[A-HW18] 水循環・水環境

[AHW18-P13] Bias correction of climate change prediction data based on Simplified Meta-statistical Extreme Value distribution

*崎川 和起¹、近森 秀高¹、工藤 亮治¹、丸尾 啓太² (1.岡山大学大学院環境生命科学研究科、2.農業・食品産業技術総合研究機構)

キーワード：極値解析、水文統計、バイアス補正手法、メタ統計的極値分布

Correcting biases for extreme values in precipitation outputs by poor observed data is key for hydrological applications and risk management depending on climate models. However, the sample size of extreme rainfall value is so small that the estimated return level and return period strongly fluctuate even when a few extreme-size minima or maxima are included by a change in a target duration for the analysis. That often disturbs the correct estimation of secular change in return level and return period of rainfall. Therefore, the result of traditional methods based on extreme theory by poor rainfall data may include a large uncertainty. This study presents a new bias correction method based on the Simplified Meta-statistical Extreme Value (SMEV) approach which enables us to estimate stably. SMEV approach provides a robust framework for frequency analyses of extremes emerging from multiple underlying processes and represents a practical tool for computationally efficient sensitivity analyses, explanatory models, and climate projections. The results indicated that the new bias method estimates corrected values with errors as small as those of the Generalized Extreme Value (GEV) method, compared to observed data, and it provides the fluctuation range of corrected values compared with the Annual Maximum Series (AMS) method. Therefore, the new bias correction method with the SMEV approach provides a robust bias correction for extreme rainfall.