Journal article 1268 views
Statistical simulation of flood variables: incorporating short-term sequencing
Journal of Flood Risk Management, Volume: 1, Issue: 1, Pages: 3 - 10
Swansea University Author: Yuzhi Cai
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DOI (Published version): 10.1111/j.1753-318X.2008.00002.x
Abstract
The pluvial and fluvial flooding in the United Kingdom over the summer of 2007arose as a result of anomalous climatic conditions that persisted for over a month.Gaining an understanding of the sequencing of storm events and representing theircharacteristics within flood risk analysis is therefore of...
Published in: | Journal of Flood Risk Management |
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ISSN: | 1753-318X |
Published: |
2008
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Online Access: |
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URI: | https://cronfa.swan.ac.uk/Record/cronfa11979 |
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2018-02-09T04:41:55Z |
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2016-05-01T15:35:23.9218600 v2 11979 2012-07-12 Statistical simulation of flood variables: incorporating short-term sequencing eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2012-07-12 CBAE The pluvial and fluvial flooding in the United Kingdom over the summer of 2007arose as a result of anomalous climatic conditions that persisted for over a month.Gaining an understanding of the sequencing of storm events and representing theircharacteristics within flood risk analysis is therefore of importance. This paperprovides a general method for simulating univariate time series data, with a givenmarginal extreme value distribution and required autocorrelation structure,together with a demonstration of the method with synthetic data. The method isthen extended to the multivariate case, where cross-variable correlations are alsorepresented. The multivariate method is shown to work well for a two-variablesimulation of wave heights and sea surges at Lerwick. This work was prompted byan engineering need for long time series data for use in continuous simulationstudies where gradual deterioration is a contributory factor to flood risk andpotential structural failure. Journal Article Journal of Flood Risk Management 1 1 3 10 1753-318X Autocorrelation; correlation structure;flood risk; marginal extremes; simulation; time series; waves; surges. 31 12 2008 2008-12-31 10.1111/j.1753-318X.2008.00002.x COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2016-05-01T15:35:23.9218600 2012-07-12T14:20:19.2321586 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Y Cai 1 B Gouldby 2 P Hawkes 3 P Dunning 4 Yuzhi Cai 0000-0003-3509-9787 5 |
title |
Statistical simulation of flood variables: incorporating short-term sequencing |
spellingShingle |
Statistical simulation of flood variables: incorporating short-term sequencing Yuzhi Cai |
title_short |
Statistical simulation of flood variables: incorporating short-term sequencing |
title_full |
Statistical simulation of flood variables: incorporating short-term sequencing |
title_fullStr |
Statistical simulation of flood variables: incorporating short-term sequencing |
title_full_unstemmed |
Statistical simulation of flood variables: incorporating short-term sequencing |
title_sort |
Statistical simulation of flood variables: incorporating short-term sequencing |
author_id_str_mv |
eff7b8626ab4cc6428eef52516fda7d6 |
author_id_fullname_str_mv |
eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai |
author |
Yuzhi Cai |
author2 |
Y Cai B Gouldby P Hawkes P Dunning Yuzhi Cai |
format |
Journal article |
container_title |
Journal of Flood Risk Management |
container_volume |
1 |
container_issue |
1 |
container_start_page |
3 |
publishDate |
2008 |
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Swansea University |
issn |
1753-318X |
doi_str_mv |
10.1111/j.1753-318X.2008.00002.x |
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Faculty of Humanities and Social Sciences |
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facultyofhumanitiesandsocialsciences |
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Faculty of Humanities and Social Sciences |
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facultyofhumanitiesandsocialsciences |
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Faculty of Humanities and Social Sciences |
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School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance |
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description |
The pluvial and fluvial flooding in the United Kingdom over the summer of 2007arose as a result of anomalous climatic conditions that persisted for over a month.Gaining an understanding of the sequencing of storm events and representing theircharacteristics within flood risk analysis is therefore of importance. This paperprovides a general method for simulating univariate time series data, with a givenmarginal extreme value distribution and required autocorrelation structure,together with a demonstration of the method with synthetic data. The method isthen extended to the multivariate case, where cross-variable correlations are alsorepresented. The multivariate method is shown to work well for a two-variablesimulation of wave heights and sea surges at Lerwick. This work was prompted byan engineering need for long time series data for use in continuous simulationstudies where gradual deterioration is a contributory factor to flood risk andpotential structural failure. |
published_date |
2008-12-31T00:26:06Z |
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1821363055290744832 |
score |
11.04748 |