• Title/Summary/Keyword: real-time flood forecast

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Long term Rainfall-Runoff Modeling Using Storage Function Method (저류함수를 이용한 일단위 장기유출모의 모형 구축)

  • Sung, Young-Du;Chong, Koo-Yol;Shin, Cheol-Kyun;Park, Jin-Hyeog
    • Journal of Korea Water Resources Association
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    • v.41 no.7
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    • pp.737-746
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    • 2008
  • The purpose of developing a rainfall-runoff and reservoir model is to provide an analysis tool for hydrological engineers in order to forecast discharge of rivers and to accomplish reservoir operations easily and accurately. In this study, based on the short-term rainfall-runoff storage function model which has gained popularity for real time flood forecast in practical water management affairs, a long-term runoff model was developed for the improvement of the calculation method of effective rainfall and percolation at the infiltration area. Annual discharge was simulated for three dam watersheds(Andong, Hapcheon, Milyang) in Nakdong River basin to analyze the accuracy of the developed model and compare it to SSARR model, which is used as the long-term runoff model in current practical water management affairs. As the result of the comparison of hydrographs, SSARR model showed relatively better results. However, it is possible for the developed model to simulate reliable long-term runoff using relatively little available data and is useful for hydrological engineers in practical affairs.

Stochastic Continuous Storage Function Model with Ensemble Kalman Filtering (II) : Application and Verification (앙상블 칼만필터를 연계한 추계학적 연속형 저류함수모형 (II) : - 적용 및 검증 -)

  • Lee, Byong-Ju;Bae, Deg-Hyo;Shamir, Eylon
    • Journal of Korea Water Resources Association
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    • v.42 no.11
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    • pp.963-972
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    • 2009
  • The objective of this study is to evaluate an application of stochastic continuous storage function model with ensemble Kalman filter technique. The case study is performed at the upstream basin of Jibo streamflow gauge including Andong and Imha dam. Test period is for the rainy season during 2006 and 2007. Long term runoff analysis is feasible in the case of using deterministic model. Ensemble members for input data and parameters are generated using Monte Carlo simulation for the purpose of applying ensemble Kalman filter technique. The cumulative absolute errors of stochastic model to the deterministic one are improved for the amount of 17.5 %, 18.3 % and more than 40.0 % for Andong dam, Imha dam and Jibo station, respectively. The results indicate that the stochastic model improves the accuracy of the simulated discharge considerably.