IDEAS home Printed from https://ideas.repec.org/a/spr/waterr/v33y2019i8d10.1007_s11269-019-02277-w.html
   My bibliography  Save this article

Mixed General Extreme Value Distribution for Estimation of Future Precipitation Quantiles Using a Weighted Ensemble - Case Study of the Lim River Basin (Serbia)

Author

Listed:
  • Milan Stojkovic

    (University of Western Ontario)

  • Slobodan P. Simonovic

    (University of Western Ontario)

Abstract

Considering the recent extreme precipitation in southeast Europe, it has become necessity to investigate the impact of climate change on extreme precipitation. The aim of this study was to determine the change in precipitation quantiles with longer return periods under changing climate conditions. The study was conducted using the daily records gathered at 11 precipitation stations within the Lim River Basin, Serbia. The simulated precipitation datasets were collected from three regional climate models for the baseline period (1971–2000), as well as the future period (2006–2055) under the 2.6, 4.5 and 8.5 representative concentration pathways. The raw precipitation data from the climate models were transformed by employing four bias correction methods. Using the bias-corrected precipitation, an ensemble of annual maximum daily precipitation was developed. A weighted ensemble approach was applied to estimate the weights of each ensemble member favorizing the members whose quantiles were closer to observed measurements. The mixed general extreme value distribution was used to derive the projected quantiles with 100, 50, 25, 10, five and two year return periods based on the estimated quantiles and the normalized weights of all ensemble members. An overall increase of 69% and 56% for the 100 and 50 year return periods, respectively, can be expected within the northern part of the basin. Similarly, an overall increase of 50–57% and 39–42% for the 100 and 50 year return periods, respectively, may be expected for the central and southern parts of the Lim River Basin.

Suggested Citation

  • Milan Stojkovic & Slobodan P. Simonovic, 2019. "Mixed General Extreme Value Distribution for Estimation of Future Precipitation Quantiles Using a Weighted Ensemble - Case Study of the Lim River Basin (Serbia)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2885-2906, June.
  • Handle: RePEc:spr:waterr:v:33:y:2019:i:8:d:10.1007_s11269-019-02277-w
    DOI: 10.1007/s11269-019-02277-w
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11269-019-02277-w
    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/s11269-019-02277-w?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. Milan Stojković & Stevan Prohaska & Nikola Zlatanović, 2017. "Estimation of flood frequencies from data sets with outliers using mixed distribution functions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(11), pages 2017-2035, 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. Tao Gao & Yifei Xu & Huixia Judy Wang & Qiaohong Sun & Lian Xie & Fuqiang Cao, 2022. "Combined Impacts of Climate Variability Modes on Seasonal Precipitation Extremes Over China," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 36(7), pages 2411-2431, May.
    2. Dai, Baorui & Xia, Ye & Li, Qi, 2022. "An extreme value prediction method based on clustering algorithm," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
    3. Sweta Rai & Alexis Hoffman & Soumendra Lahiri & Douglas W. Nychka & Stephan R. Sain & Soutir Bandyopadhyay, 2024. "Fast parameter estimation of generalized extreme value distribution using neural networks," Environmetrics, John Wiley & Sons, Ltd., vol. 35(3), 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. Slobodan Kolaković & Vladimir Mandić & Milan Stojković & Goran Jeftenić & Danilo Stipić & Srđan Kolaković, 2023. "Estimation of Large River Design Floods Using the Peaks-Over-Threshold (POT) Method," Sustainability, MDPI, vol. 15(6), pages 1-14, March.
    2. Wiesław Szulczewski & Wojciech Jakubowski, 2018. "The Application of Mixture Distribution for the Estimation of Extreme Floods in Controlled Catchment Basins," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(10), pages 3519-3534, August.

    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:8:d:10.1007_s11269-019-02277-w. 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.