Journal of Korea Water Resources Association (한국수자원학회논문집)

Korea Water Resources Association (한국수자원학회)
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Development of Hierarchical Bayesian Spatial Regional Frequency Analysis Model Considering Geographical Characteristics

지형특성을 활용한 계층적 Bayesian Spatial 지역빈도해석

  • Kim, Jin-Young (Department of Civil Engineering, Chonbuk National University) ;
  • Kwon, Hyun-Han (Department of Civil Engineering, Chonbuk National University) ;
  • Lim, Jeong-Yeul (K-water Institute, Korea Water Resources Corporation)
  • 김진영 (전북대학교 토공공학과, 방재연구센터) ;
  • 권현한 (전북대학교 토공공학과, 방재연구센터) ;
  • 임정열 (K-water 연구원)
  • Received : 2014.04.07
  • Accepted : 2014.04.23
  • Published : 2014.05.31
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Abstract

This study developed a Bayesian spatial regional frequency analysis, which aimed to analyze spatial patterns of design rainfall by incorporating geographical information (e.g. latitude, longitude and altitude) and climate characteristics (e.g. annual maximum series) within a Bayesian framework. There are disadvantages to considering geographical characteristics and to increasing uncertainties associated with areal rainfall estimation on the existing regional frequency analysis. In this sense, this study estimated the parameters of Gumbel distribution which is a function of geographical and climate characteristics, and the estimated parameters were spatially interpolated to derive design rainfall over the entire Han-river watershed. The proposed Bayesian spatial regional frequency analysis model showed similar results compared to L-moment based regional frequency analysis, and even better performance in terms of quantifying uncertainty of design rainfall and considering geographical information as a predictor.

본 연구에서는 지역특성(위도, 경도, 고도)과 기후학적 특성(연최대강우량)을 계층적 Bayesian 모형안에서 연계하여 공간적 분석이 가능한 지역빈도해석 모형을 개발하였다. 기존 지역빈도해석은 강수지점의 지리적/지형적 특성을 반영한 해석이 어려운 단점이 있으며, 지점을 기준으로 해석된 확률강수량을 유역면적강우량으로 변환 시 불확실성이 큰 단점이 있다. 이에 본 연구에서는 계층적 Bayesian 기법을 이용하여 지역특성 및 기후학적 특성이 고려된 Gumbel 확률분포형의 매개변수를 추정하였으며, 이들 매개변수들을 공간적으로 보간하여 한강유역내 모든 지점에 대해서 확률강수량을 추정할 수 있도록 하였다. 결과적으로 기존 L-모멘트 방법과 유사한 결과를 확인할 수 있었으며 확률강수량의 불확실성 정량화와 더불어 지리적/지형적 영향을 고려한 해석이 가능하였다.

Keywords

References

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