Modeling nonstationary extreme value distributions with nonlinear functions

An application using multiple precipitation projections for U.S. cities

Myoung Jin Um, Yeonjoo Kim, Momcilo Markus, Donald J. Wuebbles

Research output: Contribution to journalArticle

11 Citations (Scopus)

Abstract

Climate extremes, such as heavy precipitation events, have become more common in recent decades, and nonstationarity concepts have increasingly been adopted to model hydrologic extremes. Various issues are associated with applying nonstationary modeling to extremes, and in this study, we focus on assessing the need for different forms of nonlinear functions in a nonstationary generalized extreme value (GEV) model of different annual maximum precipitation (AMP) time series. Moreover, we suggest an efficient approach for selecting the nonlinear functions of a nonstationary GEV model. Based on observed and multiple projected AMP data for eight cities across the U.S., three separate tasks are proposed. First, we conduct trend and stationarity tests for the observed and projected data. Second, AMP series are fit with thirty different nonlinear functions, and the best functions among these are selected. Finally, the selected nonlinear functions are used to model the location parameter of a nonstationary GEV model and stationary and nonstationary GEV models with a linear function. Our results suggest that the simple use of nonlinear functions might prove useful with nonstationary GEV models of AMP for different locations with different types of model results.

Original languageEnglish
Pages (from-to)396-406
Number of pages11
JournalJournal of Hydrology
Volume552
DOIs
Publication statusPublished - 2017 Sep 1

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modeling
city
distribution
time series
climate

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

Cite this

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abstract = "Climate extremes, such as heavy precipitation events, have become more common in recent decades, and nonstationarity concepts have increasingly been adopted to model hydrologic extremes. Various issues are associated with applying nonstationary modeling to extremes, and in this study, we focus on assessing the need for different forms of nonlinear functions in a nonstationary generalized extreme value (GEV) model of different annual maximum precipitation (AMP) time series. Moreover, we suggest an efficient approach for selecting the nonlinear functions of a nonstationary GEV model. Based on observed and multiple projected AMP data for eight cities across the U.S., three separate tasks are proposed. First, we conduct trend and stationarity tests for the observed and projected data. Second, AMP series are fit with thirty different nonlinear functions, and the best functions among these are selected. Finally, the selected nonlinear functions are used to model the location parameter of a nonstationary GEV model and stationary and nonstationary GEV models with a linear function. Our results suggest that the simple use of nonlinear functions might prove useful with nonstationary GEV models of AMP for different locations with different types of model results.",
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Modeling nonstationary extreme value distributions with nonlinear functions : An application using multiple precipitation projections for U.S. cities. / Um, Myoung Jin; Kim, Yeonjoo; Markus, Momcilo; Wuebbles, Donald J.

In: Journal of Hydrology, Vol. 552, 01.09.2017, p. 396-406.

Research output: Contribution to journalArticle

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