IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v33y2019i13d10.1007_s11269-019-02384-8.html
   My bibliography  Save this article

One Dimensional Hydraulic Flow Routing Incorporating a Variable Grain Roughness Coefficient

Author

Listed:
  • Majid Niazkar

    (Shiraz University)

  • Nasser Talebbeydokhti

    (Shiraz University)

  • Seied Hosein Afzali

    (Shiraz University)

Abstract

The reach-average impacts of frictional forces, which retard flows in man-made channels and natural streams, are basically taken into account by flow resistance coefficients. These coefficients have been commonly treated as either a constant or a variable parameter, while the latter is only feasible through a tedious calibration process considering different flow and channel-boundary conditions. When neither historical records are available nor flow measurement is possible, applying a fixed-value roughness coefficient is practically inevitable. Although variation of Manning’s coefficient (n) with flow characteristics has been established in the literature, it has not been systematically implemented into hydraulic flow routing models, particularly because of the absence of a flow-dependent bed roughness predictor (BRP) suitable for numerical applications. In this study, a new grain roughness predictor, which provides derivations of n in respect with discharge and stage, is proposed. This grain roughness estimator, which enables to consider flow-dependent variation of n, is implemented in casting of governing equations of one-dimensional hydraulic flow routing method. In the numerical experiments designed to assess this implementation, three scenarios for n were considered: (1) constant n, (2) variable n computed using the new roughness predictor, and (3) variable n calculated based on the observed data. The third scenario, which requires a significant amount of field measurements, was considered as the benchmark solution. The obtained results showed that applying the proposed BRP to the hydraulic flow routing improved estimated outflows more than 40% based on the mean absolute relative error. The achieved improvement obviously demonstrates that considering variable resistance coefficient, like the one suggested in this study, may considerably improve the results of the flow-related numerical modeling.

Suggested Citation

  • Majid Niazkar & Nasser Talebbeydokhti & Seied Hosein Afzali, 2019. "One Dimensional Hydraulic Flow Routing Incorporating a Variable Grain Roughness Coefficient," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(13), pages 4599-4620, October.
    • Handle: RePEc:spr:waterr:v:33:y:2019:i:13:d:10.1007_s11269-019-02384-8
    DOI: 10.1007/s11269-019-02384-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-019-02384-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11269-019-02384-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    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. Majid Niazkar & Seied Hosein Afzali, 2016. "Application of New Hybrid Optimization Technique for Parameter Estimation of New Improved Version of Muskingum Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 30(13), pages 4713-4730, October.
    2. Majid Niazkar & Seied Afzali, 2015. "Optimum Design of Lined Channel Sections," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(6), pages 1921-1932, April.
    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. Lishuang Yao & Yang Peng & Xianliang Yu & Zhihong Zhang & Shiqi Luo, 2023. "Optimal Inversion of Manning’s Roughness in Unsteady Open Flow Simulations Using Adaptive Parallel Genetic Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(2), pages 879-897, January.
    2. Hriday Mani Kalita, 2020. "A Numerical Model for 1D Bed Morphology Calculations," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(15), pages 4975-4989, December.
    3. Hamidreza Rahimi & Saiyu Yuan & Xiaonan Tang & Chunhui Lu & Prateek Singh & Fariba Ahmadi Dehrashid, 2022. "Study on Conveyance Coefficient Influenced by Momentum Exchange Under Steady and Unsteady Flows in Compound Open Channels," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2179-2199, May.

    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. Majid Niazkar, 2020. "Discussion of “Accurate and Efficient Explicit Approximations of the Colebrook Flow Friction Equation Based on the Wright ω-Function” by Dejan Brkić and Pavel Praks, Mathematics 2019, 7 , 34; doi:10.3," Mathematics, MDPI, vol. 8(5), pages 1-6, May.
    2. Wen-chuan Wang & Wei-can Tian & Dong-mei Xu & Kwok-wing Chau & Qiang Ma & Chang-jun Liu, 2023. "Muskingum Models’ Development and their Parameter Estimation: A State-of-the-art Review," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(8), pages 3129-3150, June.
    3. Ling Kang & Liwei Zhou & Song Zhang, 2017. "Parameter Estimation of Two Improved Nonlinear Muskingum Models Considering the Lateral Flow Using a Hybrid Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 31(14), pages 4449-4467, November.
    4. Jalal Bazargan & Hadi Norouzi, 2018. "Investigation the Effect of Using Variable Values for the Parameters of the Linear Muskingum Method Using the Particle Swarm Algorithm (PSO)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(14), pages 4763-4777, November.
    5. Majid Niazkar & Nasser Talebbeydokhti & Seied Hosein Afzali, 2019. "Novel Grain and Form Roughness Estimator Scheme Incorporating Artificial Intelligence Models," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(2), pages 757-773, January.
    6. Ahmed A. Lamri & Said M. Easa, 2022. "Lambert W-function Solution for Uniform Flow Depth Problem," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(8), pages 2653-2663, June.
    7. Reyhaneh Akbari & Masoud-Reza Hessami-Kermani & Saeed Shojaee, 2020. "Flood Routing: Improving Outflow Using a New Non-linear Muskingum Model with Four Variable Parameters Coupled with PSO-GA Algorithm," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 34(10), pages 3291-3316, August.
    8. Aryan Salvati & Alireza Moghaddam Nia & Ali Salajegheh & Parham Moradi & Yazdan Batmani & Shahabeddin Najafi & Ataollah Shirzadi & Himan Shahabi & Akbar Sheikh-Akbari & Changhyun Jun & John J. Clague, 2023. "Performance improvement of the linear muskingum flood routing model using optimization algorithms and data assimilation approaches," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 118(3), pages 2657-2690, September.
    9. Esmatullah Sangin & S. K. Mishra & Pravin R. Patil, 2024. "Analogy Between SCS-CN and Muskingum Methods," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 38(1), pages 153-171, January.
    10. Mike Spiliotis & Alvaro Sordo-Ward & Luis Garrote, 2021. "Estimation of Fuzzy Parameters in the Linear Muskingum Model with the Aid of Particle Swarm Optimization," Sustainability, MDPI, vol. 13(13), pages 1-26, June.
    11. Wanlong Yang & Jun Wang & Jueyi Sui & Fangxiu Zhang & Baosen Zhang, 2019. "A Modified Muskingum Flow Routing Model for Flood Wave Propagation during River Ice Thawing-Breakup Period," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(14), pages 4865-4878, November.
    12. Aly K. Salem & Yehya E. Imam & Ashraf H. Ghanem & Abdallah S. Bazaraa, 2022. "Genetic Algorithm Based Model for Optimal Selection of Open Channel Design Parameters," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(15), pages 5867-5896, December.

    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:spr:waterr:v:33:y:2019:i:13:d:10.1007_s11269-019-02384-8. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.