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http://dx.doi.org/10.3741/JKWRA.2019.52.5.337

Selection framework of representative general circulation models using the selected best bias correction method  

Song, Young Hoon (Department of Civil Engineering, Seoul National University of Science and Technology)
Chung, Eun-Sung (Department of Civil Engineering, Seoul National University of Science and Technology)
Sung, Jang Hyun (Han River Flood Control Office, Ministry of Environment)
Publication Information
Journal of Korea Water Resources Association / v.52, no.5, 2019 , pp. 337-347 More about this Journal
Abstract
This study proposes the framework to select the representative general circulation model (GCM) for climate change projection. The grid-based results of GCMs were transformed to all considered meteorological stations using inverse distance weighted (IDW) method and its results were compared to the observed precipitation. Six quantile mapping methods and random forest method were used to correct the bias between GCM's and the observation data. Thus, the empirical quantile which belongs to non-parameteric transformation method was selected as a best bias correction method by comparing the measures of performance indicators. Then, one of the multi-criteria decision techniques, TOPSIS (Technique for Order of Preference by Ideal Solution), was used to find the representative GCM using the performances of four GCMs after the bias correction using empirical quantile method. As a result, GISS-E2-R was the best and followed by MIROC5, CSIRO-Mk3-6-0, and CCSM4. Because these results are limited several GCMs, different results will be expected if more GCM data considered.
Keywords
General circulation model (GCM); Quantile mapping; Random forest; Bias correction; TOPSIS (Technique for order of preference by similarity to ideal solution);
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