| Title |
Selection of Climate Indices for Nonstationary Frequency Analysis and Estimation of Rainfall Quantile |
| Authors |
정태호(Jung, Tae-Ho) ; 김한빈(Kim, Hanbeen) ; 김현식(Kim, Hyeonsik) ; 허준행(Heo, Jun-Haeng) |
| DOI |
https://doi.org/10.12652/Ksce.2019.39.1.0165 |
| Keywords |
기상인자;앙상블 경험적 모드분해법;비정상성 빈도해석;확률강우량 Climate indices;Ensemble empirical mode decomposition;Nonstationary frequency analysis;Rainfall quantile estimate |
| Abstract |
As a nonstationarity is observed in hydrological data, various studies on nonstationary frequency analysis for hydraulic structure design have been actively conducted. Although the inherent diversity in the atmosphere-ocean system is known to be related to the nonstationary phenomena, a nonstationary frequency analysis is generally performed based on the linear trend. In this study, a nonstationary frequency analysis was performed using climate indices as covariates to consider the climate variability and the long-term trend of the extreme rainfall. For 11 weather stations where the trend was detected, the long-term trend within the annual maximum rainfall data was extracted using the ensemble empirical mode decomposition. Then the correlation between the extracted data and various climate indices was analyzed. As a result, autumn-averaged AMM, autumn-averaged AMO, and summer-averaged NINO4 in the previous year significantly influenced the long-term trend of the annual maximum rainfall data at almost all stations. The selected seasonal climate indices were applied to the generalized extreme value (GEV) model and the best model was selected using the AIC. Using the model diagnosis for the selected model and the nonstationary GEV model with the linear trend, we identified that the selected model could compensate the underestimation of the rainfall quantiles. |