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Simulation of Wave Time Series with a Vector Autoregressive Method

Antonios Valsamidis, Yuzhi Cai Orcid Logo, Dominic Reeve Orcid Logo

Water, Volume: 14, Issue: 3, Start page: 363

Swansea University Authors: Antonios Valsamidis, Yuzhi Cai Orcid Logo, Dominic Reeve Orcid Logo

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DOI (Published version): 10.3390/w14030363

Abstract

Joint time series of wave height, period and direction are essential input data to computational models which are used to simulate diachronic beach evolution in coastal engineering. However, it is often impractical to collect a large amount of the required input data due to the expense. Based on the...

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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/cronfa59229
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first_indexed 2022-01-24T12:03:53Z
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spelling 2022-08-05T11:02:44.9677349 v2 59229 2022-01-24 Simulation of Wave Time Series with a Vector Autoregressive Method 655e856b5d6b96f6a17a5d8729cca8d5 Antonios Valsamidis Antonios Valsamidis true false eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 3e76fcc2bb3cde4ddee2c8edfd2f0082 0000-0003-1293-4743 Dominic Reeve Dominic Reeve true false 2022-01-24 FGSEN Joint time series of wave height, period and direction are essential input data to computational models which are used to simulate diachronic beach evolution in coastal engineering. However, it is often impractical to collect a large amount of the required input data due to the expense. Based on the nearshore wave records offshore of Littlehampton in Southeast England over the period from 1 September 2003 to 30 June 2016, this paper presents a statistical method to obtain simulated joint time series of wave height, period and direction covering an extended time span of a decade or more. The method is based on a vector auto-regressive moving average algorithm. The simulated times series shows a satisfactory degree of stochastic agreement between original and simulated time series, including average value, marginal distribution, autocorrelation and cross-correlation structure, which are important for Monte Carlo modelling of shoreline evolution, thereby allowing ensemble prediction of shoreline response to a variable wave climate. Journal Article Water 14 3 363 MDPI AG 2073-4441 VAR model; wave time series; autocorrelation; cross-correlation 26 1 2022 2022-01-26 10.3390/w14030363 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University This research was funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under the MORPHINE project (grant EP/N007379/1). 2022-08-05T11:02:44.9677349 2022-01-24T11:08:50.5754269 Faculty of Science and Engineering School of Engineering and Applied Sciences - Uncategorised Antonios Valsamidis 1 Yuzhi Cai 0000-0003-3509-9787 2 Dominic Reeve 0000-0003-1293-4743 3 59229__22259__8f9f2e77881b481fb1a49a9b784dad49.pdf 59229.pdf 2022-01-31T16:35:54.0706221 Output 5514598 application/pdf Version of Record true This is an open access article distributed under the Creative Commons Attribution License true eng https://creativecommons.org/licenses/by/4.0/
title Simulation of Wave Time Series with a Vector Autoregressive Method
spellingShingle Simulation of Wave Time Series with a Vector Autoregressive Method
Antonios Valsamidis
Yuzhi Cai
Dominic Reeve
title_short Simulation of Wave Time Series with a Vector Autoregressive Method
title_full Simulation of Wave Time Series with a Vector Autoregressive Method
title_fullStr Simulation of Wave Time Series with a Vector Autoregressive Method
title_full_unstemmed Simulation of Wave Time Series with a Vector Autoregressive Method
title_sort Simulation of Wave Time Series with a Vector Autoregressive Method
author_id_str_mv 655e856b5d6b96f6a17a5d8729cca8d5
eff7b8626ab4cc6428eef52516fda7d6
3e76fcc2bb3cde4ddee2c8edfd2f0082
author_id_fullname_str_mv 655e856b5d6b96f6a17a5d8729cca8d5_***_Antonios Valsamidis
eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
3e76fcc2bb3cde4ddee2c8edfd2f0082_***_Dominic Reeve
author Antonios Valsamidis
Yuzhi Cai
Dominic Reeve
author2 Antonios Valsamidis
Yuzhi Cai
Dominic Reeve
format Journal article
container_title Water
container_volume 14
container_issue 3
container_start_page 363
publishDate 2022
institution Swansea University
issn 2073-4441
doi_str_mv 10.3390/w14030363
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 Engineering and Applied Sciences - Uncategorised{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Uncategorised
document_store_str 1
active_str 0
description Joint time series of wave height, period and direction are essential input data to computational models which are used to simulate diachronic beach evolution in coastal engineering. However, it is often impractical to collect a large amount of the required input data due to the expense. Based on the nearshore wave records offshore of Littlehampton in Southeast England over the period from 1 September 2003 to 30 June 2016, this paper presents a statistical method to obtain simulated joint time series of wave height, period and direction covering an extended time span of a decade or more. The method is based on a vector auto-regressive moving average algorithm. The simulated times series shows a satisfactory degree of stochastic agreement between original and simulated time series, including average value, marginal distribution, autocorrelation and cross-correlation structure, which are important for Monte Carlo modelling of shoreline evolution, thereby allowing ensemble prediction of shoreline response to a variable wave climate.
published_date 2022-01-26T04:16:22Z
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score 11.013731

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