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

Author

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  • 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
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    References listed on IDEAS

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    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.
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    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. 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.
    3. 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).

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