Regional frequency analysis is widely used to estimate more reliable quantiles of extreme hydrometeorological events. The stationarity of data is required for its application. This assumption tends to be violated due to climate change. In this paper, four nonstationary index flood models were used to analyze the nonstationary regional data. Monte Carlo simulation was used to evaluate the performances of these models for the generalized extreme value distribution with linearly time varying location parameter and constant scale and shape parameters. As a results, it was found that the index flood model with time-invariant index flood and time-variant growth curve could yield more statistically efficient quantile when record is long enough to show significant nonstationarity.
|Number of pages||4|
|Journal||IAHS-AISH Proceedings and Reports|
|Publication status||Published - 2015|
|Event||HS02 � Hydrologic Non-Stationarity and Extrapolating Models to Predict the Future in the 2015 IUGG General Assembly - Prague, Czech Republic|
Duration: 2015 Jun 22 → 2015 Jul 2
Bibliographical notePublisher Copyright:
© Author(s) 2015.
All Science Journal Classification (ASJC) codes
- Earth and Planetary Sciences(all)