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An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia

Han Wang, Yunqing Xuan Orcid Logo

Water, Volume: 15, Issue: 1, Start page: 128

Swansea University Authors: Han Wang, Yunqing Xuan Orcid Logo

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DOI (Published version): 10.3390/w15010128

Abstract

This paper presents an analysis of the temporary variation of the area-orientated annual maximum daily rainfall (AMDR) with respect to the three spatial properties: location, size and shape of the region-of-interest (ROI) in Great Britain and Australia using two century-long datasets. The Maximum Li...

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Published in: Water
ISSN: 2073-4441
Published: MDPI AG 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa62230
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first_indexed 2023-01-02T14:11:32Z
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spelling 2023-02-03T13:38:25.9677108 v2 62230 2023-01-02 An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia a718b31e3f1749890106d990af40fb3c Han Wang Han Wang true false 3ece84458da360ff84fa95aa1c0c912b 0000-0003-2736-8625 Yunqing Xuan Yunqing Xuan true false 2023-01-02 FGSEN This paper presents an analysis of the temporary variation of the area-orientated annual maximum daily rainfall (AMDR) with respect to the three spatial properties: location, size and shape of the region-of-interest (ROI) in Great Britain and Australia using two century-long datasets. The Maximum Likelihood and Bayesian Markov-Chain-Monte-Carlo methods are employed to quantify the time-varying frequency of AMDR, where a large proportion of the ROIs shows a non-decreasing level of most frequent AMDR. While the most frequent AMDR values generally decrease with larger-sized ROIs, their temporal variation that can be attributed to the climate change impact does not show the same dependency on the size. Climate change impact on ROI-orientated extreme rainfall is seen higher for rounded shapes although the ROI shape is not as significant as the other two spatial properties. Comparison of the AMDR at different return levels shows an underestimation by conventionally used stationary models in regions where a nonstationary (i.e., time-varying) model is preferred. The findings suggest an overhaul of the current storm design procedure in view of the impact of not only climate change but also spatial variation in natural processes. Journal Article Water 15 1 128 MDPI AG 2073-4441 extreme rainfall; spatial variation; return period; GEV; climate change; nonstationarity 29 12 2022 2022-12-29 10.3390/w15010128 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University Another institution paid the OA fee Academy of Medical Sciences GCRFNGR4_1165 2023-02-03T13:38:25.9677108 2023-01-02T14:08:09.4163942 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Han Wang 1 Yunqing Xuan 0000-0003-2736-8625 2 62230__26187__1463025086794ea69627d1dfcf3de3c4.pdf 62230.VOR.pdf 2023-01-04T13:23:01.9714347 Output 5818351 application/pdf Version of Record true © 2022 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license. true eng (https://creativecommons.org/licenses/by/4.0/
title An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia
spellingShingle An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia
Han Wang
Yunqing Xuan
title_short An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia
title_full An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia
title_fullStr An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia
title_full_unstemmed An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia
title_sort An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia
author_id_str_mv a718b31e3f1749890106d990af40fb3c
3ece84458da360ff84fa95aa1c0c912b
author_id_fullname_str_mv a718b31e3f1749890106d990af40fb3c_***_Han Wang
3ece84458da360ff84fa95aa1c0c912b_***_Yunqing Xuan
author Han Wang
Yunqing Xuan
author2 Han Wang
Yunqing Xuan
format Journal article
container_title Water
container_volume 15
container_issue 1
container_start_page 128
publishDate 2022
institution Swansea University
issn 2073-4441
doi_str_mv 10.3390/w15010128
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering
document_store_str 1
active_str 0
description This paper presents an analysis of the temporary variation of the area-orientated annual maximum daily rainfall (AMDR) with respect to the three spatial properties: location, size and shape of the region-of-interest (ROI) in Great Britain and Australia using two century-long datasets. The Maximum Likelihood and Bayesian Markov-Chain-Monte-Carlo methods are employed to quantify the time-varying frequency of AMDR, where a large proportion of the ROIs shows a non-decreasing level of most frequent AMDR. While the most frequent AMDR values generally decrease with larger-sized ROIs, their temporal variation that can be attributed to the climate change impact does not show the same dependency on the size. Climate change impact on ROI-orientated extreme rainfall is seen higher for rounded shapes although the ROI shape is not as significant as the other two spatial properties. Comparison of the AMDR at different return levels shows an underestimation by conventionally used stationary models in regions where a nonstationary (i.e., time-varying) model is preferred. The findings suggest an overhaul of the current storm design procedure in view of the impact of not only climate change but also spatial variation in natural processes.
published_date 2022-12-29T04:21:41Z
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score 11.037166

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