[A-HW18] Hydrology & Water Environment

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

*Kazuki Sakikawa¹, Hidetaka Chikamori¹, Ryoji Kudo¹, Keita Maruo² (1.Graduate School of Environmental and Life Science, Okayama University, 2.The National Agriculture and Food Research Organization)

Keywords:Extreme analysis, Hydrological statistics, Bias correction method, Meta-statistical extreme value distribution

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.