• Title/Summary/Keyword: perturbed parameter

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Comparative assessment and uncertainty analysis of ensemble-based hydrologic data assimilation using airGRdatassim (airGRdatassim을 이용한 앙상블 기반 수문자료동화 기법의 비교 및 불확실성 평가)

  • Lee, Garim;Lee, Songhee;Kim, Bomi;Woo, Dong Kook;Noh, Seong Jin
    • Journal of Korea Water Resources Association
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    • v.55 no.10
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    • pp.761-774
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    • 2022
  • Accurate hydrologic prediction is essential to analyze the effects of drought, flood, and climate change on flow rates, water quality, and ecosystems. Disentangling the uncertainty of the hydrological model is one of the important issues in hydrology and water resources research. Hydrologic data assimilation (DA), a technique that updates the status or parameters of a hydrological model to produce the most likely estimates of the initial conditions of the model, is one of the ways to minimize uncertainty in hydrological simulations and improve predictive accuracy. In this study, the two ensemble-based sequential DA techniques, ensemble Kalman filter, and particle filter are comparatively analyzed for the daily discharge simulation at the Yongdam catchment using airGRdatassim. The results showed that the values of Kling-Gupta efficiency (KGE) were improved from 0.799 in the open loop simulation to 0.826 in the ensemble Kalman filter and to 0.933 in the particle filter. In addition, we analyzed the effects of hyper-parameters related to the data assimilation methods such as precipitation and potential evaporation forcing error parameters and selection of perturbed and updated states. For the case of forcing error conditions, the particle filter was superior to the ensemble in terms of the KGE index. The size of the optimal forcing noise was relatively smaller in the particle filter compared to the ensemble Kalman filter. In addition, with more state variables included in the updating step, performance of data assimilation improved, implicating that adequate selection of updating states can be considered as a hyper-parameter. The simulation experiments in this study implied that DA hyper-parameters needed to be carefully optimized to exploit the potential of DA methods.