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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
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa59229
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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 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.
Keywords: VAR model; wave time series; autocorrelation; cross-correlation
College: College of Engineering
Funders: This research was funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under the MORPHINE project (grant EP/N007379/1).
Issue: 3
Start Page: 363