Over the last couple of decades, advancements in high-performance computing have allowed phase-resolving, Boussinesq-type numerical wave models to be more practical in addressing nearshore coastal wave processes. As such, the open-source Fully Nonlinear Wave model–Total Variation Diminishing (FUNWAVE-TVD) numerical wave model has become more ubiquitous across all scientific and engineering-focused R&D organizations, including academic, government, and industry partners. In collaboration with the US Army Engineer Research and Development Center, Coastal and Hydraulics Laboratory; the University of Delaware; and HR Wallingford, a robust testbed has been developed to allow users to benchmark their applications against new releases of the model. The testbed presented here includes analytical, laboratory, and field cases, to provide guidance on the operational utility of FUNWAVE-TVD and examines numerical convergence, accuracy, and performance in modeling wave generation, propagation, wave breaking, and moving shorelines in nearshore wind-wave applications. A brief discussion on the efficiency of the model across parallel computing platforms is also provided.

Abstract In this work, we carried out a case study of numerical simulations of low-frequency wave motions at the Marina di Carrara Harbor, where field observations have been conducted from 2005 to ...

: Abstract The design of breakwaters and other coastal structures around the UK requires the assessment of the joint probability of extreme waves and sea levels. There is a standard simplified approach applied within the UK that uses joint exceedance contours of waves and sea levels. This simplified approach can be non-conservative and lead to the under-design of coastal structures unless correction factors are applied. These limitations have been known for a number of years and alternative, more robust, methods have been applied in the past. These alternative methods are risk-based as they enable the probability of the consequence (structural or serviceability failure) to be established. The more robust methods are not, however, widely used in current practice. This paper describes the development of a statistically robust nearshore multivariate extremes data set around the coastline of England. The dataset can, in principle, be used to undertake risk-based design of structures, thereby overcoming the limitations of existing practice.

In an ever increasing need to minimise costs, most coastal engineering work requires reliable information on the environmental forces and governing physical processes that need to be taken into account when designing or assessing the performance of coastal structures. This often involves the application of complex physical process based numerical models (simulators) that can be computationally expensive to run. In general, the more complex the process representation, the more computationally expensive the model simulations will be. Coastal process simulations, flood risk analysis and flood forecasting can all require substantial computational resources. The situation is exacerbated when repeated simulation runs are required in a sensitivity analysis or uncertainty analysis, for example. When the number of model simulations are excessive, it is possible to make a compromise on time and space resolution or processes represented or apply alternative methods typically with a reduction in accuracy. One such relatively simply approach is populate or train a look-up table (LUT) using only a subset of the full set of runs and then use the LUT to predict the output for the relevant input values typically using linear interpolation. A limitation with such approaches is the number of training simulations required to maintain accuracy by adequately representing the input parameter space increases significantly with increasing dimension. In addition, the relationship between the inputs and outputs can often be non-linear and hence linear interpolation is not necessarily an appropriate interpolation method to apply. This paper focusses on the use of the Gaussian process emulator (GPE) meta-modelling approach as an alternative approach to traditional LUTs. Using the specific example of wave transformation with the Simulating Waves Nearshore (SWAN) wave model, a GPE has been compared with a traditional LUT approach. In addition, the method of selecting the design points used to train the GPE has been explored and a refined algorithm, that takes account prior knowledge of the boundary conditions, has been introduced. It is shown that the GPE approach requires significantly fewer model runs to obtain similar or higher accuracy, enabling a substantial reduction in overall computation time when compared to a traditional LUT approach. The refined algorithm also shows significant computational efficiencies, meaning that potential compromises on model resolution or the physical processes can be limited.

It is widely recognised that coastal flood events can arise from combinations of extreme waves and sea levels. For flood risk analysis and the design of coastal structures it is therefore necessary to assess the joint probability of the occurrence of these variables. Traditional methods have involved the application of joint probability contours, defined in terms of extremes of sea conditions that can, if applied without correction factors, lead to the underestimation of flood risk and under-design of coastal structures. This paper describes the application of a robust multivariate statistical model to analyse extreme offshore waves, wind and sea levels around the coast of England. The approach described here is risk based in that it seeks to define extremes of response variables directly, rather than the joint extremes of sea conditions. The output of the statistical model comprises a Monte Carlo simulation of extreme events. These distributions of extreme events have been transformed from offshore to nearshore using a statistical emulator of a wave transformation model. The resulting nearshore extreme sea condition distributions have the potential to be applied for a range of purposes. The application is demonstrated using two structures located on the south coast of England.

It has long been recognised that extreme coastal flooding can arise from the joint occurrence of extreme waves, winds and sea levels. The standard simplified joint probability approach used in England and Wales can result in an underestimation of flood risk unless correction factors are applied. This paper describes the application of a state-of-the-art multivariate extreme value model to offshore winds, waves and sea levels around the coast of England. The methodology overcomes the limitations of the traditional method. The output of the new statistical analysis is a Monte-Carlo (MC) simulation comprising many thousands of offshore extreme events and it is necessary to translate all of these events into overtopping rates for use as input to flood risk assessments. It is computationally impractical to transform all of these MC events from the offshore to the nearshore. Computationally efficient statistical emulators of the SWAN wave transformation model have therefore been constructed. The emulators translate the thousands of MC events offshore. Whilst the methodology has been applied for national flood risk assessment, it has the potential to be implemented for wider use, including climate change impact assessment, nearshore wave climates for detailed local assessments and coastal flood forecasting.

New innovations are emerging which offer opportunities to improve forecasts of wave conditions. These include probabilistic modelling results, such as those based on an ensemble of multiple predictions which can provide a measure of the uncertainty, and new sources of observational data such as GNSS reflectometry and FerryBoxes, which can be combined with an increased availability of more traditional static sensors. This paper outlines an application of the Bayesian statistical methodology which combines these innovations. The method modifies the probabilities of ensemble wave forecasts based on recent past performance of individual members against a set of observations from various data source types. Each data source is harvested and mapped against a set of spatio-temporal feature types and then used to post-process ensemble model output. A prototype user interface is given with a set of experimental results testing the methodology for a use case covering the English Channel.

Flood risk analysis often involves the integration of multivariate probability distributions over a domain defined by a consequence function. Often, solutions of this risk integral involves Monte-Carlo sampling techniques, whereby 1000's of potential flood events are generated. It is necessary to evaluate the consequence of flooding for each sampled event. A significant computational time is required in running flood related physical process models, making it computationally impractical to evaluate flood risk using this approach. To overcome the computational challenges, this paper focusses on the Gaussian Process Emulator (GPE) meta-modelling approach. Traditionally, a "look-up table" method is used when a large number of simulations from a numerical model are required. This approach typically involves simulating conditions defined across a regular matrix, and then linearly interpolating intermediate conditions. In this paper we compare a traditional "look-up table" approach to the GPE and analyse their performance in approximating SWAN wave transformation model. In both cases, selecting an appropriate training design set is important and is taken into consideration in the analysis. The analysis shows that the GPE approach requires significantly fewer SWAN runs to obtain similar (or better) accuracies, enabling a substantial reduction in computation time, hence aiding the practicality of Monte-Carlo sampling techniques in advanced flood risk modelling.

Many new port and terminal developments require long, deep navigation channels through shallow water to allow access. Such channels can have a significant effect on the propagation of waves and it is important to consider this at an early design stage to minimise wave disturbance at berths. This paper reviews the hydrodynamic processes involved and presents examples where the orientation of navigation channels can be beneficial or detrimental. Some innovative ways to reduce waves through the optimisation of channel alignment and dredged-area design are described.

Coastal Structures 2007, pp. 1552-1561 (2009) No AccessOVERTOPPING REDUCTION SCHEMES FOR DEPTH-LIMITED SWELL WAVES ON AN ATLANTIC COAST JETTYTim Pullen, Peter Fitzsimons, and Nigel TozerTim PullenHR Wallingford, Howbery Park, Wallingford, OX10 8BA, UK, Peter FitzsimonsCentral Procurement Directorate, Hydebank, Belfast, NI, UK, and Nigel TozerHR Wallingford, Howbery Park, Wallingford, OX10 8BA, UKhttps://doi.org/10.1142/9789814282024_0137Cited by:0 PreviousNext AboutSectionsPDF/EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsRecommend to Library ShareShare onFacebookTwitterLinked InRedditEmail Abstract: A small fishing jetty on Arran Island on the West coast of Northern Ireland was required. The proposed structure is exposed fully to waves from the Atlantic and initial wave overtopping calculations showed that overtopping discharges at the pier could potentially reach dangerous levels at regular intervals. Modifications to the original design were examined to establish a design that would ensure the structure could be kept serviceable for most of the time. Initial analysis showed that potentially hazardous overtopping could occur as often as 74 times a year, or approximately once every 5 days. Empirical methods and procedures developed during recent research into wave overtopping were used to establish potential reductions from the initial predictions by a factor of 80. FiguresReferencesRelatedDetails Coastal Structures 2007Metrics History PDF download

The project described in this paper includes development of surge ensemble modelling for the UK, and demonstration of probabilistic coastal flood forecasting for an area in the Irish Sea. Its purpose is to develop, demonstrate, and evaluate probabilistic methods for surge, nearshore wave, and coastal flood forecasting in England and Wales, but the concepts and models would be equally applicable elsewhere. The main features that distinguish these methods from existing practice are in the use of hydraulic models extending through to action at coastal defences, and the use of ensemble and other probabilistic approaches throughout. Use of offshore forecasts to estimate the likelihood of high overtopping involves transformation of wave forecasts through the nearshore and surf zones, and the combined effects of wind, waves and sea level in causing overtopping. The model is outlined and site-specific measurements of waves and overtopping discharges are described and compared to the probabilistic predictions of the model.