Journal of Korean Society on Water Environment (한국물환경학회지)

Korean Society on Water Environment (한국물환경학회)
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Application of Bayesian Approach to Parameter Estimation of TANK Model: Comparison of MCMC and GLUE Methods

TANK 모형의 매개변수 추정을 위한 베이지안 접근법의 적용: MCMC 및 GLUE 방법의 비교

  • Kim, Ryoungeun (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Won, Jeongeun (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Choi, Jeonghyeon (Division of Earth Environmental System Science (Major of Environmental Engineering), Pukyong National University) ;
  • Lee, Okjeong (Department of Environmental Engineering, Pukyong National University) ;
  • Kim, Sangdan (Department of Environmental Engineering, Pukyong National University)
  • 김령은 (부경대학교 지구환경시스템과학부 (환경공학전공)) ;
  • 원정은 (부경대학교 지구환경시스템과학부 (환경공학전공)) ;
  • 최정현 (부경대학교 지구환경시스템과학부 (환경공학전공)) ;
  • 이옥정 (부경대학교 환경공학과) ;
  • 김상단 (부경대학교 환경공학과)
  • Received : 2020.05.06
  • Accepted : 2020.07.09
  • Published : 2020.07.30
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Abstract

The Bayesian approach can be used to estimate hydrologic model parameters from the prior expert knowledge about the parameter values and the observed data. The purpose of this study was to compare the performance of the two Bayesian methods, the Metropolis-Hastings (MH) algorithm and the Generalized Likelihood Uncertainty Estimation (GLUE) method. These two methods were applied to the TANK model, a hydrological model comprising 13 parameters, to examine the uncertainty of the parameters of the model. The TANK model comprises a combination of multiple reservoir-type virtual vessels with orifice-type outlets and implements a common major hydrological process using the runoff calculations that convert the rainfall to the flow. As a result of the application to the Nam River A watershed, the two Bayesian methods yielded similar flow simulation results even though the parameter estimates obtained by the two methods were of somewhat different values. Both methods ensure the model's prediction accuracy even when the observed flow data available for parameter estimation is limited. However, the prediction accuracy of the model using the MH algorithm yielded slightly better results than that of the GLUE method. The flow duration curve calculated using the limited observed flow data showed that the marginal reliability is secured from the perspective of practical application.

Keywords

References

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