Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model

An accuracy in the hydrological modelling will be affected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Thus, the objective was to analyse the accuracy of the long-t...

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Main Authors:	N. N. A., Tukimat, N. A., Ahmad Syukri, M. A., Malek
Format:	Article
Language:	English
Published:	Elsevier Ltd 2019
Subjects:	TA Engineering (General). Civil engineering (General)
Online Access:	http://umpir.ump.edu.my/id/eprint/25910/ http://umpir.ump.edu.my/id/eprint/25910/ http://umpir.ump.edu.my/id/eprint/25910/ http://umpir.ump.edu.my/id/eprint/25910/1/Projection%20the%20long-term%20ungauged%20rainfall%20using%20integrated%20Statistical%20Downscaling%20Model%20and%20Geographic%20Information%20System%20%28SDSM-GIS%29%20model.pdf

id	ump-25910
recordtype	eprints
spelling	ump-259102019-10-03T02:23:56Z http://umpir.ump.edu.my/id/eprint/25910/ Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model N. N. A., Tukimat N. A., Ahmad Syukri M. A., Malek TA Engineering (General). Civil engineering (General) An accuracy in the hydrological modelling will be affected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Thus, the objective was to analyse the accuracy of the long-term projected rainfall at ungauged rainfall station using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. The SDSM was used as a climate agent to predict the changes of the climate trend in Δ2030s by gauged and ungauged stations. There were five predictors set have been selected to form the local climate at the region which provided by NCEP (validated) and CanESM2-RCP4.5 (projected). According to the statistical analyses, the SDSM was controlled to produce reliable validated results with lesser %MAE (<23%) and higher R. The projected rainfall was suspected to decrease 14% in Δ2030s. All the RCPs agreed the long term rainfall pattern was consistent to the historical with lower annual rainfall intensity. The RCP8.5 shows the least rainfall changes. These findings then used to compare the accuracy of monthly rainfall at control station (Stn 2). The GIS-Kriging method being as an interpolation agent was successfully to produce similar rainfall trend with the control station. The accuracy was estimated to reach 84%. Comparing between ungauged and gauged stations, the small %MAE in the projected monthly results between gauged and ungauged stations as a proved the integrated SDSM-GIS model can producing a reliable long-term rainfall generation at ungauged station. Elsevier Ltd 2019 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/25910/1/Projection%20the%20long-term%20ungauged%20rainfall%20using%20integrated%20Statistical%20Downscaling%20Model%20and%20Geographic%20Information%20System%20%28SDSM-GIS%29%20model.pdf N. N. A., Tukimat and N. A., Ahmad Syukri and M. A., Malek (2019) Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. Heliyon, 5 (9). pp. 1-8. ISSN 2405-8440 https://doi.org/10.1016/j.heliyon.2019.e02456 https://doi.org/10.1016/j.heliyon.2019.e02456
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General)
spellingShingle	TA Engineering (General). Civil engineering (General) N. N. A., Tukimat N. A., Ahmad Syukri M. A., Malek Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model
description	An accuracy in the hydrological modelling will be affected when having limited data sources especially at ungauged areas. Due to this matter, it will not receiving any significant attention especially on the potential hydrologic extremes. Thus, the objective was to analyse the accuracy of the long-term projected rainfall at ungauged rainfall station using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model. The SDSM was used as a climate agent to predict the changes of the climate trend in Δ2030s by gauged and ungauged stations. There were five predictors set have been selected to form the local climate at the region which provided by NCEP (validated) and CanESM2-RCP4.5 (projected). According to the statistical analyses, the SDSM was controlled to produce reliable validated results with lesser %MAE (<23%) and higher R. The projected rainfall was suspected to decrease 14% in Δ2030s. All the RCPs agreed the long term rainfall pattern was consistent to the historical with lower annual rainfall intensity. The RCP8.5 shows the least rainfall changes. These findings then used to compare the accuracy of monthly rainfall at control station (Stn 2). The GIS-Kriging method being as an interpolation agent was successfully to produce similar rainfall trend with the control station. The accuracy was estimated to reach 84%. Comparing between ungauged and gauged stations, the small %MAE in the projected monthly results between gauged and ungauged stations as a proved the integrated SDSM-GIS model can producing a reliable long-term rainfall generation at ungauged station.
format	Article
author	N. N. A., Tukimat N. A., Ahmad Syukri M. A., Malek
author_facet	N. N. A., Tukimat N. A., Ahmad Syukri M. A., Malek
author_sort	N. N. A., Tukimat
title	Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model
title_short	Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model
title_full	Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model
title_fullStr	Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model
title_full_unstemmed	Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model
title_sort	projection the long-term ungauged rainfall using integrated statistical downscaling model and geographic information system (sdsm-gis) model
publisher	Elsevier Ltd
publishDate	2019
url	http://umpir.ump.edu.my/id/eprint/25910/ http://umpir.ump.edu.my/id/eprint/25910/ http://umpir.ump.edu.my/id/eprint/25910/ http://umpir.ump.edu.my/id/eprint/25910/1/Projection%20the%20long-term%20ungauged%20rainfall%20using%20integrated%20Statistical%20Downscaling%20Model%20and%20Geographic%20Information%20System%20%28SDSM-GIS%29%20model.pdf
first_indexed	2023-09-18T22:40:02Z
last_indexed	2023-09-18T22:40:02Z
_version_	1777416852844052480

Projection the long-term ungauged rainfall using integrated Statistical Downscaling Model and Geographic Information System (SDSM-GIS) model

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