Water resource management at Tabarkabad dam in Quchan city: using orthogonal polynomials to solve stochastic dynamic programming problems

Document Type : Original Article

Authors

Department of Agricultural Economics, Faculty of Agriculture, University of Zabol, Zabol, Iran

Abstract

Introduction:
The lack of efficient use of water as a production input has led to wastage of a significant amount of this input, which is financed at a great cost. Most provinces of the country have been facing water crisis for decades. Symptoms of this water crisis have been observed in some plains of Khorasan Province since the early 1970s, and this crisis has intensified in the last decade due to the lack of proper management of water resources. Therefore the present study, using a dynamic approach, studies the management of water resources in the Tabarkabad Dam in Quchan.
Materials and methods:
In this paper we put forward an easy-to-implement methodology for solving deterministic or stochastic dynamic programming problems within a standard optimization package such as GAMS. We found that the use of orthogonal polynomials was especially helpful in implementing approximation methods for the iterative computation of the infinite-horizon value function, due to their superior convergence properties over standard polynomials. This method is described using the case study of Tabarkabad Dam in Quchan city. For this purpose, data related to the Tabarkabad Dam were collected through the National Dams Information System for the years 2008-2016. Also, data relating to the estimation of the agricultural water demand function in Quchan city were obtained through a questionnaire prepared by the Ministry of Jihad-e-Agriculture.
Results and discussion:
Based on the results, comparing the actual and simulated values for the dam reservoir (state variable) and water release (control variable), it is determined that the simulations performed with orthogonal polynomial Chebyshev approximation were appropriate. Finally, based on the results, the netpresent value of water allocated to agriculture at Tabarkabad Dam in the studied period is 1471205 Rials and the allocation of water is equal to 24.745 million cubic meters per year.
Conclusion:
Considering the results obtained and the proper approximation of simulated values, we can use the proposed method of this study to solve stochastic dynamic programming problems, especially in the field of water resource management. Also, by using the annual allocation of water and taking account of other regional constraints, we can provide a suitable cropping pattern for the sustainable use of agricultural water for the coming years in the fields covered by the Tabarkabad Dam.

Keywords


  1. Anonymous, 2011. Ministry of Energy Release, Ministry of Energy publications, Iran. (in Persian). Available online at http://news.moe.gov.ir/
  2. Anonymous, 2012. Agricultural Jihad Report of Quchan City, General Directorate of Agriculture Jihad in Quchan, Iran. (in Persian). Available online at http://koaj.ir/RContent/1IE7D7A.
  3. Anonymous, 2013. Annual Report of Regional Water Company of Khorasan Razavi Province, Regional Water Company of Khorasan Razavi Province, Iran. (in Persian). Available online at http://www.khrw.ir/SC.php?type=static&id=157
  4. Anonymous, 2016. Iran Water Resources, Iran Water Resources Management Co, Iran. (in Persian). Available online at https://www.linkedin.com/pulse.
  5. Anyata, B.U., 2014. Application of dynamic programming in water resource management: A case of university of Benin water supply system. International Journal of Research in Engineering and Technology: 123-134.
  6. Bellman, R., 1961. “Adaptive Control Processes: A Guided Tour”, Princeton University Press, Princeton, USA.
  7. Bertsekas, D.P., 1976. “Dynamic Programming and Stochastic Control” Academic Press, New York, USA.
  8. Berbel, J., and Gomez-Limon, J. A., 2010. The impact of water- pricing policy in Spain: An analysis of three irrigated areas, Agricultural Water Management, 43: 22-41.
  9. Burt, O. and Allison, J.R., 1963. “Farm Management Decisions with Dynamic Programming,” Journal of Farm Economics, 45(1): 1-22.
  10. Chizari, A. and Keramatzadeh, A., 2006. Determining Economic Value of Water with Ideal Planning Approach (Case Study: Barzoo Shirvan Dam). Economic research, 71 (2): 39-66. (in Persian).
  11. Ertunga, C and hzelkan, G., 1997. Linear quadratic dynamic programming for water reservoir management. Systems and Industrial Engineering: 591-598.
  12. Gakpo, J. T., 2005. Application of stochastic dynamic programming (SDP) for the optimal allocation of irrigation water under capacity sharing arrangements. Agrekon, 44(4): 436-451.
  13. Ghahreman, B. and Sepaskhah, A., 2006. Management of dams' reservoirs. Iran Water Resources Research, 1 (2): 1-15. (in Persian).
  14. Howitt, R.E., Reynaud, S. Msangi, and K. Knapp., 2002. Calibrated Stochastic Dynamic Models For Resource Management . Working Paper, Department of Agricultural & Resource Economics, University of California, Davis.
  15. Huang, R. and Sargent, T.J., 2010. “Exercises in Dynamic Macroeconomic Theory,” Cambridge, Mass; Harvard University Press.
  16. Judd K L. 1998. “Numerical Methods in Economics,” M.I.T Press. Cambridge.
  17. KEANE, M.P. and WOLPIN, K.I., 1994. “The Solution and Estimation of Discrete Choice Dynamic Programming Models by Simulation and Interpolation: Monte Carlo Evidence,” The Review of Economics and Statistics, 76(Nov.): 648-672.
  18. Karbasi, A. and Rastegaripour, F., 2015. Optimal operation of Lar dam reservoir. Five-stage non-precision randomized design. Agricultural Economics Research, 6 (4): 21-37. (in Persian).
  19. Knapp, C.K. and Olson, L.J., 1995. “The Economics of Conjunctive Groundwater management with Stochastic Surface Supplies,” Journal of Environmental Economics and Management, 28(May), 340-356.
  20. Miranda, M.J. and Fackler P.L., 2009. “Hybrid methods for Continuous State Dynamic Programming” paper # 1332 in Computing in Economics and Finance ’99 from Society for Computational Economics
  21. Momeni, M. and Rezaei, N., 2009. Operation model from Aras Dam reservoir using dynamic programming. Industrial Management Journal, 1 (1): 139-152. (in Persian).
  22. Provencher, B., 1994. “Structural Estimation of the Stochastic Dynamic Decision Problems of Resource Users: An Application to the Timber Harvest Decision,” Journal of Environmental Economics and Management, 29(Nov): 321-338. 25
  23. Provencher, B. and Burt, O., 1994. “Approximating the optimal groundwater pumping policy in a multiaquifer stochastic conjunctive use setting,” Water Resources Research, 30(Mar): 833-843.
  24. Sastri, R.C., 2007. An Estimable Dynamic Model of recreation Behavior with an Application to Great Lakes Angling” Journal of Environmental Economics and Management, 33(2), 107-127.
  25. Rezaei, M. and Momeni, A., 2009. Operation model of Aras Dam reservoir using dynamic planning. Journal of Industrial Management, 1 (1): 139-152. (in Persian).
  26. Sabouhi, M., Soltani, Gh. And Zibaei, M., 2008. Evaluation of groundwater resources management practices: A case study of Narimani plain in Khorasan province. Water and Soil Science (Science and Technology of Agriculture and Natural Resources), 11 (1): 475-484. (in Persian).
  27. Sattari, M., Eslamian, S. and Abrishamchi, A., 2003. Optimization of water distribution in the multi-reservoir system of the Kilamarz Meyaneh basin. Independence, 21 (2): 197-201. (in Persian).
  28. Varaiya, P. and. Wets R.J.B., 2013. “Stochastic Dynamic Optimization: Approaches and Computation,” In Mathematical Programming: Recent Developments and Applications, (Iri & K. Tanabe, eds.), Kluwer Academic Publishers, pp.309-332.
  29. Velayati, A., 2013. Water Resources Management in Khorasan Province. Water Management, 6 (2): 25-36. (in Persian).
  30. Veinott, A. F., 2008. Lectures in Dynamic Programming and Stochastic Control. Department of Management Science and Engineering Stanford University Stanford, California 94305.
  31. Velazquez, P. H., 2016. Hydro-economic optimization under inflow uncertainty using the SDP_GAMS generalized optimization tool. Evolving Water Resources Systems: Understanding, Predicting and Managing. Water-Society Interactions: 410-416.