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.
Bibliographical noteFunding Information:
This study was supported by the Korea Meteorological Administration R&D Program under Grant KMIPA 2015-6180 and the Basic Science Research Program through the National Research Foundation of Korea, which is funded by the Ministry of Science, ICT & Future Planning (2015R1C1A2A01054800). Projected daily precipitation GCM data from CMIP5 over the contiguous U.S. for 1950–2099 were downloaded from http://gdo-dcp.ucllnl.org (RECLAMATION, Climate and Hydrology Projections) and RCM data from NA-CORDEX for 1950 or 1951 to 2100 were obtained from http://na-cordex.org.
© 2017 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Water Science and Technology