Statistical tool for modeling of a daily precipitation process in the context of climate change

The present study proposes a climate change assessment tool based on a statistical downscaling (SD) approach for describing the linkage between large-scale climate predictors and observed daily rainfall characteristics at a local site. The proposed SD of the daily rainfall process (SDRain) model is...

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Autores principales:	Myeong-Ho Yeo, Hoang-Lam Nguyen, Van-Thanh-Van Nguyen
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	assessment tool climate change daily precipitation statistical downscaling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/e1ed290dec4e478ab8793e26049ae1ef
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id	oai:doaj.org-article:e1ed290dec4e478ab8793e26049ae1ef
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spelling	oai:doaj.org-article:e1ed290dec4e478ab8793e26049ae1ef2021-11-05T18:40:38ZStatistical tool for modeling of a daily precipitation process in the context of climate change2040-22442408-935410.2166/wcc.2019.403https://doaj.org/article/e1ed290dec4e478ab8793e26049ae1ef2021-02-01T00:00:00Zhttp://jwcc.iwaponline.com/content/12/1/18https://doaj.org/toc/2040-2244https://doaj.org/toc/2408-9354The present study proposes a climate change assessment tool based on a statistical downscaling (SD) approach for describing the linkage between large-scale climate predictors and observed daily rainfall characteristics at a local site. The proposed SD of the daily rainfall process (SDRain) model is based on a combination of a logistic regression model for representing the daily rainfall occurrences and a nonlinear regression model for describing the daily precipitation amounts. A scaling factor (SR) and correction coefficient (CR) are suggested to improve the accuracy of the SDRain model in representing the variance of the observed daily precipitation amounts in each month without affecting the monthly mean precipitation. SDRain facilitates the construction of daily precipitation models for the current and future climate conditions. The tool is tested using the National Center for Environmental Prediction re-analysis data and the observed daily precipitation data available for the 1961–2001 period at two study sites located in two completely different climatic regions: the Seoul station in subtropical-climate Korea and the Dorval Airport station in cold-climate Canada. Results of this illustrative application have indicated that the proposed functions (e.g. logistic regression, SR, and CR) contribute marked improvement in describing daily precipitation amounts and occurrences. Furthermore, the comparison analyses show that the proposed SD method could provide more accurate results than those given by the currently popular SDSM method.Myeong-Ho YeoHoang-Lam NguyenVan-Thanh-Van NguyenIWA Publishingarticleassessment toolclimate changedaily precipitationstatistical downscalingEnvironmental technology. Sanitary engineeringTD1-1066Environmental sciencesGE1-350ENJournal of Water and Climate Change, Vol 12, Iss 1, Pp 18-31 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	assessment tool climate change daily precipitation statistical downscaling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350
spellingShingle	assessment tool climate change daily precipitation statistical downscaling Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Myeong-Ho Yeo Hoang-Lam Nguyen Van-Thanh-Van Nguyen Statistical tool for modeling of a daily precipitation process in the context of climate change
description	The present study proposes a climate change assessment tool based on a statistical downscaling (SD) approach for describing the linkage between large-scale climate predictors and observed daily rainfall characteristics at a local site. The proposed SD of the daily rainfall process (SDRain) model is based on a combination of a logistic regression model for representing the daily rainfall occurrences and a nonlinear regression model for describing the daily precipitation amounts. A scaling factor (SR) and correction coefficient (CR) are suggested to improve the accuracy of the SDRain model in representing the variance of the observed daily precipitation amounts in each month without affecting the monthly mean precipitation. SDRain facilitates the construction of daily precipitation models for the current and future climate conditions. The tool is tested using the National Center for Environmental Prediction re-analysis data and the observed daily precipitation data available for the 1961–2001 period at two study sites located in two completely different climatic regions: the Seoul station in subtropical-climate Korea and the Dorval Airport station in cold-climate Canada. Results of this illustrative application have indicated that the proposed functions (e.g. logistic regression, SR, and CR) contribute marked improvement in describing daily precipitation amounts and occurrences. Furthermore, the comparison analyses show that the proposed SD method could provide more accurate results than those given by the currently popular SDSM method.
format	article
author	Myeong-Ho Yeo Hoang-Lam Nguyen Van-Thanh-Van Nguyen
author_facet	Myeong-Ho Yeo Hoang-Lam Nguyen Van-Thanh-Van Nguyen
author_sort	Myeong-Ho Yeo
title	Statistical tool for modeling of a daily precipitation process in the context of climate change
title_short	Statistical tool for modeling of a daily precipitation process in the context of climate change
title_full	Statistical tool for modeling of a daily precipitation process in the context of climate change
title_fullStr	Statistical tool for modeling of a daily precipitation process in the context of climate change
title_full_unstemmed	Statistical tool for modeling of a daily precipitation process in the context of climate change
title_sort	statistical tool for modeling of a daily precipitation process in the context of climate change
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/e1ed290dec4e478ab8793e26049ae1ef
work_keys_str_mv	AT myeonghoyeo statisticaltoolformodelingofadailyprecipitationprocessinthecontextofclimatechange AT hoanglamnguyen statisticaltoolformodelingofadailyprecipitationprocessinthecontextofclimatechange AT vanthanhvannguyen statisticaltoolformodelingofadailyprecipitationprocessinthecontextofclimatechange
_version_	1718444134286491648

Statistical tool for modeling of a daily precipitation process in the context of climate change

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