• Title/Summary/Keyword: Chow-Liu Tree

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Development of Multi-Site Daily Rainfall Simulation Based on Homogeneous Hidden Markov Chain Model Coupled with Chow-Liu Tree Structures (Chow-Liu Tree 모형과 동질성 Hidden Markov Model을 연계한 다지점 일강수량 모의기법 개발)

  • Kwon, Hyun-Han;Kim, Tae Jeong;Kim, Oon Ki;Lee, Dong Ryul
    • Journal of Korea Water Resources Association
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    • v.46 no.10
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    • pp.1029-1040
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    • 2013
  • This study aims to develop a multivariate daily rainfall simulation model considering spatial coherence across watershed. The existing Hidden Markov Model (HMM) has been mainly applied to single site case so that the spatial coherences are not properly addressed. In this regard, HMM coupled with Chow-Liu Tree (CLT) that is designed to consider inter-dependences across rainfall networks was proposed. The proposed approach is applied to Han-River watershed where long-term and reliable hydrologic data is available, and a rigorous validation is finally conducted to verify the model's capability. It was found that the proposed model showed better performance in terms of reproducing daily rainfall statistics as well as seasonal rainfall statistics. Also, correlation matrix across stations for observation and simulation was compared and examined. It was confirmed that the spatial coherence was well reproduced via CLT-HMM model.

Development of Multisite Spatio-Temporal Downscaling Model for Rainfall Using GCM Multi Model Ensemble (다중 기상모델 앙상블을 활용한 다지점 강우시나리오 상세화 기법 개발)

  • Kim, Tae-Jeong;Kim, Ki-Young;Kwon, Hyun-Han
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.35 no.2
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    • pp.327-340
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    • 2015
  • General Circulation Models (GCMs) are the basic tool used for modelling climate. However, the spatio-temporal discrepancy between GCM and observed value, therefore, the models deliver output that are generally required calibration for applied studies. Which is generally done by Multi-Model Ensemble (MME) approach. Stochastic downscaling methods have been used extensively to generate long-term weather sequences from finite observed records. A primary objective of this study is to develop a forecasting scheme which is able to make use of a MME of different GCMs. This study employed a Nonstationary Hidden Markov Chain Model (NHMM) as a main tool for downscaling seasonal ensemble forecasts over 3 month period, providing daily forecasts. Our results showed that the proposed downscaling scheme can provide the skillful forecasts as inputs for hydrologic modeling, which in turn may improve water resources management. An application to the Nakdong watershed in South Korea illustrates how the proposed approach can lead to potentially reliable information for water resources management.