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An Area-Orientated Analysis of the Temporal Variation of Extreme Daily Rainfall in Great Britain and Australia
Water, Volume: 15, Issue: 1, Start page: 128
Swansea University Authors:
Han Wang, Yunqing Xuan
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© 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.
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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...
| Published in: | Water |
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| ISSN: | 2073-4441 |
| Published: |
MDPI AG
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa62230 |
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| 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 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. |
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| Keywords: |
extreme rainfall; spatial variation; return period; GEV; climate change; nonstationarity |
| College: |
Faculty of Science and Engineering |
| Funders: |
Academy of Medical Sciences GCRFNGR4_1165 |
| Issue: |
1 |
| Start Page: |
128 |