Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 4.256 IF 4.256
  • IF 5-year value: 4.819 IF 5-year 4.819
  • CiteScore value: 4.10 CiteScore 4.10
  • SNIP value: 1.412 SNIP 1.412
  • SJR value: 2.023 SJR 2.023
  • IPP value: 3.97 IPP 3.97
  • h5-index value: 58 h5-index 58
  • Scimago H index value: 99 Scimago H index 99
Volume 19, issue 2 | Copyright
Hydrol. Earth Syst. Sci., 19, 711-728, 2015
https://doi.org/10.5194/hess-19-711-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 Feb 2015

Research article | 04 Feb 2015

How does bias correction of regional climate model precipitation affect modelled runoff?

J. Teng1, N. J. Potter1, F. H. S. Chiew1, L. Zhang1, B. Wang1, J. Vaze1, and J. P. Evans2 J. Teng et al.
  • 1CSIRO Land and Water Flagship, Canberra, Australia
  • 2Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, Australia

Abstract. Many studies bias correct daily precipitation from climate models to match the observed precipitation statistics, and the bias corrected data are then used for various modelling applications. This paper presents a review of recent methods used to bias correct precipitation from regional climate models (RCMs). The paper then assesses four bias correction methods applied to the weather research and forecasting (WRF) model simulated precipitation, and the follow-on impact on modelled runoff for eight catchments in southeast Australia. Overall, the best results are produced by either quantile mapping or a newly proposed two-state gamma distribution mapping method. However, the differences between the methods are small in the modelling experiments here (and as reported in the literature), mainly due to the substantial corrections required and inconsistent errors over time (non-stationarity). The errors in bias corrected precipitation are typically amplified in modelled runoff. The tested methods cannot overcome limitations of the RCM in simulating precipitation sequence, which affects runoff generation. Results further show that whereas bias correction does not seem to alter change signals in precipitation means, it can introduce additional uncertainty to change signals in high precipitation amounts and, consequently, in runoff. Future climate change impact studies need to take this into account when deciding whether to use raw or bias corrected RCM results. Nevertheless, RCMs will continue to improve and will become increasingly useful for hydrological applications as the bias in RCM simulations reduces.

Publications Copernicus
Download
Short summary
This paper assesses four bias correction methods applied to RCM-simulated precipitation, and their follow-on impact on modelled runoff. The differences between the methods are small, mainly due to the substantial corrections required and inconsistent errors over time. The methods cannot overcome limitations of the RCM in simulating precipitation sequence, which affects runoff generation. Furthermore, bias correction can introduce additional uncertainty to change signals in modelled runoff.
This paper assesses four bias correction methods applied to RCM-simulated precipitation, and...
Citation
Share