IDEAS home Printed from https://ideas.repec.org/a/gam/jagris/v14y2024i7p1107-d1431761.html
   My bibliography  Save this article

Modeling Reliability Analysis for the Branch-Based Irrigation Water Demands Due to Uncertainties in the Measured Surface Runoff

Author

Listed:
  • Shiang-Jen Wu

    (Department of Civil and Disaster Prevention Engineering, National United University, Miaoli City 360302, Taiwan)

  • Han-Yuan Yang

    (Department of Civil and Disaster Prevention Engineering, National United University, Miaoli City 360302, Taiwan)

Abstract

This study aims to model the uncertainty and reliability quantification of estimating the planning irrigation water demands in the multi-canal irrigation zone, named the RA_IWD_Canal model. The proposed RA_IWD_Canal could estimate the zone-based and branch-based water demands and quantify their uncertainties and reliabilities via the weighted frequency quantile curves. The historical planning irrigation water demands and measured surface runoff from 2019 to 2024 in the Zhudong irrigation zone are utilized in the model development and application. Using the proposed RA_IWD_Canal model, the estimated branch-based irrigation water demands exhibit a significant variation (on average, from 0.02 m 3 /s to 1.7 m 3 /s) in time and space attributed to uncertainties in the historical gauged surface runoff. Also, the Zhudong Canal zone is demonstrated to be sufficiently supplied irrigation water subject to existing introduced water demands with a high reliability of 0.85; instead, the associated branches have considerable difficulty achieving the expected irrigation efficiency based on the desired water requirements with low reliability (nearly 0.25). To keep all branches in the irrigation zone consistent in irrigation efficiency, the probabilistic-based water demands could be introduced via the proposed RA_IWD_Canal model with the desired reliability.

Suggested Citation

  • Shiang-Jen Wu & Han-Yuan Yang, 2024. "Modeling Reliability Analysis for the Branch-Based Irrigation Water Demands Due to Uncertainties in the Measured Surface Runoff," Agriculture, MDPI, vol. 14(7), pages 1-27, July.
    • Handle: RePEc:gam:jagris:v:14:y:2024:i:7:p:1107-:d:1431761
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2077-0472/14/7/1107/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2077-0472/14/7/1107/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Awais Ali & Tajamul Hussain & Noramon Tantashutikun & Nurda Hussain & Giacomo Cocetta, 2023. "Application of Smart Techniques, Internet of Things and Data Mining for Resource Use Efficient and Sustainable Crop Production," Agriculture, MDPI, vol. 13(2), pages 1-22, February.
    2. Benavides, Juan & Hernández-Plaza, Eva & Mateos, Luciano & Fereres, Elías, 2021. "A global analysis of irrigation scheme water supplies in relation to requirements," Agricultural Water Management, Elsevier, vol. 243(C).
    3. Yunquan Zhang & Peiling Yang, 2022. "Agricultural Water Optimal Allocation Using Minimum Cross-Entropy and Entropy-Weight-Based TOPSIS Method in Hetao Irrigation District, Northwest China," Agriculture, MDPI, vol. 12(6), pages 1-18, June.
    Full references (including those not matched with items on IDEAS)

    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. Wu, Shiang-Jen & Yang, Han-Yuan & Chang, Che-Hao & Hsu, Chih-Tsung, 2023. "Modeling GA-derived optimization analysis for canal-based irrigation water allocation under variations in runoff-related and irrigation-related factors," Agricultural Water Management, Elsevier, vol. 290(C).
    2. You, Yongliang & Song, Ping & Yang, Xianlong & Zheng, Yapeng & Dong, Li & Chen, Jing, 2022. "Optimizing irrigation for winter wheat to maximize yield and maintain high-efficient water use in a semi-arid environment," Agricultural Water Management, Elsevier, vol. 273(C).
    3. Duarte, A.C. & Mateos, L., 2022. "How changes in cropping intensity affect water usage in an irrigated Mediterranean catchment," Agricultural Water Management, Elsevier, vol. 260(C).
    4. Li Bin & Muhammad Shahzad & Hira Khan & Muhammad Mehran Bashir & Arif Ullah & Muhammad Siddique, 2023. "Sustainable Smart Agriculture Farming for Cotton Crop: A Fuzzy Logic Rule Based Methodology," Sustainability, MDPI, vol. 15(18), pages 1-18, September.
    5. Awais Ali & Genhua Niu & Joseph Masabni & Antonio Ferrante & Giacomo Cocetta, 2024. "Integrated Nutrient Management of Fruits, Vegetables, and Crops through the Use of Biostimulants, Soilless Cultivation, and Traditional and Modern Approaches—A Mini Review," Agriculture, MDPI, vol. 14(8), pages 1-28, August.
    6. Mark Schoor & Ana Patricia Arenas-Salazar & Benito Parra-Pacheco & Juan Fernando García-Trejo & Irineo Torres-Pacheco & Ramón Gerardo Guevara-González & Enrique Rico-García, 2024. "Horticultural Irrigation Systems and Aquacultural Water Usage: A Perspective for the Use of Aquaponics to Generate a Sustainable Water Footprint," Agriculture, MDPI, vol. 14(6), pages 1-22, June.
    7. Yunquan Zhang & Peiling Yang, 2023. "A Simulation-Based Optimization Model for Control of Soil Salinization in the Hetao Irrigation District, Northwest China," Sustainability, MDPI, vol. 15(5), pages 1-20, March.
    8. David D. J. Antia, 2023. "Desalination of Saline Irrigation Water Using Hydrophobic, Metal–Polymer Hydrogels," Sustainability, MDPI, vol. 15(9), pages 1-32, April.
    9. Asuamah Yeboah, Samuel, 2023. "Empowering Sustainable Consumption: Harnessing the Potential of Smart Grid Systems and Internet of Things for Environmental Conservation," MPRA Paper 117978, University Library of Munich, Germany, revised 29 May 2023.
    10. Godlove Suila Kuaban & Valery Nkemeni & Onyeka J. Nwobodo & Piotr Czekalski & Fabien Mieyeville, 2024. "Internet of Things Adoption in Technology Ecosystems Within the Central African Region: The Case of Silicon Mountain," Future Internet, MDPI, vol. 16(10), pages 1-28, October.

    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:gam:jagris:v:14:y:2024:i:7:p:1107-:d:1431761. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.