Bias correction capabilities of quantile mapping methods for rainfall and temperature variables

This study aims to conduct a thorough investigation to compare the abilities of quantile mapping (QM) techniques as a bias correction method for the raw outputs from general circulation model (GCM)/regional climate model (RCM) combinations. The Karkheh River basin in Iran was selected as a case stud...

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Autores principales:	Maedeh Enayati, Omid Bozorg-Haddad, Javad Bazrafshan, Somayeh Hejabi, Xuefeng Chu
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	bias correction climate change coredex quantile mapping rcm Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/5f8afecda5c04fad85a0e08f3910d950
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spelling	oai:doaj.org-article:5f8afecda5c04fad85a0e08f3910d9502021-11-05T18:30:59ZBias correction capabilities of quantile mapping methods for rainfall and temperature variables2040-22442408-935410.2166/wcc.2020.261https://doaj.org/article/5f8afecda5c04fad85a0e08f3910d9502021-03-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/2/401https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354This study aims to conduct a thorough investigation to compare the abilities of quantile mapping (QM) techniques as a bias correction method for the raw outputs from general circulation model (GCM)/regional climate model (RCM) combinations. The Karkheh River basin in Iran was selected as a case study, due to its diverse topographic features, to test the performances of the bias correction methods under different conditions. The outputs of two GCM/RCM combinations (ICHEC and NOAA-ESM) were acquired from the coordinated regional climate downscaling experiment (CORDEX) dataset for this study. The results indicated that the performances of the QMs varied, depending on the transformation functions, parameter sets, and topographic conditions. In some cases, the QMs' adjustments even made the GCM/RCM combinations' raw outputs worse. The result of this study suggested that apart from DIST, PTF:scale, and SSPLIN, the rest of the considered QM methods can provide relatively improved results for both rainfall and temperature variables. It should be noted that, according to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable. HIGHLIGHTS Quantile mapping (QM) techniques are among the most important and popular bias correction methods. This study aims to provide a comprehensive comparison to identify the potential strengths and weaknesses of these methods in coping with hydro-climatic variables.; This study aims to shed light on the general abilities of QM methods in correcting the bias of both temperature and rainfall variables, both of which can have direct and indirect impacts on water resources. Furthermore, this study explores these capabilities in diverse topographic conditions. Coping with such topographic conditions is a known challenge for both general and regional circulation models.; This study explores the projections of the coordinated regional climate downscaling experiment (CORDEX) dataset. The main idea behind the CORDEX project is to coordinate the results of local climatic studies. As such, the findings of this study can help expand the capabilities and applicability of this project.; The results of this study revealed that some QM methods could, in fact, worsen the accuracy of general and regional circulation models, which highlights the importance of selecting a suitable bias correction method.; According to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable.;Maedeh EnayatiOmid Bozorg-HaddadJavad BazrafshanSomayeh HejabiXuefeng ChuIWA Publishingarticlebias correctionclimate changecoredexquantile mappingrcmEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 2, Pp 401-419 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	bias correction climate change coredex quantile mapping rcm Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	bias correction climate change coredex quantile mapping rcm Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Maedeh Enayati Omid Bozorg-Haddad Javad Bazrafshan Somayeh Hejabi Xuefeng Chu Bias correction capabilities of quantile mapping methods for rainfall and temperature variables
description	This study aims to conduct a thorough investigation to compare the abilities of quantile mapping (QM) techniques as a bias correction method for the raw outputs from general circulation model (GCM)/regional climate model (RCM) combinations. The Karkheh River basin in Iran was selected as a case study, due to its diverse topographic features, to test the performances of the bias correction methods under different conditions. The outputs of two GCM/RCM combinations (ICHEC and NOAA-ESM) were acquired from the coordinated regional climate downscaling experiment (CORDEX) dataset for this study. The results indicated that the performances of the QMs varied, depending on the transformation functions, parameter sets, and topographic conditions. In some cases, the QMs' adjustments even made the GCM/RCM combinations' raw outputs worse. The result of this study suggested that apart from DIST, PTF:scale, and SSPLIN, the rest of the considered QM methods can provide relatively improved results for both rainfall and temperature variables. It should be noted that, according to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable. HIGHLIGHTS Quantile mapping (QM) techniques are among the most important and popular bias correction methods. This study aims to provide a comprehensive comparison to identify the potential strengths and weaknesses of these methods in coping with hydro-climatic variables.; This study aims to shed light on the general abilities of QM methods in correcting the bias of both temperature and rainfall variables, both of which can have direct and indirect impacts on water resources. Furthermore, this study explores these capabilities in diverse topographic conditions. Coping with such topographic conditions is a known challenge for both general and regional circulation models.; This study explores the projections of the coordinated regional climate downscaling experiment (CORDEX) dataset. The main idea behind the CORDEX project is to coordinate the results of local climatic studies. As such, the findings of this study can help expand the capabilities and applicability of this project.; The results of this study revealed that some QM methods could, in fact, worsen the accuracy of general and regional circulation models, which highlights the importance of selecting a suitable bias correction method.; According to the results obtained from the diverse topographic conditions of the sub-basins, the empirical quantiles (QUANT) and robust empirical quantiles (RQUANT) methods proved to be excellent options to correct the bias of rainfall data, while all bias correction methods, with the notable exceptions of performed PTF:scale and SSPLIN, performed relatively well for the temperature variable.;
format	article
author	Maedeh Enayati Omid Bozorg-Haddad Javad Bazrafshan Somayeh Hejabi Xuefeng Chu
author_facet	Maedeh Enayati Omid Bozorg-Haddad Javad Bazrafshan Somayeh Hejabi Xuefeng Chu
author_sort	Maedeh Enayati
title	Bias correction capabilities of quantile mapping methods for rainfall and temperature variables
title_short	Bias correction capabilities of quantile mapping methods for rainfall and temperature variables
title_full	Bias correction capabilities of quantile mapping methods for rainfall and temperature variables
title_fullStr	Bias correction capabilities of quantile mapping methods for rainfall and temperature variables
title_full_unstemmed	Bias correction capabilities of quantile mapping methods for rainfall and temperature variables
title_sort	bias correction capabilities of quantile mapping methods for rainfall and temperature variables
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/5f8afecda5c04fad85a0e08f3910d950
work_keys_str_mv	AT maedehenayati biascorrectioncapabilitiesofquantilemappingmethodsforrainfallandtemperaturevariables AT omidbozorghaddad biascorrectioncapabilitiesofquantilemappingmethodsforrainfallandtemperaturevariables AT javadbazrafshan biascorrectioncapabilitiesofquantilemappingmethodsforrainfallandtemperaturevariables AT somayehhejabi biascorrectioncapabilitiesofquantilemappingmethodsforrainfallandtemperaturevariables AT xuefengchu biascorrectioncapabilitiesofquantilemappingmethodsforrainfallandtemperaturevariables
_version_	1718444103143784448

Bias correction capabilities of quantile mapping methods for rainfall and temperature variables

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