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Non-Stationary Annual Maximum Flood Frequency Analysis Using the Norming Constants Method to Consider Non-Stationarity in the Annual Daily Flow Series

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  • Lihua Xiong
  • Tao Du
  • Chong-Yu Xu
  • Shenglian Guo
  • Cong Jiang
  • Christopher Gippel

Abstract

Flood frequency analysis is concerned with fitting a probability distribution to observed data to make predictions about the occurrence of floods in the future. Under conditions of climate change, or other changes to the water cycle that impact flood runoff, the flood series is likely to exhibit non-stationarity, in which case the return period of a flood event of a certain magnitude would change over time. In non-stationary flood frequency analysis, it is customary to examine only the non-stationarity of annual maximum flood data. We developed a way of considering the effect of non-stationarity in the annual daily flow series on the non-stationarity in the annual maximum flood series, which we termed the norming constants method (NCM) of non-stationary flood frequency analysis (FFA). After developing and explaining a framework for application of the method, we tested it using data from the Wei River, China. After detecting significant non-stationarity in both the annual maximum daily flood series and the annual daily flow series, application of the method revealed superior model performance compared to modelling the annual maximum daily flood series under the assumption of stationarity, and the result was further improved if explanatory climatic variables were considered. We conclude that the NCM of non-stationary FFA has potential for widespread application due to the now generally accepted weakness of the assumption of stationarity of flood time series. Copyright Springer Science+Business Media Dordrecht 2015

Suggested Citation

  • Lihua Xiong & Tao Du & Chong-Yu Xu & Shenglian Guo & Cong Jiang & Christopher Gippel, 2015. "Non-Stationary Annual Maximum Flood Frequency Analysis Using the Norming Constants Method to Consider Non-Stationarity in the Annual Daily Flow Series," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 29(10), pages 3615-3633, August.
    • Handle: RePEc:spr:waterr:v:29:y:2015:i:10:p:3615-3633
    DOI: 10.1007/s11269-015-1019-6
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    References listed on IDEAS

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    7. Wentao Xu & Cong Jiang & Lei Yan & Lingqi Li & Shuonan Liu, 2018. "An Adaptive Metropolis-Hastings Optimization Algorithm of Bayesian Estimation in Non-Stationary Flood Frequency Analysis," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 32(4), pages 1343-1366, March.
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