Abstract
The study of impact of climate change on water resources is significantly increasing to evaluate its effect in regional or local scale. Regional Climate Models (RCMs) are major physical functioning tools to downscale and simulate future climate projections under various scenarios. However, most RCMs’ products present of uncertainties and generate systematic and random biases. A need of post-processing, bias correction, is inevitable to produce a dependability RCM’s products. It is obvious that bias correction performance is location dependent. In this study, a widely used Quantile Mapping (QM) technique is applied over upper Ping River basin to correct daily rainfall from MM5-RCM. Different distributions between transformation of QM are tested. Mixed distribution between Bernoulli-Weibull, Bernoulli-Gamma and non-parametric transformation are performed. The derived transformation with mixed distributions are included Bernoulli distribution in order to consider the probability of number of rain and no-rain days. Rather than predetermined distribution function, non-parametric transformation might also yield a likely better estimation due to it freedom of fitting distribution. Overall, bias correction methods are generally improved and reduce climate model output bias which is needed to be done before quantifying any impacts of climate change study.
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