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Stochastic vs deterministic programming in water management: the value of flexibility

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  • Yousaf Muhammad
  • Georg Pflug

Abstract

In the paper we develop a two stage scenario-based stochastic programming model for water management in the Indus Basin Irrigation System (IBIS). We present a comparison between the deterministic and scenario-based stochastic programming model. Our model takes stochastic inputs on hydrologic data i.e. inflow and rainfall. We divide the basin into three rainfall zones which overlap on 44 canal commands. Data on crop characteristics are taken on canal command levels. We then use ten-daily and monthly time intervals to analyze the policies. This system has two major reservoirs and a complex network of rivers, canal head works, canals, sub canals and distributaries. All the decisions on hydrologic aspects are governed by irrigation and agricultural development policies. Storage levels are maintained within the minimum and maximum bounds for every time interval according to a power generation policy. The objective function is to maximize the expected revenue from crops production. We discuss the flexibility of two stochastic optimization models with varying time horizon. Copyright Springer Science+Business Media New York 2014

Suggested Citation

  • Yousaf Muhammad & Georg Pflug, 2014. "Stochastic vs deterministic programming in water management: the value of flexibility," Annals of Operations Research, Springer, vol. 223(1), pages 309-328, December.
    • Handle: RePEc:spr:annopr:v:223:y:2014:i:1:p:309-328:10.1007/s10479-013-1455-8
    DOI: 10.1007/s10479-013-1455-8
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    References listed on IDEAS

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    1. Rao, N. H. & Sarma, P. B. S. & Chander, Subhash, 1992. "Real-time adaptive irrigation scheduling under a limited water supply," Agricultural Water Management, Elsevier, vol. 20(4), pages 267-279, February.
    2. Ullah, M. K., 2001. "Spatial distribution of reference and potential evapotranspiration across the Indus Basin Irrigation Systems," IWMI Working Papers H029426, International Water Management Institute.
    3. Oscar R. Burt & M. S. Stauber, 1971. "Economic Analysis of Irrigation in Subhumid Climate," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 53(1), pages 33-46.
    4. N. Umamahesh & P. Sreenivasulu, 1997. "Technical Communication: Two-Phase Stochastic Dynamic Programming Model for Optimal Operation of Irrigation Reservoir," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 11(5), pages 395-406, October.
    5. George B. Dantzig, 1955. "Linear Programming under Uncertainty," Management Science, INFORMS, vol. 1(3-4), pages 197-206, 04-07.
    6. Ronald Hochreiter & Georg Pflug, 2007. "Financial scenario generation for stochastic multi-stage decision processes as facility location problems," Annals of Operations Research, Springer, vol. 152(1), pages 257-272, July.
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    1. Reza Roozbahani & Babak Abbasi & Sergei Schreider & Zahra Hosseinifard, 2020. "A basin-wide approach for water allocation and dams location-allocation," Annals of Operations Research, Springer, vol. 287(1), pages 323-349, April.

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