Comparison of bias correction methods for general circulation rainfall projections under CMIP6 climate change scenarios
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Abstract
The study conducted a comparison of three bias correction methods, namely linear scaling (LS), local intensity scaling (LOCI), and distribution mapping (DM). The CMhyd application was utilized to perform bias correction on rainfall datasets obtained from 11 global climate models. The observed rainfall data collected from 15 rain gauges in the southern region of Thailand between 1980 and 2014 were subsequently projected under climate change CMIP6 SSP5-8.5 scenario. Results revealed that the LS approach presented the most precise predictions in comparison to the observed data. Analysis of rainfall trends under climate change scenario using the Mann-Kendall trend test (MK) and Sen's slope (SS) at a 0.05 significance level revealed a substantial increase in annual rainfall at 8 out of 15 stations during the rainy season, and at 7 stations during dry season. The study area experienced a 0.60% increase in average rainfall under SSP5-8.5 during the near-future period of 2015-2039. This increase further rose to 5.87% during the mid-future period of 2040-2069 and reached 8.16% during the far-future period of 2070-2100.
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