- Volume 33 Issue 5
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Development of Daily Rainfall Simulation Model Based on Homogeneous Hidden Markov Chain
동질성 Hidden Markov Chain 모형을 이용한 일강수량 모의기법 개발
- Received : 2013.04.16
- Accepted : 2013.07.17
- Published : 2013.09.30
A climate change-driven increased hydrological variability has been widely acknowledged over the past decades. In this regards, rainfall simulation techniques are being applied in many countries to consider the increased variability. This study proposed a Homogeneous Hidden Markov Chain(HMM) designed to recognize rather complex patterns of rainfall with discrete hidden states and underlying distribution characteristics via mixture probability density function. The proposed approach was applied to Seoul and Jeonju station to verify model's performance. Statistical moments(e.g. mean, variance, skewness and kurtosis) derived by daily and seasonal rainfall were compared with observation. It was found that the proposed HMM showed better performance in terms of reproducing underlying distribution characteristics. Especially, the HMM was much better than the existing Markov Chain model in reproducing extremes. In this regard, the proposed HMM could be used to evaluate a long-term runoff and design flood as inputs.
Grant : 지역특성을 반영한 상세 격자강우량 생산기술 개발
Supported by : 한국건설기술연구원
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