Attribution of long-term changes in peak river flows in Great Britain
We investigate the evidence for changes in the magnitude of peak river flows in Great Britain. We focus on a set of 117 near-natural “benchmark” catchments to detect trends not driven by land use and other human impacts, and aim to attribute trends in peak river flows to some climate indices such as...
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ftdatacite:10.6084/m9.figshare.8319848.v1 2023-05-15T17:33:20+02:00 Attribution of long-term changes in peak river flows in Great Britain Brady, Aoibheann Faraway, Julian Prosdocimi, Ilaria 2019 https://dx.doi.org/10.6084/m9.figshare.8319848.v1 https://tandf.figshare.com/articles/Attribution_of_long-term_changes_in_peak_river_flows_in_Great_Britain/8319848/1 unknown Taylor & Francis https://dx.doi.org/10.1080/02626667.2019.1628964 https://dx.doi.org/10.6084/m9.figshare.8319848 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 CC-BY Space Science Ecology FOS Biological sciences Sociology FOS Sociology Cancer Inorganic Chemistry FOS Chemical sciences Plant Biology Text article-journal Journal contribution ScholarlyArticle 2019 ftdatacite https://doi.org/10.6084/m9.figshare.8319848.v1 https://doi.org/10.1080/02626667.2019.1628964 https://doi.org/10.6084/m9.figshare.8319848 2021-11-05T12:55:41Z We investigate the evidence for changes in the magnitude of peak river flows in Great Britain. We focus on a set of 117 near-natural “benchmark” catchments to detect trends not driven by land use and other human impacts, and aim to attribute trends in peak river flows to some climate indices such as the North Atlantic Oscillation (NAO) and the East Atlantic (EA) index. We propose modelling all stations together in a Bayesian multilevel framework to be better able to detect any signal that is present in the data by pooling information across several stations. This approach leads to the detection of a clear countrywide time trend. Additionally, in a univariate approach, both the EA and NAO indices appear to have a considerable association with peak river flows. When a multivariate approach is taken to unmask the collinearity between climate indices and time, the association between NAO and peak flows disappears, while the association with EA remains clear. This demonstrates the usefulness of a multivariate and multilevel approach when it comes to accurately attributing trends in peak river flows. Text North Atlantic North Atlantic oscillation DataCite Metadata Store (German National Library of Science and Technology) |
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Space Science Ecology FOS Biological sciences Sociology FOS Sociology Cancer Inorganic Chemistry FOS Chemical sciences Plant Biology |
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Space Science Ecology FOS Biological sciences Sociology FOS Sociology Cancer Inorganic Chemistry FOS Chemical sciences Plant Biology Brady, Aoibheann Faraway, Julian Prosdocimi, Ilaria Attribution of long-term changes in peak river flows in Great Britain |
topic_facet |
Space Science Ecology FOS Biological sciences Sociology FOS Sociology Cancer Inorganic Chemistry FOS Chemical sciences Plant Biology |
description |
We investigate the evidence for changes in the magnitude of peak river flows in Great Britain. We focus on a set of 117 near-natural “benchmark” catchments to detect trends not driven by land use and other human impacts, and aim to attribute trends in peak river flows to some climate indices such as the North Atlantic Oscillation (NAO) and the East Atlantic (EA) index. We propose modelling all stations together in a Bayesian multilevel framework to be better able to detect any signal that is present in the data by pooling information across several stations. This approach leads to the detection of a clear countrywide time trend. Additionally, in a univariate approach, both the EA and NAO indices appear to have a considerable association with peak river flows. When a multivariate approach is taken to unmask the collinearity between climate indices and time, the association between NAO and peak flows disappears, while the association with EA remains clear. This demonstrates the usefulness of a multivariate and multilevel approach when it comes to accurately attributing trends in peak river flows. |
format |
Text |
author |
Brady, Aoibheann Faraway, Julian Prosdocimi, Ilaria |
author_facet |
Brady, Aoibheann Faraway, Julian Prosdocimi, Ilaria |
author_sort |
Brady, Aoibheann |
title |
Attribution of long-term changes in peak river flows in Great Britain |
title_short |
Attribution of long-term changes in peak river flows in Great Britain |
title_full |
Attribution of long-term changes in peak river flows in Great Britain |
title_fullStr |
Attribution of long-term changes in peak river flows in Great Britain |
title_full_unstemmed |
Attribution of long-term changes in peak river flows in Great Britain |
title_sort |
attribution of long-term changes in peak river flows in great britain |
publisher |
Taylor & Francis |
publishDate |
2019 |
url |
https://dx.doi.org/10.6084/m9.figshare.8319848.v1 https://tandf.figshare.com/articles/Attribution_of_long-term_changes_in_peak_river_flows_in_Great_Britain/8319848/1 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_relation |
https://dx.doi.org/10.1080/02626667.2019.1628964 https://dx.doi.org/10.6084/m9.figshare.8319848 |
op_rights |
Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.6084/m9.figshare.8319848.v1 https://doi.org/10.1080/02626667.2019.1628964 https://doi.org/10.6084/m9.figshare.8319848 |
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1766131817353576448 |