Articles | Volume 20, issue 5
Hydrol. Earth Syst. Sci., 20, 2019–2034, 2016
Hydrol. Earth Syst. Sci., 20, 2019–2034, 2016

Research article 17 May 2016

Research article | 17 May 2016

Precipitation ensembles conforming to natural variations derived from a regional climate model using a new bias correction scheme

Kue Bum Kim et al.

Related authors

Estimation of rainfall erosivity based on WRF-derived raindrop size distributions
Qiang Dai, Jingxuan Zhu, Shuliang Zhang, Shaonan Zhu, Dawei Han, and Guonian Lv
Hydrol. Earth Syst. Sci., 24, 5407–5422,,, 2020
Short summary
A Framework for Automatic Calibration of SWMM Considering Input Uncertainty
Xichao Gao, Zhiyong Yang, Dawei Han, Guoru Huang, and Qian Zhu
Hydrol. Earth Syst. Sci. Discuss.,,, 2020
Manuscript not accepted for further review
Short summary
Soil moisture sensor network design for hydrological applications
Lu Zhuo, Qiang Dai, Binru Zhao, and Dawei Han
Hydrol. Earth Syst. Sci., 24, 2577–2591,,, 2020
Short summary
Preface: Advances in flood risk assessment and management
Cristina Prieto, Dhruvesh Patel, and Dawei Han
Nat. Hazards Earth Syst. Sci., 20, 1045–1048,,, 2020
Assessment of simulated soil moisture from WRF Noah, Noah-MP, and CLM land surface schemes for landslide hazard application
Lu Zhuo, Qiang Dai, Dawei Han, Ningsheng Chen, and Binru Zhao
Hydrol. Earth Syst. Sci., 23, 4199–4218,,, 2019
Short summary

Related subject area

Subject: Hydrometeorology | Techniques and Approaches: Stochastic approaches
Assessment of meteorological extremes using a synoptic weather generator and a downscaling model based on analogues
Damien Raynaud, Benoit Hingray, Guillaume Evin, Anne-Catherine Favre, and Jérémy Chardon
Hydrol. Earth Syst. Sci., 24, 4339–4352,,, 2020
Short summary
A standardized index for assessing sub-monthly compound dry and hot conditions
Jun Li, Zhaoli Wang, Xushu Wu, Jakob Zscheischler, Shenglian Guo, and Xiaohong Chen
Hydrol. Earth Syst. Sci. Discuss.,,, 2020
Revised manuscript accepted for HESS
Short summary
A new discrete multiplicative random cascade model for downscaling intermittent rainfall fields
Marc Schleiss
Hydrol. Earth Syst. Sci., 24, 3699–3723,,, 2020
Short summary
Modelling rainfall with a Bartlett–Lewis process: new developments
Christian Onof and Li-Pen Wang
Hydrol. Earth Syst. Sci., 24, 2791–2815,,, 2020
Short summary
Nonstationary stochastic rain type generation: accounting for climate drivers
Lionel Benoit, Mathieu Vrac, and Gregoire Mariethoz
Hydrol. Earth Syst. Sci., 24, 2841–2854,,, 2020
Short summary

Cited articles

Addor, N. and Fischer, E. M.: The influence of natural variability and interpolation errors on bias characterization in RCM simulations, J. Geophys. Res.-Atmos., 120, 10180–10195,, 2015.
Arnell, N. W., Liu, C., Compagnucci, R. da Cunha, L., Hanaki, K., Howe, C., Mailu, G., Shiklomanov, I., and Stakhiv, E.: Hydrology and water resources, in: Climate Change 2001: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Third Assessment Report of the Intergovernmental Panel on Climate Change, edited by: McCarthy, J. J., Canziani, O. F., Leary, N. A., Dokken, D. J., and White, K. S., Cambridge University Press, Cambridge, 191–233, 2001.
Baigorria, G. A., Jones, J. W., Shin, D.-W., Mishra, A., and O'Brien, J. J.: Assessing uncertainties in crop model simulations using daily bias-corrected Regional Circulation Model outputs, Clim. Res., 34, 211–222,, 2007.
Bates, B., Kundzewicz, Z. W., Wu, S., and Palutikof, J.: Climate change and water, Intergovernmental Panel on Climate Change (IPCC), 2008.
Block, P. J., Souza Filho, F. A., Sun, L., and Kwon, H. H.: A Streamflow Forecasting Framework using Multiple Climate and Hydrological Models1, J. Am. Water Resour. Assoc., 45, 828–843, 2009.
Short summary
A primary advantage of using model ensembles for climate change impact studies is to represent the uncertainties associated with models through the ensemble spread. Currently, most of the conventional bias correction methods adjust all the ensemble members to one reference observation. As a result, the ensemble spread is degraded during bias correction. However the proposed method is able to correct the bias and conform to the ensemble spread so that the ensemble information can be better used.