IDEAS home Printed from https://ideas.repec.org/a/eee/agiwat/v97y2010i2p187-200.html
   My bibliography  Save this article

Building a metamodel of an irrigation district distributed-parameter model

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378-3774(09)00274-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Quirin Schiermeier, 2008. "Water: A long dry summer," Nature, Nature, vol. 452(7185), pages 270-273, March.
    2. Kleijnen, Jack P. C. & Sargent, Robert G., 2000. "A methodology for fitting and validating metamodels in simulation," European Journal of Operational Research, Elsevier, vol. 120(1), pages 14-29, January.
    3. Shangguan, Zhouping & Shao, Mingan & Horton, Robert & Lei, Tingwu & Qin, Lin & Ma, Jianqing, 2002. "A model for regional optimal allocation of irrigation water resources under deficit irrigation and its applications," Agricultural Water Management, Elsevier, vol. 52(2), pages 139-154, January.
    4. Aziz Bouzaher & Richard Cabe & Alicia L. Carriquiry & Philip W. Gassman & P. G. Lakshminarayan & Jason F. Shogren, 1992. "Metamodels and Nonpoint Pollution Policy in Agriculture," Center for Agricultural and Rural Development (CARD) Publications 92-wp97, Center for Agricultural and Rural Development (CARD) at Iowa State University.
    5. Singh, R. & Subramanian, K. & Refsgaard, J. C., 1999. "Hydrological modelling of a small watershed using MIKE SHE for irrigation planning," Agricultural Water Management, Elsevier, vol. 41(3), pages 149-166, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Graham, Tennille, 2005. "On the Road to Better Management: An investigation into the benefits of managing the impacts of dryland salinity on roads," 2005 Conference (49th), February 9-11, 2005, Coff's Harbour, Australia 137921, Australian Agricultural and Resource Economics Society.
    2. Eli Feinerman & Meira Falkovitz, 1997. "Optimal Scheduling of Nitrogen Fertilization and Irrigation," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 11(2), pages 101-117, April.
    3. Katarzyna Growiec & Jakub Growiec & Bogumil Kaminski, 2017. "Social Network Structure and The Trade-Off Between Social Utility and Economic Performance," KAE Working Papers 2017-026, Warsaw School of Economics, Collegium of Economic Analysis.
    4. Kleijnen, Jack P. C., 2005. "An overview of the design and analysis of simulation experiments for sensitivity analysis," European Journal of Operational Research, Elsevier, vol. 164(2), pages 287-300, July.
    5. Acharki, Naoufal & Bertoncello, Antoine & Garnier, Josselin, 2023. "Robust prediction interval estimation for Gaussian processes by cross-validation method," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    6. JunJie Wu & Bruce Babcock, 2001. "Spatial Heterogeneity and the Choice of Instruments to Control Nonpoint Pollution," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 18(2), pages 173-192, February.
    7. Parkes, Martin & Jian, Wang & Knowles, Rupert, 2005. "Peak crop coefficient values for Shaanxi, North-west China," Agricultural Water Management, Elsevier, vol. 73(2), pages 149-168, May.
    8. Leung, Brian & Finnoff, David & Shogren, Jason F. & Lodge, David, 2005. "Managing invasive species: Rules of thumb for rapid assessment," Ecological Economics, Elsevier, vol. 55(1), pages 24-36, October.
    9. Giorgio Fagiolo & Mattia Guerini & Francesco Lamperti & Alessio Moneta & Andrea Roventini, 2017. "Validation of Agent-Based Models in Economics and Finance," LEM Papers Series 2017/23, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    10. Weibin Zhang & Xiaochun Zha & Jiaxing Li & Wei Liang & Yugai Ma & Dongmei Fan & Sha Li, 2014. "Spatiotemporal Change of Blue Water and Green Water Resources in the Headwater of Yellow River Basin, China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 28(13), pages 4715-4732, October.
    11. H. Christopher Frey & Sumeet R. Patil, 2002. "Identification and Review of Sensitivity Analysis Methods," Risk Analysis, John Wiley & Sons, vol. 22(3), pages 553-578, June.
    12. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    13. Balali, Hamid & Khalilian, Sadegh & Viaggi, Davide & Bartolini, Fabio & Ahmadian, Majid, 2011. "Groundwater balance and conservation under different water pricing and agricultural policy scenarios: A case study of the Hamadan-Bahar plain," Ecological Economics, Elsevier, vol. 70(5), pages 863-872, March.
    14. Tunali, S. & Batmaz, I., 2003. "A metamodeling methodology involving both qualitative and quantitative input factors," European Journal of Operational Research, Elsevier, vol. 150(2), pages 437-450, October.
    15. Phong Nguyen Thanh & Thinh Le Van & Tuan Tran Minh & Tuyen Huynh Ngoc & Worapong Lohpaisankrit & Quoc Bao Pham & Alexandre S. Gagnon & Proloy Deb & Nhat Truong Pham & Duong Tran Anh & Vuong Nguyen Din, 2023. "Adapting to Climate-Change-Induced Drought Stress to Improve Water Management in Southeast Vietnam," Sustainability, MDPI, vol. 15(11), pages 1-27, June.
    16. Jack P. C. Kleijnen & Susan M. Sanchez & Thomas W. Lucas & Thomas M. Cioppa, 2005. "State-of-the-Art Review: A User’s Guide to the Brave New World of Designing Simulation Experiments," INFORMS Journal on Computing, INFORMS, vol. 17(3), pages 263-289, August.
    17. Andrew J. Plantinga & JunJie Wu, 2003. "Co-Benefits from Carbon Sequestration in Forests: Evaluating Reductions in Agricultural Externalities from an Afforestation Policy in Wisconsin," Land Economics, University of Wisconsin Press, vol. 79(1), pages 74-85.
    18. Lakshminarayan, P. G., 1993. "Tradeoffs in balancing multiple objectives of an integrated agricultural economic and environmental system," ISU General Staff Papers 1993010108000011833, Iowa State University, Department of Economics.
    19. Jin, Ding & Thube, Sneha Dattatraya & Hedtrich, Johannes & Henning, Christian & Delzeit, Ruth, 2019. "A Baseline Calibration Procedure for CGE models: An Application for DART," Conference papers 333057, Purdue University, Center for Global Trade Analysis, Global Trade Analysis Project.
    20. Robertson, Joseph J. & Polly, Ben J. & Collis, Jon M., 2015. "Reduced-order modeling and simulated annealing optimization for efficient residential building utility bill calibration," Applied Energy, Elsevier, vol. 148(C), pages 169-177.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:agiwat:v:97:y:2010:i:2:p:187-200. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/agwat .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.