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

Kim, Kue Bum, Bray, Michaela ORCID: and Han, Dawel 2016. Exploration of optimal time steps for daily precipitation bias correction: a case study using a single grid of RCM on the Exe River in Southwest England. Hydrological Sciences Journal 61 (2) , pp. 289-301. 10.1080/02626667.2015.1027207

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: Q Science > QE Geology
T Technology > TC Hydraulic engineering. Ocean engineering
Uncontrolled Keywords: regional climate model, bias correction, quantile mapping, digital filter, AIC
Publisher: Taylor & Francis
ISSN: 0262-6667
Date of Acceptance: 30 January 2015
Last Modified: 28 Oct 2022 09:00

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