Assessment of hydrologic impacts of climate change in Tunga-Bhadra river basin, India with HEC-HMS and SDSM. Hydrol Process 27(11):1572–1589CrossRefGoogle Scholar
  16. Mehraban M (2010) Development and improvement of pond for management of seasonal drought in Guilan. National conference in management of pond in North of Iran (in Persian)Google Scholar
  17. Mushtaq S, Dawe D, Lin H, Moya P (2006) An assessment of the role of ponds in the adoption of water-saving irrigation practices in the Zhanghe irrigation system, China. Agric Water Manag 83(2):100–110CrossRefGoogle Scholar
  18. Mushtaq S, Dawe D, Hafeez M (2007) Economic evaluation of small multi-purpose ponds in the Zhanghe irrigation system, China. Agric Water Manag 91(1):61–70CrossRefGoogle Scholar
  19. Navabian M, Aghajan M, Rezaei M (2011) Optimal irrigation regime under different yield of Oryza (HasHemi variety), ICID 21st Congress, Tehran, IranGoogle Scholar
  20. Nixon J, Dandy G, Simpson A (2001) A genetic algorithm for optimizing off-farm irrigation scheduling. J Hydroinf 3:11–22CrossRefGoogle Scholar
  21. Parsinejad M, Yazdani MR, Ebrahimian H (2009) Field and regional scale evaluation of irrigation efficiency in paddy fields case study: Guilan, Iran. Irrig Drain 58(2):147–156CrossRefGoogle Scholar
  22. Ponce VM, Hawkins RH (1996) Runoff curve number: has it reached maturity? J Hydrol Eng 1(1):11–19CrossRefGoogle Scholar
  23. Razavipour T (1998) Measurement of deep percolation in soil in Guilan. M.S. Thesis, Guilan UniversityGoogle Scholar
  24. Reshmidevi TV, Jana R, Eldho TI (2008) Geospatial estimation of soil moisture in rain-fed paddy fields using SCS-CN-based model. Agric Water Manag 95:447–457CrossRefGoogle Scholar
  25. Service C (1985) National engineering handbook, section 4: hydrologyGoogle Scholar
  26. Srinivas R, Singh AP, Deshmukh A (2017) Development of a HEC-HMS based watershed modeling system for identification, allocation and optimization of reservoirs in a river basin. Environ Monit Assess 190:31Google Scholar
  27. Tsun Fang W, Pin Chien C, Chen Chen S (2012) Study on agricultural benefits by increasing capacity of water ponds: a case study at Taoyuan paddy fields. Paddy Water Environ 10(1):231–250CrossRefGoogle Scholar
  28. Wöhling T, Vrugt JA, Barkle GF (2008) Comparison of three multiobjective optimization algorithms for inverse modeling of vadose zone hydraulic properties. Soil Sci Soc Am J 72(2):305–319CrossRefGoogle Scholar
  29. Wu RS, Liu JS, Chang SY, Hussain F (2017) Modeling of mixed crop field water demand and a smart irrigation system. Water 9(11):885CrossRefGoogle Scholar
  30. Xie X, Cui Y (2011) Removal development and test of SWAT for modeling hydrological processes in irrigation districts with paddy rice. J Hydrol 396:61–71CrossRefGoogle Scholar
  31. Zhang Q, Maeda S, Kawachi T (2007) Stochastic multiobjective optimization model for allocating irrigation water to paddy fields. Paddy Water Environ 5(2):93–99CrossRefGoogle Scholar

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.Department of Irrigation and Reclamation EngineeringUniversity of TehranTehranIran

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