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Building a metamodel of an irrigation district distributed-parameter model

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  • Galelli, S.
  • Gandolfi, C.
  • Soncini-Sessa, R.
  • Agostani, D.

Abstract

Complex decision-making problems, related to planning and management of irrigation water resources, generally preclude the use of large, distributed-parameter models, which are then commonly substituted by lumped-parameter models. This paper, with the aim of improving the quality of these latter, introduces a new approach for their design. This approach is based on metamodelling, which proposes to identify a simple, lumped-parameter model on the basis of the data produced via simulation with a distributed-parameter model. The approach proposed is tested on a real-world case study, namely the identification of a metamodel describing the water demand of the Muzza-Bassa Lodigiana irrigation district (Italy). The metamodel, which inherits the physical description of the original distributed-parameter model, is sufficiently simple to permit the resolution of an optimal control problem, i.e. the design, via stochastic dynamic programming, of the release policy of Lake Como, serving the Muzza irrigation district.

Suggested Citation

  • Galelli, S. & Gandolfi, C. & Soncini-Sessa, R. & Agostani, D., 2010. "Building a metamodel of an irrigation district distributed-parameter model," Agricultural Water Management, Elsevier, vol. 97(2), pages 187-200, February.
    • Handle: RePEc:eee:agiwat:v:97:y:2010:i:2:p:187-200
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    References listed on IDEAS

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    Cited by:

    1. Nana, E. & Corbari, C. & Bocchiola, D., 2014. "A model for crop yield and water footprint assessment: Study of maize in the Po valley," Agricultural Systems, Elsevier, vol. 127(C), pages 139-149.
    2. Bocchiola, D. & Nana, E. & Soncini, A., 2013. "Impact of climate change scenarios on crop yield and water footprint of maize in the Po valley of Italy," Agricultural Water Management, Elsevier, vol. 116(C), pages 50-61.
    3. Ricart, Sandra & Gandolfi, Claudio, 2017. "Balancing irrigation multifunctionality based on key stakeholders’ attitudes: Lessons learned from the Muzza system, Italy," Land Use Policy, Elsevier, vol. 69(C), pages 461-473.
    4. Wu, Xin & Zheng, Yi & Wu, Bin & Tian, Yong & Han, Feng & Zheng, Chunmiao, 2016. "Optimizing conjunctive use of surface water and groundwater for irrigation to address human-nature water conflicts: A surrogate modeling approach," Agricultural Water Management, Elsevier, vol. 163(C), pages 380-392.
    5. Ricart Casadevall, Sandra, 2016. "Improving the management of water multi-functionality through stakeholder involvement in decision-making processes," Utilities Policy, Elsevier, vol. 43(PA), pages 71-81.
    6. Zhang, J.L. & Li, Y.P. & Wang, C.X. & Huang, G.H., 2015. "An inexact simulation-based stochastic optimization method for identifying effluent trading strategies of agricultural nonpoint sources," Agricultural Water Management, Elsevier, vol. 152(C), pages 72-90.
    7. Yang, Gaiqiang & Guo, Ping & Huo, Lijuan & Ren, Chongfeng, 2015. "Optimization of the irrigation water resources for Shijin irrigation district in north China," Agricultural Water Management, Elsevier, vol. 158(C), pages 82-98.
    8. Yang, Gaiqiang & Liu, Lei & Guo, Ping & Li, Mo, 2017. "A flexible decision support system for irrigation scheduling in an irrigation district in China," Agricultural Water Management, Elsevier, vol. 179(C), pages 378-389.

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