Quantification of Parameter Uncertainty in Distributed Rainfall-Runoff Modeling

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  • 分布型降雨流出モデルにおけるパラメータの不確実性の定量化

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In general, hydrological models have several (or a lot of) parameters that cannot be directly measured, which only are inferred by calibration procedure against a historical input-output data record. While the applications of automatic parameter estimation techniques have received considerable attention over the last decades, such classical methods have received criticism for their lack of rigor in handling with uncertainty in the parameter estimates. This work addresses the calibration of the distributed rainfall-runoff model KsEdgeFC2D, the quantification of parameter uncertainty and its effect on the prediction of streamflow for Kamishiiba catchment (211km2). In this study, to analyze the propagation of parameter uncertainty into prediction, we employ the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm. Moreover, we compare SCEM-UA derived optimal parameter values to those estimated using deterministic SCE-UA method with three different objective functions to account for the structural stability of KsEdgFC2D model and to demonstrate the capability of the SCEM-UA algorithm to efficiently evolve to parameter posterior distribution.



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    departmental bulletin paper
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