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An Ensemble Flood Simulation for the Dam Preliminary Release Operation in Japan Using JMA-NHM Rainfall

Author(s): Kenichiro Kobayashi; Shigenori Otsuka; Kazuo Saito

Linked Author(s): Kenichiro Kobayashi

Keywords: Ensemble weather forecasting; Ensemble flood forecasting; Distributed rainfall-runoff model; JMA-NHM; Kasahori dam catchment Niigat

Abstract: The paper deals with a study on a short-date ensemble flood forecasting specifically for usual small dam catchments in Japan. The numerical ensemble rainfalls simulated by the Japan Meteorological Agency – Nonhydrostatic Model (JMA-NHM) are used as the inputs to a rainfall-runoff model for predicting river discharge into a dam. The spatial resolution of the weather ensemble simulations by JMA-NHM is 2 km, thus it has a potential to be used for even small -scale Japanese dam catchments (less than 100km2 area). The Kasahori dam catchment which size is approximately 70 km2 is selected as the application site of the ensemble flood simulations since the dam catchment experienced a historically rare rainfall/flood event on July 2011. Firstly, the rainfall over the Kasahori dam c atchment during the flood period is analyzed with the ground rainfall, JMA Radar-Composite and JMA Radar-AMeDAS analysis data. Then, a distributed rainfall -runoff model is applied to the Kasahori dam catchment. The runoff-model parameter is calibrated with the Radar-Composite rainfall. Afterwards, the ensemble rainfalls by the JMA-NHM are given to the runoff model. The ensemble inflow discharges to the Kasahori dam are compared with the observed inflow. As a result, a simulated discharge of 11 ensemble members succeeds in reproducing the Kasahori dam 1st peak inflow at the same level magnitude with the observation, though the simulated peak is 2 hours ahead of the observed peak. All the 11 ensemble discharges predict the values more than the flood discharge 140 m3/s, a threshold value to decide the preliminary release from the dam. The result indicates a good potential of the ensemble flood forecasting even for small dam catchments.

DOI:

Year: 2013

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