Optimization of Land and Irrigation Water Allocation Based on Water Balance Approach Using Linear Programming

آب و خاک

Article 4, Volume 31, Issue 2 - Serial Number 52, May 2017, Pages 372-385   

Document Type: Research Article

DOI: 10.22067/jsw.v31i2.52166

Authors

Hamid Reza Moradi^{* 1}; khalil Jalili¹; Omid Bozorg Hadad²

¹Tarbiat Modares University

²University of Tehran

Abstract

Introduction: The conflict between environmental protection and the economic development by different land uses within a watershed are challenges facing land use planners in many developing countries. Because of the growing demand for water, water resources optimization allocation management is at the forefront in formulating sustainable development policies for many countries. Conjunctive use of surface water and groundwater is being practiced in many regions of the world to bring more areas under irrigation, increase agricultural production and productivity, and also maintain overall system balance. Successful agricultural water management policies put the physical, hydro-geological, and socio-economic constraints on these integrated water supplies. Application of optimization approaches has been started since the human faced a low efficiency production of the system. Optimization of resource allocation is one of the proper strategies to achieve sustainable development and to reduce resource dissipation. So land and irrigation water allocation based on water balance approach is the aim of this research and this paper proposed an optimal land and water resource allocation model based on linear programming to Islamabad plain’s irrigation areas.
Materials and Methods: The Islamabad plain aquifer is located in Seymareh watershed and 55 km of Kermanshah city in the Kermanshah Province; it comprises 19438 ha and extends between 33◦20 to 34◦24 N latitude and 46◦ 15to 46◦47 E longitude. Annual precipitation and annual temperature of study area are 445.1 mm and 12 ◦C respectively . The mean net benefit of irrigated wheat, sugar beet, corn, potato, irrigated chick-pea, alfalfa, vegetables, melon, tomato, fruit garden, dry wheat, dry barley, dry chick-pea and dry lentil were therefore calculated to be respectively some 38.21, 76.7, 34.39, 81.0, 16.98, 21.69, 47.2, 12.4, 61.4, 74.0, 7.26, 0.72, 17.1 and 10.9 Mir/ha and the objective functions of the benefit maximization problem in the Islamabad aquifer was formulated The problem was structuredin the study area to maximize economic return. The information and data required for defining constants and coefficients of objective Function and constraints, viz. Land availability, water availability/supply, present crop pattern, socio-economic conditions were extracted from the available comprehensive Hydrogeology, field studies and farmers viewpoints. A linear optimization problem has been formulated for the Islamabad plain to achieve sustainable development and optimal land allocation to crop pattern, then solved using the simplex method with the help of LINGO software packages and the optimal solution was ultimately determined. Three management scenarios and six action plan with resources accessibility, crop rotation, socio-economic constraints and nonnegative variables have analyzed and sensitivity analysis was done.
Results and Discussion: The results of the study verified that the linear optimization problem was successfully solved using the LINGO software program and the results led to maximize benefits in the Islamabad plain. The results also showed the successful linkage between economic aspects and environmental outcomes at an aquifer scale. Results show that in all scenarios sugar beet, corn, chick-pea, tomato and melon have been removed from the optimal cropping pattern. Wheat areas in two scenarios and five action plans have been increased. Benefit of optimization in management scenarios and in the entire optimal crop pattern was positive and increase from 19 to 55 percent. Sensitivity analysis showed that the change of some specific allocations would create much more impact on the final optimal solutions generated by the optimization programming.The results of sensitivity analyses also showed that the objective function was strongly susceptible to the constraint of water availability and total area of plain.
Conclusions: A benefit problem was formulated and then solved to maximize benefits using optimization of allocable land and irrigation water resources to 14 productions of present crop pattern within the Islamabad plain in Kermanshah province. The LINGO optimization software program was successfully applied and led to determine appropriate areas allotted to different crop. The results obtained during the study approved the applicability of optimization model in solving problems which sometimes conflicting each other. On the study plain there appears a significant augmentation in profit from allocating the optimal cultivated areas. The approach could provide better information on where changes are required, how large the changes need to be, and how much the changes will benefit the people when improving. The conjunction of optimization techniques with other tools like geographical information system, genetic algorithm, fuzzy logic, artificial neuron networks and applying different softwares and simulation techniques are also suggested to be taken into account in further studies to draw ultimate necessary conclusions.

Keywords

Crop pattern; LINGO Software; Simplex Method; Sustainable development; West Islamabad

References

1- Alcala F.J., Martinez-Valderrama J., Robles-Marin P., Guerrera F., Martin-Martin M., Raffaelli G., and Asebriy L. 2015. A hydrological–economic model for sustainable groundwater use in sparse-data drylands: Application to the Amtoudi Oasis in southern Morocco, northern Sahara. Science of The Total Environment. 537:309–322.

2- Almaraz S.D.L., Marianne B., Catherine A.P., Ludovic M., and Serge D. 2015. Design of a multi-contaminant water allocation network using multi-objective optimization. Computer Aided Chemical Engineering. 37: 911–916.

3- Bozorg Haddad O., and Marino M.A. 2010. Optimum operation of wells in coastal aquifers. Proceedings of the ICE - Water Management. 164(3):135 –146.

4- Das B., Singh A., Panda S.N., and Yasuda H. 2015. Optimal land and water resources allocation policies for sustainable irrigated agriculture. Land Use Policy. 42: 527-537.

5- Divakar L., Babel M.S., Perret S., Das G.A. 2013. Optimal water allocation model based on satisfaction and economic benefits. International Journal of Water. 7(4):363-381.

6- Fallah-Mehdipour E., Bozorg Haddad O., and Marino M.A. 2013. Prediction and simulation of monthly groundwater levels by genetic programming. Journal of Hydro-Environment Research. 7: 253-260.

7- Ganji Khorramdel N., Mohammadi K., and Monem M.J. 2008. Optimization of observation well network for the estimation of groundwater balance using double water table fluctuation method. Journal of Soil and Water. 22(2): 358-370.

8- Hassan-Esfahani L., Torres-Rua A., and McKee M. 2015. Assessment of optimal irrigation water allocation for pressurized irrigation system using water balance approach, learning machines, and remotely sensed data. Agricultural Water Management. 153: 42–50.

9- Jalili Kh., Sadeghi S.H.R., and Nikkami D. 2006. Land use optimization of watershed for soil erosion minimization using linear programming. Journal of Science & Technology of Agriculture and Natural Resources. 10(4): 15-26.

10- Jalili Kh., Moradi H.R., and Bozorg Haddad O. 2015. Analysis of aquifer water level drawdown in Islamabad due to the allocation of groundwater resources. p. 499–509. In S.M. Tajbakhsh Fakhrabadi (ed.) Proceedings of Water Harvesting and Watershed Management Congress. 18-19 Feb. 2015. Birjand University, Birjand, Iran.

11- Li M., and Guo P. 2014. A multi-objective optimal allocation model for irrigation water resources under multiple uncertainties. Applied Mathematical Modelling. 38(19–20): 4897–4911.

12- Mahdi-Moradi J., BozorgHaddad O., Karney M.A., and Marino B.W. 2007. Reservoir operation in assigning optimal multi-crop irrigation areas. Agricultural Water Management. 90: 149-19.

13- Mohammadrezapour Tabari M., Ebadi T., and Maknoon R. 2010. Development of a smart model for groundwater level prediction based on aquifer dynamic conditions. Journal of Water and Waste Water. 4: 70-80.

14- Molina J.L., Bromley J., Grcia-Arostegui J.L., Sullivan C., and Benavete J. 2010. Integrated water resources management of overexploited hydrological system using objected-oriented bayesian networks. Environmental Modeling & Software. 25(4): 383-397.

15- Nikkami D. 1999. Optimizing the management of soil erosion using GIS, Ph.D dissertation. Concordia University.108 pp.

16- Roozbahani R., Schreider S., and Abbasi B. 2015. Optimal water allocation through a multi-objective compromise between environmental, social, and economic preferences. Environmental Modelling & Software. 64:18–30.

17- Sadeghi S.H.R., Jalili Kh., and Nikkami D. 2009. Land use optimization in watershed scale. Land Use Policy. 26(2009)186-193.

18- Sefelnasr A., Gossel W., and Wycisk P. 2015. Groundwater management options in an arid environment: The Nubian Sandstone Aquifer System, Eastern Sahara. Journal of Arid Environments. 122:46-58.

19- Vedula S., Mujumdar, P.P., and Sekhar G.C. 2004. Conjunctive use modeling for multicrop irrigation. Journal of Agricultural Water Management. 73: 193-221.