Developing an optimization model for conjunctive use of water resources and cultivation area allocation by game theory application

Subject Areas : Subjects

Abbas Sedghamiz ¹ (Assistant Prof. of Water Engineering, Darab College of Agricultural Sciences and Natural Resources, Shiraz University, Iran)
Mohammad Reza Nikoo ² (Associate Prof. of Civil Engineering, College of Civil Engineering, Shiraz University, Shiraz, Iran)
Manouchehr Heidarpour ³ (Professor of Water Engineering, College of Agriculture, Isfahan University of Technology, Isfahan, Iran)

Received: 2020-11-14 Accepted : 2021-10-26 Published : 2021-10-23

Keywords: Multiobjective optimization model, simulation model, conjunctive use, cooperative game model, Condorcet method,

Abstract :

In the presented study, a multiobjective optimization-simulation model was developed to al-locate optimal water, cultivation area and crop pattern to different agricultural sectors. The sectors are located at a region encompassed by three cities; Minoo dasht, Azad shahr and Gonbad kavoos, in Golestan province. The model is a Surface-Groundwater conjunctive use model in which Modflow-GMS model is used to simulate groundwater flow. In the optimiza-tion part, the objectives consist of the maximization of the equity, agricultural benefit and green water ratio. Also, the constraints are related to maximum drawdown in water table, minimum and maximum volume of the reservoir, budget equation, release and cultivation ar-ea. After running the model by multiobjective genetic algorithm method, a pareto solution curve including 63 solution points obtained. The most preferable solution is selected by Con-dorcet method. Then different cooperative game theory method are applied to reallocate the benefit due to forming the grand coalition among the sectors. The results show that the amount of allocated water is a function of the summer crops area. Also it is shown that the most benefit is obtained for Gonbad kavoos, while the most benefit per cultivation area is for Minoo dasht. After that, the grand coalition forming causes to increase benefit up to 14864×109 Rial (20 percent growth). To reallocate benefit among the regions, different coop-erative game methods are applied. The results show that the most reallocated benefit for Minoo dasht, Azad shahr and Gonbad kavoos are obtained from normal nucleolus, nucleolus and proportional nucleolus, respectively.

References:

1. Alizadeh, M. R., Nikoo, M. R. and Rakhshandehroo, G. R. 2017. Developing a Multi-Objective Conflict-Resolution Model for Optimal Groundwater Management Based on Fallback Bargaining Models and Social Choice Rules: a Case Study. Water Resources Management, 31(5): 1457–1472.


2. Cañón, J., González, J., and Valdés, J. 2009. Reservoir Operation and Water Allocation to Mitigate Drought Effects in Crops: A Multilevel Optimization Using the Drought Frequency Index. Journal of Water Resources Planning and Management, 135(6): 458–465.


3. Cid-Garcia, N. M., Bravo-Lozano, A. G., and Rios-Solis, Y. A. 2014. A crop planning and real-time irrigation method based on site-specific management zones and linear programming. Computers and Electronics in Agriculture, 107: 20–28.


4. Dinar, A., and Howitt, R. E. 1997. Mechanisms for Allocation of Environmental Control Cost: Empirical Tests of Acceptability and Stability. Journal of Environmental Management, 49(2): 183–203.


5. Fallah-Mehdipour, E. Bozorg Hadadad, O., Alimohammadi, S., and Loáiciga, Hugo A. 2015. Development of Real-Time Conjunctive Use Operation Rules for Aquifer-Reservoir Systems. Water Resources Management, 29(6): 1887–1906.


6. Galán-Martín, Á., Vaskan, P., Antón, A., Esteller, L. J., and Guillén-Gosálbez, G.   Multi-objective optimization of rainfed and irrigated agricultural areas considering production and environmental criteria: a case study of wheat production in Spain. Journal of Cleaner Production, 140: 816–830.


7. Ganji, A., Karamouz, M. and Khalili, D. 2007. Development of stochastic dynamic Nash game model for reservoir operation II. The value of players; information availability and cooperative behaviors. Advances in Water Resources, 30(1): 157–168.


8. 1388. Report of water resource conjunctive use in Garesoo-Gorganrood basins, Vol 1: Analyzing data and water budjet, Part 3: Groundwater [In Persian]. Tehran.


9. Loáiciga, H. A. 2004. Analytic game - Theoretic approach to ground-water extraction. Journal of Hydrology, 297(1–4): 22–33.


10. Madani, K. 2010. Game theory and water resources. Journal of Hydrology, 381(3–4): 225–238.


11. Mahjouri, N., and Ardestani, M. 2010. A game theoretic approach for interbasin water resources allocation considering the water quality issues. Environmental Monitoring and Assessment, 167(1–4): 527–544.


12. Mattiuzi, C. D. P., Marques, G. F., and Medellín-Azuara, J. 2019. Reassessing Water Allocation Strategies and Conjunctive Use to Reduce Water Scarcity and Scarcity Costs for Irrigated Agriculture in Southern Brazil. Water, 11(6): 1140.


13. Nadrah, N., Tukimat, A. and Harun, S. 2014. Optimization of Water Supply Reservoir in the Framework of Climate Variation.  International Journal of Software Engineering and Its Applications, 8(3): 361–378.


14. Nikoo, M. R., Varjavand, I., Kerachian, R., Pirooz, M., and Karimi, A. 2014. Multi-objective optimumdesign of double-layer perforated-wall breakwaters: Application of NSGA-II and bargaining models. Applied Ocean Research, 47: 47–52.


15. Raquel, S., Ferenc, S., Emery, C., and Abraham, R. 2007. Application of game theory for a groundwater conflict in Mexico. Journal of Environmental Management, 84(4): 560–571.


16. Sedghamiz, A., Heidarpour, M., Nikoo, M. R., and Eslamian, S. 2018. A Game Theory Approach for Conjunctive Use Optimization Model Based on Virtual Water Concept. Civil Engineering Journal, 4(6): 1315-1325.


17. Sedghamiz, A., Nikoo, M. R., Heidarpour, M., and Sadegh, M. 2018. Developing a non-cooperative optimization model for water and crop area allocation based on leader-follower game. Journal of Hydrology, 567: 51–59.


18. Sedghamiz, A. 2018. Developing an Agent-Based Model and the Application of Game Theory for Optimal Water Conjunctive Allocation in Rivers Basin [In Persian]. Isfahan University of Technology.


19. Sepahvand, R., Safavi, H. R. and Rezaei, F. 2019. Multi-Objective Planning for Conjunctive Use of Surface and Ground Water Resources Using Genetic Programming. Water Resources Management, 33(6): 2123–2137.


20. Sheikhmohammady, M. and Madani, K. 2008. Bargaining over the Caspian Sea — The Largest Lake on the Earth. World Environmental and Water Resources Congress Ahupua’a - Proceedings of the World Environmental and Water Resources Congress, 316:1–9.


21. Song, J. Yang, Y., Sun, X., and Lin, J. 2020. Basin-scale multi-objective simulation-optimization modeling for conjunctive use of surface water and groundwater in northwest China. Earth Syst. Sci., 24: 2323–2341.


22. Srinivasa Prasad, A., Umamahesh, N. V., and Viswanath, G. K. 2011. Optimal irrigation planning model for an existing storage based irrigation system in India. Irrigation and Drainage Systems, 25(1): 19–38.


23. Wang, L., Fang, L., and Hipel, K. W. 2008. Basin-wide cooperative water resources allocation. European Journal of Operational Research, 190(3): 798–817.


24. Young, H. P., Okada, N., and Hashimoto, T. 1982. Cost allocation in water resources development. Water Resources Research, 18(3): 463–475.


25. Zeinali, M., Azari, A., and Heidari, M. M. 2020. Multiobjective Optimization for Water Resource Management in Low-Flow Areas Based on a Coupled Surface Water–Groundwater Model. Journal of Water Resources Planning and Management, 146(5).