Regional frequency analysis of extreme precipitation based on a nonstationary population index flood method

Hanbeen Kim, Ju Young Shin, Taereem Kim, Sunghun Kim, Jun Haeng Heo

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

Anthropogenic climate change has led to nonstationarity in hydrological data and their statistical characteristics. To consider nonstationarity in regional frequency analysis, several nonstationary index flood (NS-IF) methods comprising a time-dependent site-specific scaling factor or nonstationary regional growth curves have been suggested. However, these methods have limitations related to underestimation from using sample statistics as a site-specific scaling factor or considering nonstationarity only in regional parameters. To overcome these drawbacks, this study developed a nonstationary population index flood (NS-PIF) method that considers nonstationarity in the statistical characteristics at each site in a region based on nonstationary generalized extreme value distributions. Monte Carlo simulations were conducted for synthetic regions under various nonstationary conditions to compare the performance of the NS-PIF method with those of existing NS-IF methods. Then the applicability of the NS-PIF method to real-world data was assessed via Monte Carlo simulations of regions with annual maximum rainfall data in South Korea. The results indicated that the NS-PIF method can solve the underestimation problem inherent in existing NS-IF methods. Moreover, the NS-PIF method yielded the best performance and provided more reliable and reasonable quantile estimates considering site-specific trends. In addition, the heterogeneity measure based on L-skewness and L-kurtosis was identified as a suitable test of homogeneity for application of the proposed method.

Original languageEnglish
Article number103757
JournalAdvances in Water Resources
Volume146
DOIs
Publication statusPublished - 2020 Dec

Bibliographical note

Funding Information:
This work was supported by a National Research Foundation of Korea(NRF) grant funded by the Korea government (MSIT) (No. 2019R1A2C2010854). Daily rainfall data used in this study can be obtained from the Korea Meteorological Administration (https://data.kma.go.kr/data/grnd/selectAsosRltmList.do?pgmNo=36).

Funding Information:
This work was supported by a National Research Foundation of Korea ( NRF ) grant funded by the Korea government (MSIT) (No. 2019R1A2C2010854 ). Daily rainfall data used in this study can be obtained from the Korea Meteorological Administration ( https://data.kma.go.kr/data/grnd/selectAsosRltmList.do?pgmNo=36 ).

Publisher Copyright:
© 2020

All Science Journal Classification (ASJC) codes

  • Water Science and Technology

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