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Non-stationary frequency analysis of rainfall events in Korea and Japan
  • +7
  • Wei Zhu,
  • LUO Pingping,
  • Yitao Song,
  • Shuangtao Wang,
  • Yang Wu,
  • Eunbi Kang,
  • LYU Jiqiang,
  • ZHOU Meimei,
  • Kaoru Takara,
  • Daniel Nover
Wei Zhu
Chang'an University

Corresponding Author:[email protected]

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LUO Pingping
Chang'an University
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Yitao Song
Chang'an University
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Shuangtao Wang
Chang'an University
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Yang Wu
Chang'an University
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Eunbi Kang
Department of Civil and Earth Resources Engineering Graduate School of Engineering Kyoto University
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LYU Jiqiang
Chang'an University
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ZHOU Meimei
Chang'an University
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Kaoru Takara
Graduate School of Advanced Integrated Studies (GSAIS) in Human Survivability (Shishu-Kan) Kyoto University Kyoto Japan
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Daniel Nover
University of California Merced
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Abstract

Predicting extreme storm and flood events requires analysis to predict probable rainfall in target years. We present a non-stationary frequency analysis for 6 meteorological stations in Korea and Japan: Gangneung, Kwangju, Pohang, Seoul, Kochi, Iida. Non-stationary analysis results in higher estimated rainfall than stationary analysis for all stations. Increased probable rainfall in Korean stations was higher than in Japanese stations (i.e. Z-values of Korean stations were larger than for Japanese stations). Using rainfall data at the 6 sites with increasing trends, we estimate 3 types of probably predicted rainfall for the target years 2020, 2050 and 2070. According to the results of applicability analysis, in the case of a 100-year return period, the probable rainfall estimated by non-stationary methods has a residual of 1.6~2.5% in Kochi, 11.98~16.01% in Gangneung, 4.3~4.9% in Kwangju, and 3.2~5.3% in Seoul. This study indicates that non-stationary methods provide better results in terms of confidence than stationary methods for representing rainfall with increasing trends. The non-stationary rainfall frequency analysis provided more reasonable and well-directed estimates of probable rainfall for the target year. Results show that non-stationary methods estimate probable rainfall well over short timescales based on linear regression of observed data. Further, the probable rainfall estimator for target years reflects the increasing temporal pattern of rainfall and predicts future rainfall. Results from this study can inform the design of flood prevention approaches and effective hydraulic structures.
30 Mar 2022Submitted to Hydrological Processes
06 Apr 2022Submission Checks Completed
06 Apr 2022Assigned to Editor
06 Apr 2022Reviewer(s) Assigned
13 Apr 2022Review(s) Completed, Editorial Evaluation Pending