대한토목학회논문집 (KSCE Journal of Civil and Environmental Engineering Research)

  • 제34권2호
  • /
  • Pages.469-478
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  • 2014
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  • 1015-6348(pISSN)
  • /
  • 2799-9629(eISSN)
대한토목학회 (Korean Society of Civil Engeneers)
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연최대치계열과 비연초과치계열으로부터 산정한 확률강우량의 비교·분석

Comparison of Design Rainfalls From the Annual Maximum and the Non-annual Exceedance Series

  • 박예준 (한양대학교 대학원 건설환경공학과) ;
  • 권현한 (전북대학교 토목공학과) ;
  • 정은성 (서울과학기술대학교 건설시스템디자인공학과) ;
  • 김태웅 (한양대학교 공학대학 건설환경플랜트공학과)
  • 투고 : 2013.08.06
  • 심사 : 2014.02.03
  • 발행 : 2014.04.01
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초록

확률수문량을 산정하기 위해서 실무에서 많이 사용하는 연최대치 계열은 자료의 구축이 간편한 장점이 있지만, 우리나라에서 연최대치 계열을 이용하기에는 자료의 수가 매우 제한적이다. 특히, 적은 관측자료를 바탕으로 확률강우량 또는 설계홍수량을 추정할 경우 과다산정을 할 가능성이 매우 높다. 본 연구에서는 기상청에서 제공하는 1973년부터 2012년까지 총 40년간의 관측자료를 대상으로 독립호우사상을 구분하고, 연최대치 계열과 비연초과치 계열을 구성한 후, 연최대치 계열과 비연초과치 계열로부터 산정된 확률강우량의 상관성을 분석하고, 적은 관측자료를 가지고 지점빈도해석을 수행하여 확률강우량을 추정할 수 있는 방법을 제안하였다.

The annual maximum series (AMS) is usually used to estimate hydrological quantiles in practice because it is simple to construct and straightforward to probabilistic interpretation. However, it is limited to use the AMS in Korea due to the lack of reliable observed data which leads to the overestimation of design rainfall and/or flood. Using the 40-year observations of rainfall provided by the Korea Meteorological Administration, this study constructed the AMS and non-annual exceedance series (NAES) after identifying the independent storm event, analyzed the correlation between design rainfalls estimated from the AMS and NAES, and proposed a new method of point frequency analysis to estimate design rainfalls from the small number of observations.

키워드

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피인용 문헌

  1. Quantitative characterization of historical drought events in Korea -focusing on outlier analysis of precipitation- vol.49, pp.2, 2016, https://doi.org/10.3741/JKWRA.2016.49.2.145
  2. Assessment of uncertainty associated with parameter of gumbel probability density function in rainfall frequency analysis vol.49, pp.5, 2016, https://doi.org/10.3741/JKWRA.2016.49.5.411
  3. Nonstationary Probability Rainfall Estimation at Seoul Using Neural Networks and GCM Data vol.18, pp.2, 2018, https://doi.org/10.9798/KOSHAM.2018.18.2.63