An Integrated Framework for Optimal Irrigation Planning Under Uncertainty: Application of Soil, Water, Atmosphere and Plant Modeling

Abstract

In this paper, an innovative framework is developed for simulating the water distribution in agricultural lands considering existing constraints related to soil, water, atmosphere and plant. Some nonlinear operating rules are formulated for the irrigation planning and groundwater management in Shahrekord plain in Iran. Evapotranspiration values are estimated based on a real-time modeling. Groundwater exploitations are limited for each irrigated area by considering its actual water requirement and soil moisture balance with daily time steps at the root zone. Moreover, this work introduces an approach for taking into account the uncertainty of available water. For this purpose, the membership functions of fuzzy inputs are discretized into five levels and then a multiobjective optimization model is developed to find the extreme values of economic efficiency of irrigation water for different levels. The results show that under limited water conditions, the economic productivity could be further improved when water, soil, atmosphere and crop relationships are simultaneously considered. In the proposed cropping pattern, the net annual return was increased by more than 43% comparing to the existing cropping pattern. Furthermore, different efficiency criteria for crops with higher values of yield production (e.g., potato, maize, sugar beet and alfalfa) are more affected by the existing uncertainties.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

References

  1. Abbasi N, Bahramloo R, Movahedan M (2015) Strategic planning for remediation and optimization of irrigation and drainage networks: a case study for Iran. Agric Agric Sci Procedia 4:211–221

    Google Scholar 

  2. Akbari M, Toomanian N, Droogers P, Bastiaanssen W, Gieske A (2007) Monitoring irrigation performance in Esfahan, Iran, using NOAA satellite imagery. Agri Water Manag 88:99–109

    Article  Google Scholar 

  3. Alvarez JFO, Valero JAJ, Benito JMT, Mata EL (2004) MOPECO: an economic optimization model for irrigation water management. Irrigation Sci. 23:61–75

    Article  Google Scholar 

  4. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Commun 6:181–197

    Google Scholar 

  5. Fakharinia M, Lalehzari R, Yaghoobzadeh M (2012) The use of subsurface barriers in the sustainable management of groundwater resources. World Appl Sci J 19(11):1585–1590

    Google Scholar 

  6. Fallah-Mehdipour E, Bozorg Haddad O, Marino MA (2013) Extraction of multi-crop planning rules in a reservoir system: application of evolutionary algorithms. J Irrigation Drain Eng. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000572

    Article  Google Scholar 

  7. Grafton QR, Hussey K (2011) Water resources planning and management. Cambridge University Press, New York

    Google Scholar 

  8. Haghighi A, Zahedi-Asl A (2014) Uncertainty analysis of water supply networks using the fuzzy set theory and NSGA-II. Eng Appl Artif Int 32:270–282

    Article  Google Scholar 

  9. Hsiao TC, Steduto P, Fereres E (2007) A systematic and quantitative approach to improve water use efficiency in agriculture. Irrigation Sci 25:209–231

    Article  Google Scholar 

  10. Huang J, Ridoutt BG, Chang X, Zheng H, Chen F (2012) Cropping pattern modifications change water resource demands in the Beijing metropolitan area. J Integr Agric 11(11):1914–1923

    Article  Google Scholar 

  11. Jakeman AJ, Barreteau O, Hunt BJ, Rinaudo JD, Ross N (2016) Integrated groundwater management. Springer, New York, p 756

    Google Scholar 

  12. Karamouz M, Kerachian R, Zahraie B (2004) Monthly water resources and irrigation planning: case study of conjunctive use of surface and groundwater resources. J Irrig Drain Eng 130(5):391–402

    Article  Google Scholar 

  13. Karamouz M, Rezapour-Tabari MM, Kerachian R (2007) Application of genetic algorithm and artificial neural networks in conjunctive use of surface and groundwater resources. J Water Int 32(1):163–176

    Article  Google Scholar 

  14. Karamouz M, Zahraie B, Kerachian R, Eslami A (2010) Crop pattern and conjunctive use management: a case study. Irrig Drain 59(2):161–173

    Google Scholar 

  15. Lalehzari R (2017) Closure to “Multi-objective management of water allocation to sustainable irrigation planning and optimal cropping pattern”. J Irrigation Drain Eng ASCE. https://doi.org/10.1061/(ASCE)IR.1943-4774.0001144

    Article  Google Scholar 

  16. Lalehzari R, Boroomand-Nasab S (2017) Improved volume balance using upstream flow depth for advance time estimation. Agric Water Manag 186:120–126

    Article  Google Scholar 

  17. Lalehzari R, Kerachian R (2020) Developing a framework for daily common pool groundwater allocation to demands in agricultural regions. Agric Water Manag 241:106278

    Article  Google Scholar 

  18. Lalehzari R, Tabatabaei SH (2015) Simulating the impact of subsurface dam construction on the change of nitrate distribution. Environ Earth Sci 74:3241–3249

    Article  Google Scholar 

  19. Lalehzari R, Tabatabaei SH, Kholghi M (2013) Simulation of nitrate transport and wastewater seepage in groundwater flow system. Int J Environ Sci Technol 10:1367–1376

    Article  Google Scholar 

  20. Lalehzari R, Tabatabaei SH, Kholghi M, Yarali N, Saba AA (2014) Evaluation of Scenarios in artificial recharge with treated wastewater on the quantity and quality of Shahrekord aquifer. J Environ Stud 40(1):52–55

    Google Scholar 

  21. Lalehzari R, Ansari Samani F, Boroomand-Nasab S (2015) Analysis of evaluation indicators for furrow irrigation using opportunity time. Irrigation Drain 64(1):85–92

    Article  Google Scholar 

  22. Lalehzari R, Boroomand-Nasab S, Moazed H, Haghighi A (2016) Multi-objective management of water allocation to sustainable irrigation planning and optimal cropping pattern. J Irrigation Drain Eng. https://doi.org/10.1061/(ASCE)IR.1943-4774.0000933

    Article  Google Scholar 

  23. Li X, Zhang J, Liu J, Liu J, Zhu A, Lv F, Zhang C (2011) A modified checkbook irrigation method based on GIS-coupled model for regional irrigation scheduling. Irrigation Sci 29:115–126

    Article  Google Scholar 

  24. Lorite IJ, Mateos L, Orgaz F, Fereres E (2007) Assessing deficit irrigation strategies at the level of an irrigation district. Agric Water Manag 91:51–60

    Article  Google Scholar 

  25. Montazar A (2013) A decision tool for optimal irrigated crop planning and water resources sustainability. J Glob Optim 55:641–654

    Article  Google Scholar 

  26. Nikoo MR, Kerachian R, Karimi A, Azadnia AA (2013) Optimal water and waste-load allocations in rivers using a fuzzy transformation technique: a case study. Environ Monit Assess 185(3):2483–2502

    Article  Google Scholar 

  27. Noory H, Liaghat AM, Parsinejad M, Bozorg Haddad O (2012) Optimizing irrigation water allocation and multicrop planning using discrete PSO algorithm. J Irrigation Drain Eng ASCE 138(5):437–444

    Article  Google Scholar 

  28. Parsinejad M, Bemani-Yazdi A, Araghinejad S, Nejadhashemi AP, Sarai Tabrizi M (2013) Optimal water allocation in irrigation networks based on real time climatic data. Agric Water Manag. 117:1–8

    Article  Google Scholar 

  29. Shi Y, Eberhart R (1999) Empirical study of particle swarm optimization. In: Proceeding IEEE international congers evolutionary computation, Washington, DC, USA, pp 1945–1950

  30. Singh A (2015) Land and water management planning for increasing farm income in irrigated dry areas. Land Use Pol 42:244–250

    Article  Google Scholar 

  31. Singh A, Panda SN (2012) Development and application of an optimization model for the maximization of net agricultural return. Agric Water Manag 115:267–275

    Article  Google Scholar 

  32. Soltani M, Kerachian R, Nikoo MR, Noory H (2016) A conditional value at risk-based model for planning agricultural water and return flow allocation in river systems. Water Resour Manage 30(1):427–443

    Article  Google Scholar 

  33. Tabatabaei SH, Lalehzari R, Nourmahnad N, Khazaei M (2010) Groundwater quality and land use change (a case study: Shahrekord aquifer, Iran). J Res Agric Sci 6:39–48

    Google Scholar 

  34. Turner K, Georgiou S, Clark R, Brouwer R, Burke J (2004) Economic valuation of water resources in agriculture. FAO water reports, p 204

  35. Varade S, Patel JN (2018) Determination of optimum cropping pattern using advanced optimization algorithms. J Hydrol Eng 23(6):05018010

    Article  Google Scholar 

  36. Vedula S, Mujumdar PP, Sekhar GC (2005) Conjunctive use modeling for multicrop irrigation. Agric Water Manag 73:193–221

    Article  Google Scholar 

  37. Veysi S, Naseri AA, Hamzeh S, Bartholomeus H (2017) A satellite based crop water stress index for irrigation scheduling in sugarcane fields. Agric Water Manag 189:70–86

    Article  Google Scholar 

Download references

Acknowledgements

This research has been supported by Iran National Science Foundation (INSF) under Grant Number 95000151.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Reza Lalehzari.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lalehzari, R., Kerachian, R. An Integrated Framework for Optimal Irrigation Planning Under Uncertainty: Application of Soil, Water, Atmosphere and Plant Modeling. Iran J Sci Technol Trans Civ Eng 45, 429–442 (2021). https://doi.org/10.1007/s40996-020-00442-5

Download citation

Keywords

  • Uncertainty analysis
  • Cropping pattern
  • Water use efficiency
  • Fuzzy set theory
  • Shahrekord plain