Satoshi Watanabe, Masafumi Yamada, Shiori Abe, Misako Hatono
Received 2020/06/01, Accepted 2020/08/11, Published 2020/09/11

Satoshi Watanabe¹, Masafumi Yamada², Shiori Abe³, Misako Hatono⁴

1) School of Engineering, the University of Tokyo, Japan
2) Disaster Prevention Research Institute, Kyoto University, Japan
3) Mitsui Consultants, co., Ltd., Japan
4) Graduate School of Advanced Science and Engineering, Hiroshima University, Japan

Design rainfall depth, which is a fundamental index used in river planning, was estimated by rainfall obtained from super-ensemble simulations with bias correction, and the future change under 4 degree warming was projected. The modifications of existing bias correction methods were proposed to resolve the issue of overfitting and gap in size between reference and super-ensemble simulation data. A bias correction approach considering the bias between the historical experiment, the reference data, and the change between the historical and future experiments separately was defined as two-pass bias correction. The two-pass bias correction was performed with a moving window method that calculated moving average for time period and rank-order statistics. The result indicated that the approach proposed in this study estimates the design rainfall depth with a small error compared to that calculated without the moving window. The moving window method effectively resolves the issue of overfitting. The projection indicated that the range of projection among sea-surface temperature (SST) patterns is equivalent to 25% of the design rainfall depth for most basins and 60% for certain specific basins. The results indicate the importance of the appropriate bias correction and the consideration of range among the SST patterns for super-ensemble simulation data.

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