Exploration of optimal time steps for daily precipitation bias correction: a case study using a single grid of RCM on the River Exe in southwest England

Kue Bum Kim, Michaela Bray, Dawei Han

Research output: Contribution to journalArticle (Academic Journal)peer-review

3 Citations (Scopus)
273 Downloads (Pure)

Abstract

Bias correction is a necessary post-processing procedure in order to use Regional Climate Model (RCM) simulated local climate variables as the input data for hydrological models due to systematic errors of RCMs. Most of present bias correction methods adjust statistical properties between observed and simulated data based on a predefined duration (e.g., a month or a season). However, there is a lack of analysis about the optimal period for bias correction. This study has attempted to address the question whether there is an optimal number for bias correction groups (i.e. optimal bias correction period). To explore this optimal number we used a catchment in southwest England with the regional climate model HadRM3 precipitation data. The proposed methodology uses only one grid of RCM in the Exe catchment, one emission scenario (A1B) and one-member (Q0) among 11-members of HadRM3. We tried 13 different bias correction periods from 3-day to 360-day (i.e., the whole one year) correction using the quantile mapping method. After the bias correction a low pass filter is used to remove the high frequencies (i.e., noise) followed by estimating Akaike’s information criterion. For the case study catchment with the regional climate model HadRM3 precipitation, the results showed that about 8-day bias correction period is the best. We hope this preliminary study about the optimum number of bias correction period for daily RCM precipitation will stimulate more research activities to improve the methodology with different climatic conditions so that more experience and knowledge could be obtained. Future efforts on several unsolved problems have been suggested such as how strong the filter should be and the impact of the number of bias correction groups on river flow simulations.
Original languageEnglish
Pages (from-to)289-301
Number of pages13
JournalHydrological Sciences Journal
Volume61
Issue number2
Early online date17 Dec 2015
DOIs
Publication statusPublished - Feb 2016

Keywords

  • regional climate model
  • bias correction
  • quantile mapping
  • digital filter
  • AIC

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