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Modified approach to reduce GCM bias in downscaled precipitation: A study in Ganga River Basin

Reanalysis data is widely used to develop predictor-predictand models, which are further used to downscale coarse gridded general circulation models (GCM) data at a local scale. However, large variability in the downscaled product using different GCMs is still a big challenge. The first objective of...

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Main Authors: Sharma, C., Ojha, C.S.P., Shukla, A.K., Pham, Q.B., Linh, N.T.T., Fai, C.M., Loc, H.H., Dung, T.D.
Format: Article
Language:English
Published: 2020
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spelling my.uniten.dspace-128472020-07-07T04:26:43Z Modified approach to reduce GCM bias in downscaled precipitation: A study in Ganga River Basin Sharma, C. Ojha, C.S.P. Shukla, A.K. Pham, Q.B. Linh, N.T.T. Fai, C.M. Loc, H.H. Dung, T.D. Reanalysis data is widely used to develop predictor-predictand models, which are further used to downscale coarse gridded general circulation models (GCM) data at a local scale. However, large variability in the downscaled product using different GCMs is still a big challenge. The first objective of this study was to assess the performance of reanalysis data to downscale precipitation using different GCMs. High bias in downscaled precipitation was observed using different GCMs, so a different downscaling approach is proposed in which historical data of GCM was used to develop a predictor-predictand model. The earlier approach is termed "Re-Obs" and the proposed approach as "GCM-Obs". Both models were assessed using mathematical derivation and generated synthetic series. The intermodal bias in different GCMs downscaled precipitation using Re-Obs and GCM-Obs model was also checked. Coupled Model Inter-comparison Project-5 (CMIP5) data of ten different GCMs was used to downscale precipitation in different urbanized, rural, and forest regions in the Ganga river basin. Different measures were used to represent the relative performances of one downscaling approach over other approach in terms of closeness of downscaled precipitation with observed precipitation and reduction of bias using different GCMs. The effect of GCM spatial resolution in downscaling was also checked. The model performance, convergence, and skill score were computed to assess the ability of GCM-Obs and Re-Obs models. The proposed GCM-Obs model was found better than Re-Obs model to statistically downscale GCM. It was observed that GCM-Obs model was able to reduce GCM-Observed and GCM-GCM bias in the downscaled precipitation in the Ganga river basin. © 2019 by the authors. 2020-02-03T03:27:14Z 2020-02-03T03:27:14Z 2019 Article 10.3390/w11102097 en
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
language English
description Reanalysis data is widely used to develop predictor-predictand models, which are further used to downscale coarse gridded general circulation models (GCM) data at a local scale. However, large variability in the downscaled product using different GCMs is still a big challenge. The first objective of this study was to assess the performance of reanalysis data to downscale precipitation using different GCMs. High bias in downscaled precipitation was observed using different GCMs, so a different downscaling approach is proposed in which historical data of GCM was used to develop a predictor-predictand model. The earlier approach is termed "Re-Obs" and the proposed approach as "GCM-Obs". Both models were assessed using mathematical derivation and generated synthetic series. The intermodal bias in different GCMs downscaled precipitation using Re-Obs and GCM-Obs model was also checked. Coupled Model Inter-comparison Project-5 (CMIP5) data of ten different GCMs was used to downscale precipitation in different urbanized, rural, and forest regions in the Ganga river basin. Different measures were used to represent the relative performances of one downscaling approach over other approach in terms of closeness of downscaled precipitation with observed precipitation and reduction of bias using different GCMs. The effect of GCM spatial resolution in downscaling was also checked. The model performance, convergence, and skill score were computed to assess the ability of GCM-Obs and Re-Obs models. The proposed GCM-Obs model was found better than Re-Obs model to statistically downscale GCM. It was observed that GCM-Obs model was able to reduce GCM-Observed and GCM-GCM bias in the downscaled precipitation in the Ganga river basin. © 2019 by the authors.
format Article
author Sharma, C.
Ojha, C.S.P.
Shukla, A.K.
Pham, Q.B.
Linh, N.T.T.
Fai, C.M.
Loc, H.H.
Dung, T.D.
spellingShingle Sharma, C.
Ojha, C.S.P.
Shukla, A.K.
Pham, Q.B.
Linh, N.T.T.
Fai, C.M.
Loc, H.H.
Dung, T.D.
Modified approach to reduce GCM bias in downscaled precipitation: A study in Ganga River Basin
author_facet Sharma, C.
Ojha, C.S.P.
Shukla, A.K.
Pham, Q.B.
Linh, N.T.T.
Fai, C.M.
Loc, H.H.
Dung, T.D.
author_sort Sharma, C.
title Modified approach to reduce GCM bias in downscaled precipitation: A study in Ganga River Basin
title_short Modified approach to reduce GCM bias in downscaled precipitation: A study in Ganga River Basin
title_full Modified approach to reduce GCM bias in downscaled precipitation: A study in Ganga River Basin
title_fullStr Modified approach to reduce GCM bias in downscaled precipitation: A study in Ganga River Basin
title_full_unstemmed Modified approach to reduce GCM bias in downscaled precipitation: A study in Ganga River Basin
title_sort modified approach to reduce gcm bias in downscaled precipitation: a study in ganga river basin
publishDate 2020
_version_ 1672614182794559488
score 13.211869

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