Probability distributions for a quantile mapping technique for bias correction of precipitation data: A case study to precipitation data under climate change

Jun-Haeng Heo, Ju-Young Shin, Hyunjun Ahn, Thomas Kjeldsen, Changsam Jeong

Research output: Contribution to journalArticle

Abstract

The quantile mapping method is a bias correction method that leads to a good performance in terms of precipitation. Selecting an appropriate probability distribution model is essential for successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved they are fit for precipitation modeling because of their flexibility. Hence, application of a two-shape parameter distribution will improve the performance of the quantile mapping method in bias correction of precipitation data. In this study, the applicability and appropriateness of two-shape parameter distribution models are examined in quantile mapping for bias correction of simulated precipitation data from a climate model under a climate change scenario. Additionally, the impacts of distribution selection on the frequency analysis of future extreme precipitation from climate are investigated. Generalized Lindley, Burr XII, and Kappa distributions are used and their fits and appropriateness are compared to those of conventional distributions in a case study. Applications of two-shape parameter distributions lead to better performances in reproducing statistical characteristics of observed precipitation compared to those of conventional distributions. The Kappa distribution is considered the best distribution model as it can reproduce reliable spatial dependences of the quantile corresponding to a 100-year return period, unlike the gamma distribution.
Original languageEnglish
JournalWater
Publication statusAccepted/In press - 11 Jul 2019

Cite this

Probability distributions for a quantile mapping technique for bias correction of precipitation data: A case study to precipitation data under climate change. / Heo, Jun-Haeng; Shin, Ju-Young; Ahn, Hyunjun; Kjeldsen, Thomas; Jeong, Changsam.

In: Water, 11.07.2019.

Research output: Contribution to journalArticle

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