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.
Highlights
Simulation of time series plays an important role in many areas due to the high cost in obtaining in situ measurements
In the field of coastal engineering simulation of wave time series has been used for estimating the duration of storm events and their spacing in time, see e.g., [1,2], with the aim of assessing the risk of serious beach erosion [3,4]
Distributions of wave test height, periodthat andyitdirection and their autocorrelations and crosscorrelations were estimated from this sequence
Summary
Simulation of time series plays an important role in many areas due to the high cost in obtaining in situ measurements. In the field of coastal engineering simulation of wave time series has been used for estimating the duration of storm events and their spacing in time, see e.g., [1,2], with the aim of assessing the risk of serious beach erosion [3,4]. Carlo simulation of coastal flooding or erosion, wave sequences with similar statistics are required. This problem is challenging and may be stated as simulating a non-stationary non-normal, correlated, trivariate stochastic process, given a sample or realisation of the process. Several methods have been proposed for tackling this type of problem. Li and Winker [5] proposed a Monte Carlo method, or a quasi
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