TY - JOUR
T1 - Modeling nonstationary extreme value distributions with nonlinear functions
T2 - An application using multiple precipitation projections for U.S. cities
AU - Um, Myoung Jin
AU - Kim, Yeonjoo
AU - Markus, Momcilo
AU - Wuebbles, Donald J.
N1 - Publisher Copyright:
© 2017 Elsevier B.V.
PY - 2017/9
Y1 - 2017/9
N2 - 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.
AB - 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.
KW - Extreme precipitation
KW - Nonlinear function
KW - Nonstationary
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U2 - 10.1016/j.jhydrol.2017.07.007
DO - 10.1016/j.jhydrol.2017.07.007
M3 - Article
AN - SCOPUS:85023632250
SN - 0022-1694
VL - 552
SP - 396
EP - 406
JO - Journal of Hydrology
JF - Journal of Hydrology
ER -