Abstract
Precipitation projections from regional climate models in West Africa are attributed with significant biases with respect to the observed. This study aims at evaluating of six methods of precipitation bias correction on four RCM (CCLM, CRCM, RACMO and REMO) outputs in the Ouémé basin. The bias correction methods used are classified into three namely: the Delta approach, the Linear Scaling method and the quantile approaches. Corrected and uncorrected RCM precipitation data were compared with the observed using Mean Absolute Error (MAE) and Root Mean Square error (RMSE). The findings showed that raw outputs of regional climate models (RCMs) are characterized with several biases. In general, the models overestimate precipitation. For daily precipitation correction, the quantile approaches assuming a gamma distribution for daily precipitation were not able to reduce the biases of precipitation. The empirical quantile mapping and the adjusted quantile mapping are the most effective in correcting the biases of daily precipitation. Thus the adjusted quantile mapping can be used to correct biases of precipitation projections for modeling the future availability of water resources.
Highlights
Managing future fresh water resources under a changing climate with vastly uncertain future atmospheric greenhouse gas emissions is a daunting challenge facing human society today
We evaluated the ability of six daily precipitation bias correction methods in reducing the biases of four regional climate models outputs
Delta approach remains powerful to remove the bias of precipitation in the mean but it overestimates the number of dry days
Summary
Managing future fresh water resources under a changing climate with vastly uncertain future atmospheric greenhouse gas emissions is a daunting challenge facing human society today. Several researchers demonstrated that raw output from regional climate models (RCMs) cannot be used directly as input for impact models because of systematic bias [4,5,6,7,8]. These errors originate from different sources like (1) errors transferred from GCMs to RCMs (boundary problem), (2) insufficiently resolved surface properties (like orography) and (3) errors due to numeric resolutions and parameterization [9,10,11,12,13]. The RCMs errors depend on simulated variables and may be large for Yèkambèssoun N’Tcha M’Po et al.: Comparison of Daily Precipitation Bias Correction Methods
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
