Abstract
Double gamma quantile mapping (DGQM) can outperform single gamma quantile mapping (SGQM) for bias correction of global circulation models (GCMs) using two gamma functions for two segments based on a specific quantile. However, there are two ambiguous points, the use of specific quantile and only Gamma probability distribution function. Therefore, this study introduced a flexible dividing point, δ (%), which can be adjusted to the regionally observed values at the station and consider the combination of various probability distributions for the two separate segments (e.g., Weibull, lognormal, and Gamma). The newly proposed method, flexible double distribution quantile mapping (F-DDQM), was employed to correct the bias of 8 GCMs of Coupled Model Intercomparison Project Phase 6 (CMIP6) at 22 stations in South Korea. The results clearly show a higher performance of F-DDQM than DGQM and Flexible-DGQM (F-DGQM) by 27% and 19%, respectively, in root mean square error. The F-DGQM also performed better in replicating probability distribution, spatial variability and extremes of observed precipitation than other methods. This study contributes to improving the bias correction method for better projection of extreme values.
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Acknowledgements
This work was supported by the Korea Agency for Infrastructure Technology Advancement grant (22CTAP-C163540-02) funded by the Ministry of Land, Infrastructure and Transport of Korea. This study is also supported by the National Research Foundation of Korea (2021R1A2C20056990).
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All authors contributed to the study conception and design. Marterial preparation, data collection and analysis were performed by Young Hoon Song. The first draft of the manuscript was written by Young Hoon song, and Eun Sung Chung. The review and editing was performed by Eun Sung Chung and Shamsuddin Shahid. All authors commented on previous versions of the manuscript.
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Song, Y.H., Chung, ES. & Shahid, S. The New Bias Correction Method for Daily Extremes Precipitation over South Korea using CMIP6 GCMs. Water Resour Manage 36, 5977–5997 (2022). https://doi.org/10.1007/s11269-022-03338-3
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DOI: https://doi.org/10.1007/s11269-022-03338-3