Articles | Volume 20, issue 3
Nat. Hazards Earth Syst. Sci., 20, 755–770, 2020
https://doi.org/10.5194/nhess-20-755-2020
Nat. Hazards Earth Syst. Sci., 20, 755–770, 2020
https://doi.org/10.5194/nhess-20-755-2020

Research article 23 Mar 2020

Research article | 23 Mar 2020

Ensemble flood simulation for a small dam catchment in Japan using nonhydrostatic model rainfalls – Part 2: Flood forecasting using 1600-member 4D-EnVar-predicted rainfalls

Kenichiro Kobayashi et al.

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Cited articles

Cloke, H. L. and Pappenberger, F.: Ensemble flood forecasting: A review, J. Hydrol., 375, 613–626, https://doi.org/10.1016/j.jhydrol.2009.06.005, 2009. 
Doucet, A., de Freitas, N., and Gordon, N. J.: Sequential Monte Carlo Methods in Practice, Springer-Verlag, New York, USA, 2001. 
Duc, L. and Saito, K.: A 4DEnVAR data assimilation system without vertical localization using the K computer, in: Japan Geoscience Union meeting, 20–25 May 2017, Chiba, Japan, AAS12-P04, 2017. 
Duc, L. and Saito, K.: Verification in the presence of observation errors: Bayesian point of view, Q. J. Roy. Meteorol. Soc., 144, 1063–1090, https://doi.org/10.1002/qj.3275, 2018. 
Fletcher, S. J.: Data Assimilation for the Geosciences: From Theory to Application, Elsevier, Amsterdam, the Netherlands, 2017. 
Short summary
The feasibility of flood forecasting with 1600 rainfall forecasts was investigated. The rainfall forecasts were obtained from an advanced data assimilation system. The high probability of flood occurrence was predicted, which is not possible by the single deterministic forecast. The necessity of emergency flood operation was shown with a long leading time. This suggests that it is worth investing in increasing numbers of meteorological ensembles to improve flood forecasting.
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