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Open AccessArticle

Modified Approach to Reduce GCM Bias in Downscaled Precipitation: A Study in Ganga River Basin

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Civil Engineering Department, Indian Institute of Technology, Roorkee 247667, India
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Department of Hydraulic and Ocean Engineering, National Cheng-Kung University, Tainan 701, Taiwan
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Faculty of Water Resource Engineering, Thuyloi University, Hanoi 100000, Vietnam
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Civil Engineering Department, college of Engineering, Universiti Tenaga Nasional (UNITEN), Kajang 43000, Malaysia
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Faculty of Environmental and Food Engineering, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam
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National Institute of Education, 1, Nanyang Walk, Singapore 637616, Singapore
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Center of Water Management and Climate Change, Vietnam National University Ho Chi Minh City, Ho Chi Minh City 700000, Vietnam
*
Author to whom correspondence should be addressed.
Water 2019, 11(10), 2097; https://doi.org/10.3390/w11102097
Received: 14 July 2019 / Revised: 15 August 2019 / Accepted: 15 August 2019 / Published: 8 October 2019
(This article belongs to the Special Issue Urban Water Accounting)
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. View Full-Text
Keywords: Ganga river basin; precipitation; general circulation models (GCM); downscaling; GCM bias; model performance Ganga river basin; precipitation; general circulation models (GCM); downscaling; GCM bias; model performance
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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. Water 2019, 11, 2097.

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