IDEAS home Printed from https://ideas.repec.org/a/spr/nathaz/v86y2017i1d10.1007_s11069-016-2687-0.html
   My bibliography  Save this article

Selection of the best fit probability distribution in rainfall frequency analysis for Qatar

Author

Listed:
  • Abdullah Al Mamoon

    (Ministry of Municipality and Environment
    Western Sydney University)

  • Ataur Rahman

    (Western Sydney University)

Abstract

Design rainfall is widely used in urban infrastructure planning and design such as culverts and urban drainage systems. In design rainfall estimation, one of the primary steps is the selection of a suitable probability distribution that fits the observed rainfall data adequately. This study examines the selection of the best fit probability distribution in design rainfall estimation. The annual maximum (AM) rainfall data from 29 rainfall stations in Qatar are used in this study. The rainfall record lengths of these stations are in the range of 24–49 years (average of 36 years). Fourteen different distributions and three goodness-of-fit tests (Kolmogorov–Smirnov, Anderson–Darling and Chi-squared) are considered. Based on a relative scoring method, the GEV distribution is found to be the best fit distribution. Results from bootstrapping and simulation analyses show that sample estimates of skewness of the AM rainfall series are subject to a higher degree of sensitivity to data length compared with standard deviation and mean as expected. Since the rainfall quantile estimates of higher return periods are greatly influenced by skewness, a longer data length is needed in reducing the uncertainty in rainfall quantile estimates for higher return periods, which is currently unavailable in Qatar.

Suggested Citation

  • Abdullah Al Mamoon & Ataur Rahman, 2017. "Selection of the best fit probability distribution in rainfall frequency analysis for Qatar," 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. 86(1), pages 281-296, March.
    • Handle: RePEc:spr:nathaz:v:86:y:2017:i:1:d:10.1007_s11069-016-2687-0
    DOI: 10.1007/s11069-016-2687-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11069-016-2687-0
    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/s11069-016-2687-0?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.

    Citations

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


    Cited by:

    1. Siti Fatihah Salleh & Ahmad Abubakar Suleiman & Hanita Daud & Mahmod Othman & Rajalingam Sokkalingam & Karl Wagner, 2023. "Tropically Adapted Passive Building: A Descriptive-Analytical Approach Using Multiple Linear Regression and Probability Models to Predict Indoor Temperature," Sustainability, MDPI, vol. 15(18), pages 1-25, September.

    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:nathaz:v:86:y:2017:i:1:d:10.1007_s11069-016-2687-0. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.