Numerical Wave Model Research Articles

Enhancing typhoon wave hindcasting with random forests and BP neural networks in the SWAN model

Forecasting typhoon waves during typhoons is crucial. In this paper, the numerical wave model SWAN was enhanced through integration with two machine learning methods: the Back Propagation Neural Network and Random Forest. This integration facilitated the development of two distinct models, namely SWAN-BP and SWAN-Tree. Through correlation analysis, key input features were identified for the machine learning models. The forecasts from the SWAN model were subsequently utilized as inputs to enhance further wave prediction. These hybrid models were validated using data from Typhoon Doksuri (2023) and Typhoon Nesat (2017). The results indicated significant improvements in predicting typhoon-induced wave heights with both the SWAN-BP and SWAN-Tree models compared to the original SWAN model. Specifically, the SWAN-BP model demonstrated a 33% improvement in accuracy for the Typhoon Doksuri, whereas the SWAN-Tree model exhibited a 24% improvement. For Typhoon Nesat, the accuracy improvements were 23% for the SWAN-BP model and 21% for the SWAN-Tree model. These findings demonstrate that integrating wave numerical models with machine learning techniques can significantly enhance the predictive accuracy of numerical models. This approach offers a cost-effective means to improve the existing wave forecasting database. Traditionally, the direct use of meteorological and oceanographic data for typhoon wave prediction might be compromised by biases inherent in the numerical wave models. However, the SWAN-BP and SWAN-Tree models effectively reduce these biases, thereby providing more accurate and robust predictions. In conclusion, this paper enhances the predictive accuracy of the SWAN model and establishes a crucial foundation for more precise typhoon wave forecasting through the application of machine learning techniques.

Super-resolution on unstructured coastal wave computations with graph neural networks and polynomial regressions

Accurate high-resolution wave forecasts are essential for coastal communities, but local and even coastal coverage is often still missing due to the heavy computational load of modern state-of-the-art wave models. This study presents a machine learning super-resolution approach that drastically reduces the computational effort, while keeping errors negligible for the majority of forecasting applications. The method consists of first computing a wave forecast on a coarse mesh which is then converted to a forecast of finer resolution with the help of machine learning. To demonstrate the feasibility and the potential for practical applications of this approach, we present a case study of a 44-year hindcast along the French Basque coast over an unstructured mesh. We introduce two machine learning approaches, a graph neural network and a polynomial ridge regression and compare their performances in different sea states and spatial environments. Both models exhibit very small prediction errors for the significant wave heights, with Root Mean Square Errors (RMSEs) ranging from 0.3cm to 2cm, depending on the study region, while being up to 80 times faster than a direct computation of a numerical wave model at the corresponding spatial resolution. To the best of our knowledge, this is the first time that a super-resolution approach is extended to unstructured meshes in the field of coastal sciences.

Climate controls on longshore sediment transport and coastal morphology adjacent to engineered inlets

Coastal jetties are commonly used throughout the world to stabilize channels and improve navigation through inlets. These engineered structures form artificial boundaries to littoral cells by reducing wave-driven longshore sediment transport across inlet entrances. Consequently, beaches adjacent to engineered inlets are subject to large gradients in longshore transport rates and are highly sensitive to changes in wave climate. Here, we quantify annual beach and nearshore sediment volume changes over a 9-yr time period along 80 km of wave-dominated coastlines in the U.S. Pacific Northwest. Beach and nearshore monitoring during the study period (2014–2023) reveal spatially coherent, multi-annual patterns of erosion and deposition on opposing sides of two engineered inlets, indicating a regional reversal of longshore-transport direction. A numerical wave model coupled with a longshore transport predictor was calibrated and validated to explore the causes for the observed spatial and temporal patterns of erosion and deposition adjacent to the inlets. The model results indicate that subtle but important changes in wave direction on seasonal to multi-annual time scales were responsible for the reversal in the net longshore sediment transport direction and opposing patterns of morphology change. Changes in longshore transport direction coincided with a reversal in the Pacific Decadal Oscillation (PDO) climate index, suggesting large-scale, multi-decadal climate variability may influence patterns of waves and sediment dynamics at other sites throughout the Pacific basin.

Wave energy assessment and wave converter applicability at the Pacific coast of Central America

Nowadays, numerous governments have instituted diverse regulatory frameworks aimed at fostering the assimilation of sustainable energy sources characterized by reduced environmental footprints. Solar, wind, geothermal, and ocean energies were subject to extensive scrutiny, owing to their ecological merits. However, these sources exhibit pronounced temporal fluctuations. Notably, ocean dynamics offer vast energy reservoirs, with oceanic waves containing significant amounts of energy. In the Central American Pacific context, the exploration of wave energy resources is currently underway. Accurate numerical wave models are required for applied studies such as those focused on the estimation of exploitable wave power; and even more so in Central American region of the Pacific Ocean where existing numerical models simulations have so far relied on coarse resolution and limited validation field data. This work presents a high-resolution unstructured wave hindcast over the Central American Pacific region, implemented using the third-generation spectral wave model WAVEWATCH III over the period between 1979 and 2021. The results of the significant wave height have been bias-corrected on the basis of satellite information spanning 2005 to 2015, and further validation was performed using wave buoy and acoustic Doppler current profiler (ADCP) records located in the nearshore region of the Central America Pacific coast. After correction and validation of the wave hindcast, we employed the dataset for the evaluation and assessment of wave energy and its possible exploitation using different wave energy converters (WECs). This evaluation addressed the need to diverse the energy portfolio within the exclusive economic zones of Guatemala, El Salvador, Honduras, Nicaragua, Costa Rica, Panama, Colombia, and Ecuador in a sustainable manner. Moreover, a comprehensive analysis was carried out on the advantages of harnessing wave energy, juxtaposed with the imperative of regulatory frameworks and the current dearth of economic and environmental guidelines requisite for development within the region.

Development and Validation of NOAA’s 20-year global wave ensemble reforecast

Abstract A new 20-year wave reforecast was generated based on the NOAA Global Ensemble Forecast System, version 12 (GEFSv12). It was produced using the same wave model setup as the NCEP’s operational GEFSv12 wave component, which employs the numerical wave model WAVEWATCH III and utilizes three grids with spatial resolutions of 0.2° and 0.25°. The reforecast comprises 5 members with one cycle per day and forecast range of 16 days. Once a week, it expands to 35 days and 11 members. This paper describes the development of the wave ensemble reforecast, focusing primarily on validation against buoys and altimeters. The statistical analyses demonstrated very good performance in the short range for significant wave height, with correlation coefficients of 0.95-0.96 on day 1, and between 0.86-0.88 within week 1, along with bias close to zero. After day 10, correlation coefficients fall below 0.70. We found that the degradation of predictability and the increase in scatter errors predominantly occur in the forecast lead time between days 4 and 10, in terms of the ensemble mean and individual members, including the control. For week 2 and beyond, a probabilistic spatio-temporal analysis of the ensemble space provides useful forecast guidance. Our results provide a framework for expanding the usefulness of wave ensemble data in operational forecasting applications.

Regional significant wave height forecast in the East China Sea based on the Self-Attention ConvLSTM with SWAN model

Accurate prediction of regional significant wave heights is crucial for marine resource development, especially in areas with limited wave observations. This paper proposes a method for predicting regional significant wave heights by integrating the numerical wave model Simulating Wave Nearshore (SWAN) with the spatio-temporal prediction network Self-Attention ConvLSTM. The SWAN model is used to simulate regional significant wave heights in the East China Sea. To improve simulation accuracy, calibration of wind input, whitecapping dissipation, quadruplet wave–wave interactions, and bottom friction parameterization schemes in the model source terms were conducted, resulting in a simulated correlation coefficient of 0.97 with measured significant wave heights. The regional significant wave height training dataset constructed using the SWAN model preserves the intrinsic relationships between waves, wind speed, and wind direction over a recent period. Subsequently, a regional prediction system is built based on the output of the SWAN model, integrating the self-attention mechanism into the spatio-temporal prediction model ConvLSTM to construct SA-ConvLSTM. This model extracts correlated features among regional waves from the training dataset to predict future significant wave heights at different time intervals. The prediction performance of four spatio-temporal forecasting networks was compared to verify the accuracy of the SA-ConvLSTM model. The average errors in predicting regional significant wave heights at 3, 6, and 12 h were 0.07 m, 0.09 m, and 0.09 m, respectively. The R-Square metrics for these three prediction intervals were 0.85, 0.78, and 0.7, respectively.

Research on numerical modeling of two-dimensional freak waves and prediction of freak wave heights based on LSTM deep learning networks

Freak waves have posed a serious threat to the safety of marine structures. Thus, the accurate simulation and prediction of freak waves are crucial for maritime safety. This paper proposes a nonlinear numerical method based on phase modulation. This method achieves precise time and location simulation of two-dimensional freak waves, while preserving the key statistical characteristics of the wave sequence, thereby obtaining more accurate two-dimensional wave height information. Additionally, this paper constructs a Long Short-Term Memory (LSTM) deep neural network integrated with a Sequence-to-Sequence (seq2seq) framework for predicting sequences of freak wave heights. In this study, we conduct an in-depth predictive analysis of the experimental data for sequences of freak wave heights. And this paper provides a comprehensive assessment of the performance of Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long Short-Term Memory (LSTM) networks in the task of freak wave prediction. The experimental results indicate that the seq2seq-LSTM model demonstrates significant performance advantages in predicting the wave heights of freak waves, particularly for long-term predictions. In summary, the numerical simulation method based on phase modulation and the improved seq2seq-LSTM deep learning model are of significant practical value for enhancing the safety of offshore platforms and ships.

Evidence for littoral convergence and sediment delivery to Mattole submarine canyon, Northern California

At geologic timescales, a significant proportion of the sediment eroded from continents is transferred from rivers to littoral cells, then through submarine canyons to deep ocean basins. Accumulation and occasional discharge of sediment from canyon headwall regions is a likely driver for the sediment gravity flows believed to be responsible for much of this mass transport, but the responsible mechanisms and event timescales in canyon heads are imprecisely defined, largely due to the drowning of canyon heads since the last glaciation and the resulting dearth of modern depositional environments to observe. We investigated a littoral cell adjacent to Mattole submarine canyon in Northern California to better understand the influence of bathymetry and coast shape on sediment trajectories and hypothesize that canyon heads promote littoral sediment accumulation due to bathymetric sheltering of the adjacent coast from waves and convergence of littoral cells exposed to varying wave direction. The uppermost gullies of Mattole Canyon extend to the littoral cell, and a train of sediment waves within the uppermost thalweg of Mattole Canyon suggests that gravity-driven flows of shore-derived sediment may have recently occurred. Mattole Canyon and nearby Mendocino Canyon flow into the deep Mendocino Channel, which has the highest observed Late Holocene sediment accumulation rates in Northern California. We documented the beach slope, grain size, and sediment provenance along 15 km of coast adjacent to Mattole Canyon and conducted numerical wave modeling to infer the dominant direction and magnitude of sediment transport, relative wave sheltering by bathymetry, and estimated sediment mobility in the vicinity of the canyon head over a multi-year period. South of the canyon headwall, in the vicinity of the Mattole River outlet, coarser sediment rich in metasandstone clasts and steeper beach slopes coincide with decadal-scale beach accretion. North of the canyon head, sediments are generally finer, beaches are flatter, clast lithologies include more quartz and mudstone grains, and there is decadal-scale net erosion. Wave modeling suggests the Mattole headwall region is subject to sustained sheltering from waves due to canyon bathymetry, littoral sediment convergence above the canyon head for much of a typical year, and occasional bed entrainment of sediment in the canyon’s uppermost gullies by large waves. Larger winter waves from the northwest likely result in net southerly drift north of the canyon head, with a high likelihood of preferentially transporting fine-grained, more quartz-rich sediment towards the Mattole headwall. Smaller summer swells from the west and south likely results in net northerly transport of metasandstone clast-rich Mattole River-derived sediment towards the canyon head. The imbalance in seasonality in wave conditions, coast shape, and sediment delivery by the Mattole River and other coastal creeks is broadly consistent with the spatial patterns in grain size, mineralogy, slope, and beach width that we observe in the vicinity of the canyon, and the net delivery of sediment to the Mattole Canyon head. An approximate mass balance of sediment flux from coastal streams feeding Mattole River littoral cell suggests that sediment volumes likely accumulate in the canyon headwall region that could supply several density flow events in a typical decade. These findings highlight the importance of the connectivity of the canyon to the nearshore region for the processes and products in canyons and fans. We hypothesize that feedbacks between canyon bathymetry, sediment accumulation, and mass evacuating flows promotes the long-term persistence of canyon systems.

Computational study on complex wave hydrodynamics of multidirectional extreme waves at fringing reef

Due to the special topography of coral reefs, most of the energy of incoming waves can be reduced through the wave breakings at the reef edge, thus playing a vital role in protecting the coastal regions behind coral reefs. In severe weather conditions, wind-generated waves tend to propagate in multiple directions in real ocean environment. The abrupt multidirectional wave-focusing mechanisms may result in the formation of huge waves in both deep and shallow waters, threatening the safety of coastal infrastructures and well-being of coastal communities at the reef islands. Therefore, it is crucial to thoroughly examine the complex hydrodynamics of multidirectional extreme waves at the fringing reefs. Previous studies on reef wave hydrodynamics predominantly focus on unidirectional waves. Only a small number of studies have considered the wave hydrodynamics of fringing reefs under multidirectional waves, let alone multidirectional extreme waves. To address the knowledge gap in previous studies, this study analyzes the complex hydrodynamic processes of multidirectional extreme waves at the fringing reef by applying a nonhydrostatic numerical wave model (NHWAVE). Influences of several key factors are analyzed, such as significant wave height, peak wave period, water depth, incident wave angle, and reef topography. It is expected that the research findings of this study will contribute to a deeper understanding of the hydrodynamics of multidirectional extreme waves at the fringing reef.

A two-module bias-correction model for sea wave hindcasting based on the long-short term memory neural network

The lack of observations and biases of winds and bathymetry in the coastal region result in significant biases in the numerical modeling of sea waves. In this study, we establish a two-module bias correction model based on the Long-Short Term Memory neural network (denoted as TM-BCM) which accounts for the data imbalance by separately training the neural network for low and high sea states. The in-situ observations in 2020 and 2021 are used to model training and testing, respectively. The test results show that TM-BCM significantly reduces the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of modeled sea waves by 52.9% and 65.5%, respectively. In addition, TM-BCM outperforms the single-module model (denoted as SM-BCM) that does not account for the imbalance of training data. Our results highlight the great value of deep learning in correcting biases for numerical models and the importance of accounting for data imbalance in training the neural network.

A Hybrid Extended Kalman Filter Based on Parametrized ANNs for the Improvement of the Forecasts of Numerical Weather and Wave Prediction Models

A hybrid optimization filter for weather and wave numerical models is proposed and tested in this study. Parametrized Artificial Neural Networks are utilized in conjunction with Extended Kalman Filters to provide a novel postprocess strategy for 10 m wind speed, 2 m air temperature, and significant wave height simulations. The innovation of the developed model is the implementation of Feedforward Neural Networks and Radial Basis Function Neural Networks as estimators of an exogenous parameter that adjusts the covariance matrices of the Extended Kalman Filter process. This hybrid system is evaluated through a time window process leading to promising results, thus enabling a decrease in systematic errors alongside the restriction of the error variability and the corresponding forecast uncertainty. The obtained results showed that the average reduction of the systematic error exceeded 75%, while the corresponding nonsystematic part of that error decreased by 35%.

DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin

Abstract. We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the behaviour of a numerical surface ocean wave model (Simulating WAves Nearshore, SWAN) at a sparse set of locations, thus enabling numerically cheap large-ensemble prediction over synoptic to climate timescales. DELWAVE was trained on COSMO-CLM (Climate Limited-area Model) and SWAN input data during the period of 1971–1998, tested during 1998–2000, and cross-evaluated over the far-future climate time window of 2071–2100. It is constructed from a convolutional atmospheric encoder block, followed by a temporal collapse block and, finally, a regression block. DELWAVE reproduces SWAN model significant wave heights with a mean absolute error (MAE) of between 5 and 10 cm, mean wave directions with a MAE of 10–25°, and a mean wave period with a MAE of 0.2 s. DELWAVE is able to accurately emulate multi-modal mean wave direction distributions related to dominant wind regimes in the basin. We use wave power analysis from linearised wave theory to explain prediction errors in the long-period limit during southeasterly conditions. We present a storm analysis of DELWAVE, employing threshold-based metrics of precision and recall to show that DELWAVE reaches a very high score (both metrics over 95 %) of storm detection. SWAN and DELWAVE time series are compared to each other in the end-of-century scenario (2071–2100) and compared to the control conditions in the 1971–2000 period. Good agreement between DELWAVE and SWAN is found when considering climatological statistics, with a small (≤ 5 %), though systematic, underestimate of 99th-percentile values. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.

Regional evaluation of simulated waves during tropical storm events in the Gulf of Mexico

High waves in the Gulf of Mexico are mainly originated in tropical cyclones and winter storms. The availability of accurate estimations of high wave characteristics is an essential prerequisite for marine spatial planning, coastal zone management, and marine developments over the Northeastern Gulf of Mexico (NeGoM). Moreover, accurate wave simulation relies upon accurate wind field estimations. The wind field data used as input for numerical wave models is typically obtained from global and regional atmospheric model outputs. However, these model outputs may exhibit higher bias relative to observations, especially during fast-traveling tropical weather events. This study evaluates wave sensitivity over the NeGoM to different input wind speed and direction sources using unstructured SWAN model simulations. Five wind data products were employed in this study from ECMWF and NCEP global and regional models to examine the impact of wind forcing on wave simulations. The quality of wind field input data from these sources was evaluated relative to available observations (wind speed and direction) at National Data Buoy Center stations. A detailed sensitivity analysis is carried out to determine optimal wave model configuration. The model's performance is evaluated by comparing its wave simulation results with observational data during selected periods, simultaneously assessing the quality and sufficiency of wind data inputs from various sources. Our study shows that wind fields from FNL and NAM datasets are better correlated with observational data, in terms of vector correlation. Simulations using ECMWF – Real-Time product, combined with Janssen white-capping scheme better estimate wave conditions over the NeGoM compared with the other wind forcings used in this study. Moreover, Janssen white-capping scheme seems to be more realistic for estimations during Hurricane Michael; while for Hurricane Ida, Janssen generation scheme tends to overestimate the wave heights and Komen provides more accurate results.

GWSM4C: A global wave surrogate model for climate simulation based on a convolutional architecture

Accurate and rapid ocean surface wave simulations are essential for navigation safety, marine activity, and climate change research. However, traditional numerical wave models incur substantial computational costs, especially in long-term simulations for climatology research. Deep-learning methods have shown promise in enabling faster simulations with fewer computational resources. However, the cumulative error in current deep-learning wave models limits their use as surrogate component models in climate modeling. In this work, a deep-learning model, named the Global Wave Surrogate Model for Climate simulation (GWSM4C), was developed based on a convolutional architecture, which accounts for the physical processes (wind waves and swells) of waves. Because GWSM4C can automatically generate initial conditions for each simulation moment depending only on historical wind fields, the accumulation of errors from previous simulated wave states can be avoided. The experimental results demonstrated that simulated global significant wave height produced by GWSM4C exhibited a correlation coefficient of 0.94 and a root-mean-square error of 0.21 m compared with the traditional numerical wave model. Furthermore, the generalization test of the GWSM4C indicates that it is feasible to use GWSM4C for long-term wave simulation in climatology-related studies or to integrate it into a coupled climate model as a surrogate wave component model.

Statistical downscaling of coastal directional wave spectra using deep learning

The modelling of coastal Directional Wave Spectra (DWSs) often requires downscaling techniques integrating DWSs from open ocean boundaries. Dynamic downscaling methods reliant on numerical wave models are often computationally expensive. In coastal areas, wave dynamics are strongly influenced by the bathymetry and coastal morphology, implying that once the DWSs at the open ocean boundary are known, the DWSs at various locations along the coast are almost determined. This property can be utilized for statistical downscaling of coastal DWSs. This study presents a deep learning approach to compute coastal DWSs from open ocean DWSs. The performance of the proposed downscaling model was evaluated using both numerical wave model data and buoy data in the Southern California Bight. The results show that the deep learning approach can effectively and efficiently downscale coastal DWSs without relying on any predefined spectral shapes, thereby showing potential for coastal spectral wave climate studies.

From apparent attenuation towards physics-based source terms – a perspective on spectral wave modeling in ice-covered seas

Numerical modeling of waves in sea ice covered regions of the oceans is important for many applications, from short-term forecasting and ship route planning up to climate modeling. In spite of a substantial progress in wave-in-ice research that took place in recent years, spectral wave models – the main tool for wave modeling at regional and larger scales – still don’t capture the underlying physics and have rather poor predictive skills. This article discusses recent developments in wave observations and spectral wave modeling in sea ice, identifies problems and shortcomings of the approaches used so far, and sketches future directions that, in the opinion of the author, have the potential to improve the performance of wave-in-ice models.

Analysis of extreme wave probabilistic return value based on the 20-year spectral and empirical wave modeling approach in the Coastal Area of Pangandaran

Abstract The coastal area of Pangandaran is located on the southern coast of Java Island, Indonesia. It offers stunning beach panoramas, and tempting waves for surfers, while also providing livelihoods for people who depend on the marine biodiversity and abundance of fish caught in the surrounding waters. However, its exposure to the Indian Ocean makes it susceptible to extreme waves, posing risks to tourism, fishing, and safety. This study aims to conduct an extreme wave hazard analysis based on the Probabilistic Return Value (PRV) using Significant Wave Height (SWH) data for over 20 years of data to obtain a wave climate value in the Batukaras area. The SWH values are obtained from two methods, numerical spectral wave modeling using Simulating Waves Nearshore (SWAN) and calculations using an empirical formula from the Joint North Sea Wave Project (JONSWAP), both using wind data from the ERA-5 Satellite. Based on these calculations, the Gumbel distribution was analysed to determine the PRV value on the coast. By estimating the return value of extreme waves based on the spectral SWAN method, for a return period of 300 years, extreme wave events occur at a height of 2.01 m to 2.64 m at a depth of 5-21 m. Meanwhile, the empirical JONSWAP method generates the same probability for wave heights of 7.19 m to 12.81 m based on statistical predictions based on modeling data. Based on these results, community and tourist preparedness on the coast of Batukaras Village is expected to increase, particularly during bad weather.

Storm damage assessment of a port in the Southwestern Black Sea

This paper presents a comprehensive investigation of the storm damage at a commercial port located in the Southwestern Black Sea Region that occurred on January 18–19, 2018. One week after the event, a field survey was conducted at the port focusing on significantly damaged mound breakwaters and protection structures that failed at several sections. A numerical wave modeling study is carried out to estimate the wave characteristics at deep sea, nearshore, and inside the port to assess the observed damage during the field survey. Widely used numerical models WAVEWATCH III, SWAN, and SWASH are utilized using nested computational domains and calibrated based on satellite measurements. As a result, the significant wave height of the storm is estimated as 7.8 m with a peak period of 12.4 s near the port area, approaching mainly from the northwest direction. The damage mechanisms of the mound structures are discussed based on the field observations and the wave modeling studies. The insufficient seaside armor unit sizes and the orientation of the breakwaters are found to be the main reasons for the damage.

An analysis of surface waves in the Caribbean Sea based on a high-resolution numerical wave model

Surface waves are extremely important in a large variety of oceanographic applications and thus, the study of their spatiotemporal characteristics remains crucial. This study analyzes waves in the Caribbean Sea (CS) and western Atlantic Ocean (AO) using a high-resolution (HR) Simulating WAves Nearshore model validated with buoy observations and paired with a HR bathymetric dataset from 2010 – 2019. Island sheltering effects are examined but special attention is given to these effects under Hurricane Dorian in The Bahamas using observations from the China-France Oceanographic Satellite. Results illustrate that wave heights within the CS fluctuated with Caribbean Low-Level Jet activity, but a different wave regime exists within the AO. While wind waves overwhelmingly dominate the wave field and this is true even in the AO, surprisingly, the contribution of swell in the central CS was equal to one site in the AO. Possibly, due to interaction with the shallow Nicaraguan Rise, wave heights were strongly (depth-induced) refracted nearly 45°, a feature unseen in previous research using coarse bathymetric datasets. Island sheltering effects were pervasive and were naturally most pronounced under hurricane conditions. Crucially, New Providence in The Bahamas is vulnerable to hurricane-forced waves funneled through the Grand Bahama and Northeastern Providence Channels.

Validation Of An Efficient Non-Hydrostatic Wave Model As A Design Tool For Foreshores In Physical Models

In the design of physical model experiments in coastal engineering, it is common that the construction of a foreshore is necessary to obtain the desired wave conditions at a given location, usually close to a structure being tested. In addition to the commonly used spectral wave parameters to describe the target wave conditions, the wave height distribution and associated parameters (such as Hmax and H2%), wave periods and infra-gravity waves can be important to reproduce properly as well. Wave height distributions are typically important for tests where e.g. wave run-up or wave forces are of interest. Since the construction of foreshores is labour intensive (and thus expensive), it is useful to be able to check a priori whether the target wave conditions are met with a given foreshore design and whether the chosen transition slope does not significantly influence the wave conditions. One way to do this is by using numerical wave models. For a numerical model to be a useful design tool for physical model layouts, the predicted wave transformation (over the foreshore) needs to be sufficiently accurate. One option is to use detailed CFD wave models for this purpose, such as OpenFOAM, which has been shown to accurately reproduce wave transformation (Jacobsen et al., 2015; Jacobsen et al., 2018). CFD models, however, typically feature high computational demand and consequentially computational times in the order of days. This is a significant disadvantage in the context of designing a physical model experiment, for which the layout and configuration is often an iterative process, which would be hampered by overly long computational times. On the other hand, spectral wave models, like SWAN (Booij et al., 1996), are much faster but do not model all the individual waves rendering it unable to model exceedance distributions. As a middle-ground, it seems that the XBeach model (Roelvink et al., 2009) used in its 2-layer non-hydrostatic mode (De Ridder et al., 2021) might present a workable compromise between computational time and accurate representation of the wave transformation. The model has been validated by De Ridder et al. (2021) on bichromatic waves, complex barred beach geometry and a fringing reef for bulk wave parameters and spectral properties. Expanding on earlier validation work, in this work the ability of the XBeach 2-layer non-hydrostatic model (XBeach-nh) to reproduce wave height exceedance distributions over a sloping foreshore is validated using wave flume data, described in Sections 2 and 3. The preliminary results of the validation effort are shown in Section 4.