Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty
Extreme value modeling for extreme rainfall is one of the most important processes in the field of hydrology. For the improvement of extreme value modeling and its physical meaning, large-scale climate modes have been widely used as covariates of distribution parameters, as they can physically accou...
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ftdoajarticles:oai:doaj.org/article:7f2fd6ab92644d3dbc461f2b2ac74617 2023-05-15T17:35:40+02:00 Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty Hanbeen Kim Taereem Kim Ju-Young Shin Jun-Haeng Heo 2022-02-01T00:00:00Z https://doi.org/10.3390/w14030478 https://doaj.org/article/7f2fd6ab92644d3dbc461f2b2ac74617 EN eng MDPI AG https://www.mdpi.com/2073-4441/14/3/478 https://doaj.org/toc/2073-4441 doi:10.3390/w14030478 2073-4441 https://doaj.org/article/7f2fd6ab92644d3dbc461f2b2ac74617 Water, Vol 14, Iss 478, p 478 (2022) extreme value modeling nonstationary GEV distribution seasonal climate indices ensemble empirical mode decomposition residual bootstrap method uncertainty Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 article 2022 ftdoajarticles https://doi.org/10.3390/w14030478 2022-12-31T15:50:42Z Extreme value modeling for extreme rainfall is one of the most important processes in the field of hydrology. For the improvement of extreme value modeling and its physical meaning, large-scale climate modes have been widely used as covariates of distribution parameters, as they can physically account for climate variability. This study proposes a novel procedure for extreme value modeling of rainfall based on the significant relationship between the long-term trend of the annual maximum (AM) daily rainfall and large-scale climate indices. This procedure is characterized by two main steps: (a) identifying significant seasonal climate indices (SCIs), which impact the long-term trend of AM daily rainfall using statistical approaches, such as ensemble empirical mode decomposition, and (b) selecting an appropriate generalized extreme value (GEV) distribution among the stationary GEV and nonstationary GEV (NS-GEV) using time and SCIs as covariates by comparing their model fit and uncertainty. Our findings showed significant relationships between the long-term trend of AM daily rainfall over South Korea and SCIs (i.e., the Atlantic Meridional Mode, Atlantic Multidecadal Oscillation in the fall season, and North Atlantic Oscillation in the summer season). In addition, we proposed a model selection procedure considering both the Akaike information criterion and residual bootstrap method to select an appropriate GEV distribution among a total of 59 GEV candidates. As a result, the NS-GEV with SCI covariates generally showed the best performance over South Korea. We expect that this study can contribute to estimating more reliable extreme rainfall quantiles using climate covariates. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Water 14 3 478 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
extreme value modeling nonstationary GEV distribution seasonal climate indices ensemble empirical mode decomposition residual bootstrap method uncertainty Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
spellingShingle |
extreme value modeling nonstationary GEV distribution seasonal climate indices ensemble empirical mode decomposition residual bootstrap method uncertainty Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 Hanbeen Kim Taereem Kim Ju-Young Shin Jun-Haeng Heo Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty |
topic_facet |
extreme value modeling nonstationary GEV distribution seasonal climate indices ensemble empirical mode decomposition residual bootstrap method uncertainty Hydraulic engineering TC1-978 Water supply for domestic and industrial purposes TD201-500 |
description |
Extreme value modeling for extreme rainfall is one of the most important processes in the field of hydrology. For the improvement of extreme value modeling and its physical meaning, large-scale climate modes have been widely used as covariates of distribution parameters, as they can physically account for climate variability. This study proposes a novel procedure for extreme value modeling of rainfall based on the significant relationship between the long-term trend of the annual maximum (AM) daily rainfall and large-scale climate indices. This procedure is characterized by two main steps: (a) identifying significant seasonal climate indices (SCIs), which impact the long-term trend of AM daily rainfall using statistical approaches, such as ensemble empirical mode decomposition, and (b) selecting an appropriate generalized extreme value (GEV) distribution among the stationary GEV and nonstationary GEV (NS-GEV) using time and SCIs as covariates by comparing their model fit and uncertainty. Our findings showed significant relationships between the long-term trend of AM daily rainfall over South Korea and SCIs (i.e., the Atlantic Meridional Mode, Atlantic Multidecadal Oscillation in the fall season, and North Atlantic Oscillation in the summer season). In addition, we proposed a model selection procedure considering both the Akaike information criterion and residual bootstrap method to select an appropriate GEV distribution among a total of 59 GEV candidates. As a result, the NS-GEV with SCI covariates generally showed the best performance over South Korea. We expect that this study can contribute to estimating more reliable extreme rainfall quantiles using climate covariates. |
format |
Article in Journal/Newspaper |
author |
Hanbeen Kim Taereem Kim Ju-Young Shin Jun-Haeng Heo |
author_facet |
Hanbeen Kim Taereem Kim Ju-Young Shin Jun-Haeng Heo |
author_sort |
Hanbeen Kim |
title |
Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty |
title_short |
Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty |
title_full |
Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty |
title_fullStr |
Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty |
title_full_unstemmed |
Improvement of Extreme Value Modeling for Extreme Rainfall Using Large-Scale Climate Modes and Considering Model Uncertainty |
title_sort |
improvement of extreme value modeling for extreme rainfall using large-scale climate modes and considering model uncertainty |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/w14030478 https://doaj.org/article/7f2fd6ab92644d3dbc461f2b2ac74617 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Water, Vol 14, Iss 478, p 478 (2022) |
op_relation |
https://www.mdpi.com/2073-4441/14/3/478 https://doaj.org/toc/2073-4441 doi:10.3390/w14030478 2073-4441 https://doaj.org/article/7f2fd6ab92644d3dbc461f2b2ac74617 |
op_doi |
https://doi.org/10.3390/w14030478 |
container_title |
Water |
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14 |
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3 |
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478 |
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1766134899124731904 |