Statistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin

Climate change impact studies are generally carried out with higher resolution general circulation model (GCM) outputs, which are usually for a global scale, and it is difficult to use the same for a regional scale. GCM simulations require downscaling to get a coarser scale output for local climate...

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Autores principales:	Parthiban Loganathan, Amit Baburao Mahindrakar
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	climate change performance evaluation statistical downscaling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/ce8bbdd7513d47ef8c92eac6536e588d
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spelling	oai:doaj.org-article:ce8bbdd7513d47ef8c92eac6536e588d2021-11-05T19:07:22ZStatistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin2040-22442408-935410.2166/wcc.2021.223https://doaj.org/article/ce8bbdd7513d47ef8c92eac6536e588d2021-09-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/6/2314https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354Climate change impact studies are generally carried out with higher resolution general circulation model (GCM) outputs, which are usually for a global scale, and it is difficult to use the same for a regional scale. GCM simulations require downscaling to get a coarser scale output for local climate impact studies. In this study, an improvised principal component regression (PCR) downscaling technique is adapted to downscale 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM historical outputs. A massive river basin named Cauvery with 35 observation stations is categorized into three subbasins to study the regional climate impacts. In this case, the PCR model performed remarkably well compared to other conventional machine learning models with half the computational time than usual. The test statistics state that the validation of the proposed model illustrates a variance in calibration results of the PCR model, which ranges between 2 and 5%, and a variance in validation, which is less than 7% throughout the study area. Since it is desired to prioritize GCMs to choose the merely suitable models for a strategic climate study, the models were selected based on the PCR model performance. Furthermore, CCSM4, inmcm4, and EC-EARTH model's performance in recreating precipitation statistics over the study area are exceptional. HIGHLIGHTS The evaluation and comparison of downscaling Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) based on renowned machine learning (ML) techniques.; The suggestion of an alternate ML (principal component regression) approach for improvised downscaling.; Subbasin-wise climate assessment on a large-scale river basin.; The intercomparison of ML models with respect to calibration and validation periods.; The outcome suggests an improvised climate downscaling approach with an appropriate CMIP5 GCM.;Parthiban LoganathanAmit Baburao MahindrakarIWA Publishingarticleclimate changeperformance evaluationstatistical downscalingEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 6, Pp 2314-2324 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	climate change performance evaluation statistical downscaling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	climate change performance evaluation statistical downscaling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Parthiban Loganathan Amit Baburao Mahindrakar Statistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin
description	Climate change impact studies are generally carried out with higher resolution general circulation model (GCM) outputs, which are usually for a global scale, and it is difficult to use the same for a regional scale. GCM simulations require downscaling to get a coarser scale output for local climate impact studies. In this study, an improvised principal component regression (PCR) downscaling technique is adapted to downscale 26 Coupled Model Intercomparison Project Phase 5 (CMIP5) GCM historical outputs. A massive river basin named Cauvery with 35 observation stations is categorized into three subbasins to study the regional climate impacts. In this case, the PCR model performed remarkably well compared to other conventional machine learning models with half the computational time than usual. The test statistics state that the validation of the proposed model illustrates a variance in calibration results of the PCR model, which ranges between 2 and 5%, and a variance in validation, which is less than 7% throughout the study area. Since it is desired to prioritize GCMs to choose the merely suitable models for a strategic climate study, the models were selected based on the PCR model performance. Furthermore, CCSM4, inmcm4, and EC-EARTH model's performance in recreating precipitation statistics over the study area are exceptional. HIGHLIGHTS The evaluation and comparison of downscaling Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs) based on renowned machine learning (ML) techniques.; The suggestion of an alternate ML (principal component regression) approach for improvised downscaling.; Subbasin-wise climate assessment on a large-scale river basin.; The intercomparison of ML models with respect to calibration and validation periods.; The outcome suggests an improvised climate downscaling approach with an appropriate CMIP5 GCM.;
format	article
author	Parthiban Loganathan Amit Baburao Mahindrakar
author_facet	Parthiban Loganathan Amit Baburao Mahindrakar
author_sort	Parthiban Loganathan
title	Statistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin
title_short	Statistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin
title_full	Statistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin
title_fullStr	Statistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin
title_full_unstemmed	Statistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin
title_sort	statistical downscaling using principal component regression for climate change impact assessment at the cauvery river basin
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/ce8bbdd7513d47ef8c92eac6536e588d
work_keys_str_mv	AT parthibanloganathan statisticaldownscalingusingprincipalcomponentregressionforclimatechangeimpactassessmentatthecauveryriverbasin AT amitbaburaomahindrakar statisticaldownscalingusingprincipalcomponentregressionforclimatechangeimpactassessmentatthecauveryriverbasin
_version_	1718444067374759936

Statistical downscaling using principal component regression for climate change impact assessment at the Cauvery river basin

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