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A Modified Constrained State Formulation of Stochastic Soil Moisture for Crop Water Allocation

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  • A. Ganji
  • M. Shekarriz fard

Abstract

In response to uncertainty in crop water allocation, several methodologies have been proposed in the literature, most of them considering rainfall as a stochastic variable affecting soil moisture. A methodology considering uncertainties both in irrigation depth and soil moisture is more realistic for irrigated crops as developed here using an explicit stochastic optimization model. This new work is based on an earlier constrained state formulation which did not consider the irrigation depth as stochastic. In constrained state formulation methods, the first and second moments of state variables are developed considering the uncertainties which are then used as constraints in an optimization model. In contrast to alternative methods that are dynamic programming-based, the proposed optimization method can be solved using standard nonlinear optimization tools. Performance of the proposed model is evaluated for the case of two different crops, winter wheat and barley. Model verification is performed by comparing the results with simulation results. The model is quite acceptable and shows considerable improvement over analogous models. Copyright Springer Science+Business Media B.V. 2010

Suggested Citation

  • A. Ganji & M. Shekarriz fard, 2010. "A Modified Constrained State Formulation of Stochastic Soil Moisture for Crop Water Allocation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 24(3), pages 547-561, February.
    • Handle: RePEc:spr:waterr:v:24:y:2010:i:3:p:547-561
    DOI: 10.1007/s11269-009-9458-6
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    References listed on IDEAS

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    1. P. Guo & G. Huang & L. He & H. Zhu, 2009. "Interval-parameter Two-stage Stochastic Semi-infinite Programming: Application to Water Resources Management under Uncertainty," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 23(5), pages 1001-1023, March.
    2. A. Ganji & D. Khalili & M. Karamouz & K. Ponnambalam & M. Javan, 2008. "A Fuzzy Stochastic Dynamic Nash Game Analysis of Policies for Managing Water Allocation in a Reservoir System," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 22(1), pages 51-66, January.
    3. Tsakiris, G. P., 1982. "A method for applying crop sensitivity factors in irrigation scheduling," Agricultural Water Management, Elsevier, vol. 5(4), pages 335-343, December.
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    Cited by:

    1. Ghazali, Mahboubeh & Honar, Tooraj & Nikoo, Mohammad Reza, 2018. "A hybrid TOPSIS-agent-based framework for reducing the water demand requested by stakeholders with considering the agents’ characteristics and optimization of cropping pattern," Agricultural Water Management, Elsevier, vol. 199(C), pages 71-85.
    2. Mehran Homayounfar & Sai Lai & Mehdi Zomorodian & Ali Sepaskhah & Arman Ganji, 2014. "Optimal Crop Water Allocation in Case of Drought Occurrence, Imposing Deficit Irrigation with Proportional Cutback Constraint," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(10), pages 3207-3225, August.
    3. Lu Zhuo & Dawei Han & Qiang Dai & Tanvir Islam & Prashant Srivastava, 2015. "Appraisal of NLDAS-2 Multi-Model Simulated Soil Moistures for Hydrological Modelling," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3503-3517, August.

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