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.
| Published in | Hydrology (Volume 4, Issue 6) |
| DOI | 10.11648/j.hyd.20160406.11 |
| Page(s) | 58-71 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2017. Published by Science Publishing Group |
Delta, Linear Scaling, Precipitation, Bias Correction Method, Ouémé, Benin
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APA Style
Yèkambèssoun N’Tcha M’Po, Agnidé Emmanuel Lawin, Ganiyu Titilope Oyerinde, Benjamin Kouassi Yao, Abel Akambi Afouda. (2017). Comparison of Daily Precipitation Bias Correction Methods Based on Four Regional Climate Model Outputs in Ouémé Basin, Benin. Hydrology, 4(6), 58-71. https://doi.org/10.11648/j.hyd.20160406.11
ACS Style
Yèkambèssoun N’Tcha M’Po; Agnidé Emmanuel Lawin; Ganiyu Titilope Oyerinde; Benjamin Kouassi Yao; Abel Akambi Afouda. Comparison of Daily Precipitation Bias Correction Methods Based on Four Regional Climate Model Outputs in Ouémé Basin, Benin. Hydrology. 2017, 4(6), 58-71. doi: 10.11648/j.hyd.20160406.11
AMA Style
Yèkambèssoun N’Tcha M’Po, Agnidé Emmanuel Lawin, Ganiyu Titilope Oyerinde, Benjamin Kouassi Yao, Abel Akambi Afouda. Comparison of Daily Precipitation Bias Correction Methods Based on Four Regional Climate Model Outputs in Ouémé Basin, Benin. Hydrology. 2017;4(6):58-71. doi: 10.11648/j.hyd.20160406.11
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Download@article{10.11648/j.hyd.20160406.11,
author = {Yèkambèssoun N’Tcha M’Po and Agnidé Emmanuel Lawin and Ganiyu Titilope Oyerinde and Benjamin Kouassi Yao and Abel Akambi Afouda},
title = {Comparison of Daily Precipitation Bias Correction Methods Based on Four Regional Climate Model Outputs in Ouémé Basin, Benin},
journal = {Hydrology},
volume = {4},
number = {6},
pages = {58-71},
doi = {10.11648/j.hyd.20160406.11},
url = {https://doi.org/10.11648/j.hyd.20160406.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.hyd.20160406.11},
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.},
year = {2017}
}
TY - JOUR T1 - Comparison of Daily Precipitation Bias Correction Methods Based on Four Regional Climate Model Outputs in Ouémé Basin, Benin AU - Yèkambèssoun N’Tcha M’Po AU - Agnidé Emmanuel Lawin AU - Ganiyu Titilope Oyerinde AU - Benjamin Kouassi Yao AU - Abel Akambi Afouda Y1 - 2017/01/07 PY - 2017 N1 - https://doi.org/10.11648/j.hyd.20160406.11 DO - 10.11648/j.hyd.20160406.11 T2 - Hydrology JF - Hydrology JO - Hydrology SP - 58 EP - 71 PB - Science Publishing Group SN - 2330-7617 UR - https://doi.org/10.11648/j.hyd.20160406.11 AB - 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. VL - 4 IS - 6 ER -
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