Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes
The objective of this study is to quantify the spatial dependency and trend of annual maxima precipitation (annual highest daily precipitation, from 1970 to 2020) across selected weather stations in the Nelson Churchill River Basin (NCRB) of North America. This study uses max-stable processes to exa...
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ftdoajarticles:oai:doaj.org/article:5d62db05769149e88435891761d28f5e 2023-11-12T04:15:53+01:00 Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes Alaba Boluwade 2023-09-01T00:00:00Z https://doi.org/10.2166/wcc.2023.187 https://doaj.org/article/5d62db05769149e88435891761d28f5e EN eng IWA Publishing http://jwcc.iwaponline.com/content/14/9/3131 https://doaj.org/toc/2040-2244 https://doaj.org/toc/2408-9354 2040-2244 2408-9354 doi:10.2166/wcc.2023.187 https://doaj.org/article/5d62db05769149e88435891761d28f5e Journal of Water and Climate Change, Vol 14, Iss 9, Pp 3131-3149 (2023) canada extreme events flash flood protection flood protection max-stable processes maxima annual rainfall Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 article 2023 ftdoajarticles https://doi.org/10.2166/wcc.2023.187 2023-10-15T00:37:21Z The objective of this study is to quantify the spatial dependency and trend of annual maxima precipitation (annual highest daily precipitation, from 1970 to 2020) across selected weather stations in the Nelson Churchill River Basin (NCRB) of North America. This study uses max-stable processes to examine spatial extremes of annual maxima precipitation. The generalized extreme value (GEV) parameters are expressed as simple linear combinations of geographical coordinates (i.e., longitude and latitude) and topography. The results show that topography, geographical coordinates, and time (as a temporal covariate) were important covariates in reproducing the stochastic extreme precipitation field using the spatial generalized extreme value (SPEV). The inclusion of time as a covariate further confirms the impacts of climate change on extreme precipitation in the NCRB. The fitted SPEV was used to predict the 25- and 50-year return period levels. The fitted Extremal-t max-stable process model captured the spatial dependency structure of the extreme precipitation in the NCRB. The study is relevant in quantifying the spatial dependency structure of extreme precipitation in the Northern Great Plains. The result will contribute as a decision-support system in climate adaptation strategies in the United States and Canada. HIGHLIGHTS 25- and 50-year return-level scenarios for the daily annual extreme precipitation show spatial variability in all the sub-basins.; The precipitation extremes in the NCRB show both spatial trends and dependency.; Including topography shows that the Rocky Mountains have some influence on the extreme precipitation in the NCRB.; Fitted Extremal-t max-stable process adequately captured the spatial dependency structures.; Article in Journal/Newspaper Churchill River Directory of Open Access Journals: DOAJ Articles Canada Journal of Water and Climate Change 14 9 3131 3149 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
canada extreme events flash flood protection flood protection max-stable processes maxima annual rainfall Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 |
spellingShingle |
canada extreme events flash flood protection flood protection max-stable processes maxima annual rainfall Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Alaba Boluwade Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes |
topic_facet |
canada extreme events flash flood protection flood protection max-stable processes maxima annual rainfall Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 |
description |
The objective of this study is to quantify the spatial dependency and trend of annual maxima precipitation (annual highest daily precipitation, from 1970 to 2020) across selected weather stations in the Nelson Churchill River Basin (NCRB) of North America. This study uses max-stable processes to examine spatial extremes of annual maxima precipitation. The generalized extreme value (GEV) parameters are expressed as simple linear combinations of geographical coordinates (i.e., longitude and latitude) and topography. The results show that topography, geographical coordinates, and time (as a temporal covariate) were important covariates in reproducing the stochastic extreme precipitation field using the spatial generalized extreme value (SPEV). The inclusion of time as a covariate further confirms the impacts of climate change on extreme precipitation in the NCRB. The fitted SPEV was used to predict the 25- and 50-year return period levels. The fitted Extremal-t max-stable process model captured the spatial dependency structure of the extreme precipitation in the NCRB. The study is relevant in quantifying the spatial dependency structure of extreme precipitation in the Northern Great Plains. The result will contribute as a decision-support system in climate adaptation strategies in the United States and Canada. HIGHLIGHTS 25- and 50-year return-level scenarios for the daily annual extreme precipitation show spatial variability in all the sub-basins.; The precipitation extremes in the NCRB show both spatial trends and dependency.; Including topography shows that the Rocky Mountains have some influence on the extreme precipitation in the NCRB.; Fitted Extremal-t max-stable process adequately captured the spatial dependency structures.; |
format |
Article in Journal/Newspaper |
author |
Alaba Boluwade |
author_facet |
Alaba Boluwade |
author_sort |
Alaba Boluwade |
title |
Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes |
title_short |
Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes |
title_full |
Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes |
title_fullStr |
Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes |
title_full_unstemmed |
Stochastic modeling of spatial dependency structures of extreme precipitation in the Northern Great Plains using max-stable processes |
title_sort |
stochastic modeling of spatial dependency structures of extreme precipitation in the northern great plains using max-stable processes |
publisher |
IWA Publishing |
publishDate |
2023 |
url |
https://doi.org/10.2166/wcc.2023.187 https://doaj.org/article/5d62db05769149e88435891761d28f5e |
geographic |
Canada |
geographic_facet |
Canada |
genre |
Churchill River |
genre_facet |
Churchill River |
op_source |
Journal of Water and Climate Change, Vol 14, Iss 9, Pp 3131-3149 (2023) |
op_relation |
http://jwcc.iwaponline.com/content/14/9/3131 https://doaj.org/toc/2040-2244 https://doaj.org/toc/2408-9354 2040-2244 2408-9354 doi:10.2166/wcc.2023.187 https://doaj.org/article/5d62db05769149e88435891761d28f5e |
op_doi |
https://doi.org/10.2166/wcc.2023.187 |
container_title |
Journal of Water and Climate Change |
container_volume |
14 |
container_issue |
9 |
container_start_page |
3131 |
op_container_end_page |
3149 |
_version_ |
1782333133921714176 |