강우-유출모형 매개변수의 최적화 및 불확실성 분석

• 영문명: Parameter Optimization and Uncertainty Analysis of the Rainfall-Runoff Model
• 발행기관: 한국방재학회
• 저자명: 문영일 권현한 Moon, Young-Il Kwon, Hyun-Han
• 간행물 정보: 『3. 한국방재학회 학술대회논문집』2008년, 723~726쪽, 전체 4쪽
• 주제분류: 공학 > 기타공학
• 파일형태: PDF
• 발행일자: 2008.02.28

영문 초록

It is not always easy to estimate the parameters in hydrologic models due to insufficient hydrologic data when hydraulic structures are designed or water resources plan are established, uncertainty analysis, therefore, are inevitably needed to examine reliability for the estimated results. With regard to this point, this study applies a Bayesian Markov Chain Monte Carlo scheme to the NWS-PC rainfall-runoff model that has been widely used, and a case study is performed in Soyang Dam watershed in Korea. The NWS-PC model is calibrated against observed daily runoff, and thirteen parameters in the model are optimized as well as posterior distributions associated with each parameter are derived. The Bayesian Markov Chain Monte Carlo shows a improved result in terms of statistical performance measures and graphical examination. The patterns of runoff can be influenced by various factors and the Bayesian approaches are capable of translating the uncertainties into parameter uncertainties. One could provide against an expected runoff event by utilizing information driven by Bayesian methods. Therefore, the rainfall-runoff analysis coupled with the uncertainty analysis can give us an insight in evaluating flood risk and dam size in a reasonable way.

키워드

Rainfall-Runoff Model Parameter Optimization Uncertainty Analysis