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

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

Research output: Contribution to journalArticle

1 Citation (Scopus)

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 the successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved that they are fit for precipitation modeling because of their flexibility. Hence, the application of a two-shape parameter distribution will improve the performance of the quantile mapping method in the bias correction of precipitation data. In this study, the applicability and appropriateness of two-shape parameter distribution models are examined in quantile mapping, for a 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 do lead to better performances in reproducing the 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
Article number1475
JournalWater (Switzerland)
Volume11
Issue number7
DOIs
Publication statusPublished - 2019 Jul 1

Fingerprint

Climate Change
Precipitation (meteorology)
probability distribution
Climate change
Probability distributions
climate change
case studies
Climate
trend
Climate models
methodology
climate models
mapping method
distribution
climate
performance
frequency analysis
return period
climate modeling
flexibility

All Science Journal Classification (ASJC) codes

  • Biochemistry
  • Geography, Planning and Development
  • Aquatic Science
  • Water Science and Technology

Cite this

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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 the successful implementation of quantile mapping. Probability distribution models with two shape parameters have proved that they are fit for precipitation modeling because of their flexibility. Hence, the application of a two-shape parameter distribution will improve the performance of the quantile mapping method in the bias correction of precipitation data. In this study, the applicability and appropriateness of two-shape parameter distribution models are examined in quantile mapping, for a 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 do lead to better performances in reproducing the 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.",
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Probability distributions for a quantile mapping technique for a bias correction of precipitation data : A case study to precipitation data under climate change. / Heo, Jun Haeng; Ahn, Hyunjun; Shin, Ju Young; Kjeldsen, Thomas Rodding; Jeong, Changsam.

In: Water (Switzerland), Vol. 11, No. 7, 1475, 01.07.2019.

Research output: Contribution to journalArticle

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