Journal cover Journal topic
Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
15 Feb 2016
Review status
This discussion paper is a preprint. It has been under review for the journal Hydrology and Earth System Sciences (HESS). The manuscript was not accepted for further review after discussion.
Bias correction schemes for CMORPH satellite rainfall estimates in the Zambezi River Basin
W. Gumindogaa,b, T. H. M. Rientjesa, A. T. Hailec, H. Makurirab, and P. Reggianid aFaculty ITC, University of Twente, The Netherlands
bUniversity of Zimbabwe, Civil Eng. Department Box MP 167 Mt Pleasant, Harare, Zimbabwe
cInternational Water Management Institute (IWMI), Ethiopia
dUniversity of Siegen, Germany
Abstract. Obtaining reliable records of rainfall from satellite rainfall estimates (SREs) is a challenge as SREs are an indirect rainfall estimate from visible, infrared (IR), and/or microwave (MW) based information of cloud properties. SREs also contain inherent biases which exaggerate or underestimate actual rainfall values hence the need to apply bias correction methods to improve accuracies. We evaluate the performance of five bias correction schemes for CMORPH satellite-based rainfall estimates. We use 54 raingauge stations in the Zambezi Basin for the period 1998–2013 for comparison and correction. Analysis shows that SREs better match to gauged estimates in the Upper Zambezi Basin than the Lower and Middle Zambezi basins but performance is not clearly related to elevation. Findings indicate that rainfall in the Upper Zambezi Basin is best estimated by an additive bias correction scheme (Distribution transformation). The linear based (Spatio-temporal) bias correction scheme successfully corrected the daily mean of CMORPH estimates for 70 % of the stations and also was most effective in reducing the rainfall bias. The nonlinear bias correction schemes (Power transform and the Quantile based empirical-statistical error correction method) proved most effective in reproducing the rainfall totals. Analyses through bias correction indicate that bias of CMORPH estimates has elevation and seasonality tendencies across the Zambezi river basin area of large scale.

Citation: Gumindoga, W., Rientjes, T. H. M., Haile, A. T., Makurira, H., and Reggiani, P.: Bias correction schemes for CMORPH satellite rainfall estimates in the Zambezi River Basin, Hydrol. Earth Syst. Sci. Discuss.,, 2016.
W. Gumindoga et al.
W. Gumindoga et al.


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