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Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique

Department of Civil Engineering, Inha University, Incheon 22212, Korea
Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology(KICT), Goyang-Si 10223, Korea
Urban Disaster Prevention & Water Resource Research Center, Korea Research Institute for Human Settlement(KRIHS), Sejong-si 30147, Korea
Author to whom correspondence should be addressed.
Water 2019, 11(4), 850;
Received: 6 March 2019 / Revised: 18 April 2019 / Accepted: 19 April 2019 / Published: 23 April 2019
PDF [8125 KB, uploaded 26 April 2019]


The purpose of this study is to reduce the uncertainty in the generation of rainfall data and runoff simulations. We propose a blending technique using a rainfall ensemble and runoff simulation. To create rainfall ensembles, the probabilistic perturbation method was added to the deterministic raw radar rainfall data. Then, we used three rainfall-runoff models that use rainfall ensembles as input data to perform a runoff analysis: The tank model, storage function model, and streamflow synthesis and reservoir regulation model. The generated rainfall ensembles have increased uncertainty when the radar is underestimated, due to rainfall intensity and topographical effects. To confirm the uncertainty, 100 ensembles were created. The mean error between radar rainfall and ground rainfall was approximately 1.808–3.354 dBR. We derived a runoff hydrograph with greatly reduced uncertainty by applying the blending technique to the runoff simulation results and found that uncertainty is improved by more than 10%. The applicability of the method was confirmed by solving the problem of uncertainty in the use of rainfall radar data and runoff models. View Full-Text
Keywords: rainfall ensemble; blending technique; runoff analysis; uncertainty rainfall ensemble; blending technique; runoff analysis; uncertainty

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Lee, M.; Kang, N.; Joo, H.; Kim, H.S.; Kim, S.; Lee, J. Hydrological Modeling Approach Using Radar-Rainfall Ensemble and Multi-Runoff-Model Blending Technique. Water 2019, 11, 850.

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