A Bayesian nonstationary probability rainfall estimation model using the Grid method is developed. A hierarchical Bayesian framework is consisted with prior and hyper-prior distributions associated with parameters of the Gumbel distribution which is selected for rainfall extreme data. In this study, the Grid method is adopted instead of the Matropolis Hastings algorithm for random number generation since it has advantage that it can provide a thorough sampling of parameter space. This method is good for situations where the best-fit parameter values are not easily inferred a priori, and where there is a high probability of false minima. The developed model was applied to estimated target year probability rainfall using hourly rainfall data of Seoul station from 1973 to 2012. Results demonstrated that the target year estimate using nonstationary assumption is about 5∼8% larger than the estimate using stationary assumption.

1. 서 론
 2. 계층적 베이지안 모형
  2.1 우도함수
  2.2 사전분포 및 초사전분포
  2.3 사후분포
 3. 매개변수 추정기법
  3.1 Gibbs sampler
  3.2 Grid method
  3.3 확률강우량 산정
 4. 결 론
 감사의 글
 References

• 곽도현(경북대학교 공과대학 건축·토목공학부) | Kwak, Dohyun
• 김광섭(경북대학교 대학원 건축·토목공학부) | Kim, Gwangseob Corresponding author