An improved bias correction scheme based on comparative precipitation characteristics. Issue 9 (17th October 2014)

• Record Type: Journal Article
• Authors: Kim, Kue Bum; Bray, Michaela; Han, Dawei
• Is Part Of: Hydrological processes. Volume 29:Issue 9(2015:Apr.)
• Page Start: 2258
• Page End: 2266
• Publication Date: 2014-10-17
• Subjects: Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.10366
• Languages: English
• ISSNs: 0885-6087

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>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 owing to systematic errors of RCMs. Most of present bias correction methods adjust statistical properties between observed and simulated data on the basis of calendar periods, e.g. month or season. However, this matching statistic is only a necessary condition, not a sufficient condition, as temporal distribution of the precipitation between observed and simulated data is ignored. This study suggests an improved bias correction scheme that considers not only statistical properties but also the temporal distribution between the time series of observed and modelled data. The ratio of the observed precipitation to simulated precipitation is used to compare the behaviour between the observed and modelled precipitation data, and three criteria are proposed when dividing bias correction periods: (1) underestimation of precipitation, (2) stability of /underestimation of precipitation, (2) stability of precipitation ratio and (3) oscillation of precipitation ratio. The results show that the output of the proposed bias correction method follows the trend of the observed precipitation better than that of the conventional bias correction method. This study indicates that temporal distribution should not be ignored when choosing a comparison period for bias correction. However, the study is only a preliminary attempt to address this important issue, and we hope it will stimulate more research activities to improve the methodology. Future efforts on several unsolved problems have been suggested such as how to find out the optimal group number to avoid the overfitting and underfitting conditions. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>