SWAN Model Research Articles

Maximum waves in the Black Sea based on the results of numerical modeling

In this paper, a comparison of the wind waves parameters calculations in the Black and Azov seas, using two common spectral wave models: DHI MIKE 21 SW and SWAN was made. The fields of the wind waves main parameters (wave heights, periods, directions of propagation) were obtained for a climatic period of time from 1979 to 2018. Comparison of the calculation results shows that with the accepted model settings, the SWAN model, compared with MIKE, overestimates the values of wave heights with weak and moderate waves and underestimates with the extreme ones. Estimation of the maximum wave heights on the Black Sea, possible once in a given number of years, performed on two different models, was made. It showed that for the conditions of the Black Sea the difference between the calculated values of significant wave heights of rare frequency for the MIKE and SWAN models does not exceed 12%. The maximum significant wave heights, possible once in a hundred years, in the Black Sea can reach 11-12 m.

An AI model for predicting the spatiotemporal evolution process of coastal waves by using the Improved-STID algorithm

Accurate and rapid prediction of water wave evolution is crucial for ensuring the safe and healthy operation of ocean and coastal engineering. Using hourly wave data from the coastal areas of the Hawaiian Islands from 2019 to 2021, this study developed an artificial intelligence model for rapidly predicting the spatiotemporal evolution of coastal waves by improving the STID algorithm. In the study, the SWAN wave model was used to generate an initial training dataset for driving Improved-STID algorithm, where the Improved-STID algorithm and SWAN model share the same unstructured computational grid with good adaptability to complex topography. The proposed Improved-STID algorithm takes into account the overall spatiotemporal correlations of wave evolution across different regions. The results show that on the basis of prediction efficiency on a par with the original STID, the Improved-STID can be more accurately applied to the spatiotemporal evolution process of coastal waves. The prediction errors in this study are: the RMSE of 0.23 m, MAE of 0.14 m, MAPE of 17.96 %, and the calculation time of the proposed AI model is only about 1.8 % of that of the SWAN wave model.

Development and performance of a high-resolution surface wave and storm surge forecast model: application to a large lake

Abstract. A real-time forecast model of surface hydrodynamics in Lake Ontario (Coastlines-LO) was developed to automatically predict storm surges and surface waves. The system uses a dynamically coupled Delft3D–SWAN model with a structured grid to generate 48 h predictions for the lake that are updated every 6 h. The lake surface is forced with meteorological data from the High Resolution Deterministic Prediction System (HRDPS). The forecast model has been running since May 2021, capturing a wide variety of storm conditions. Good agreement between observations and modelled results is achieved, with root mean squared errors (RMSEs) for water levels and waves under 0.02 and 0.26 m, respectively. During storm events, the magnitude and timing of storm surges are accurately predicted at nine monitoring stations (RMSE <0.05 m), with model accuracy either improving or remaining consistent with decreasing forecast length. Forecast significant wave heights agree with observed data (1 %–12 % relative error for peak wave heights) at four wave buoys in the lake. Coastlines-LO forecasts for storm surge prediction for two consecutive storm events were compared to those from the Great Lakes Coastal Forecasting System (GLCFS) to further evaluate model performance. Both systems achieved comparable results with average RMSEs of 0.02 m. Coastlines-LO is an open-source wrapper code driven by open data and has relatively low computational requirements compared to GLCFS, making this approach suitable for forecasting marine conditions in other coastal regions.

Artificial intelligence and wealth inequality: A comprehensive empirical exploration of socioeconomic implications

This study introduces a global database on artificial intelligence (AI) capital stock and related AI indicators. Using the data constructed, we investigate the impact of AI and capital stock accumulation on wealth inequality, a dimension not extensively explored in the literature. This study contributes to the growing body of literature on the socioeconomic consequences of AI, with implications for scholars, policymakers, and corporate executives. An innovative database detailing AI capital stock is developed by incorporating data from various sources, including corporate reports, industry databases, and scholarly literature. This novel dataset, focusing on the US, the EU, and Japan from 1995 to 2020, is a critical resource for future investigations. The research methodology is centered on an extended Solow–Swan model, conceptualizing AI as a form of capital that can substitute for or complement traditional forms of labor. A panel-corrected standard errors model is used to analyze the data, accounting for potential cross-sectional dependence and heteroscedasticity. Our findings reveal a positive and statistically significant correlation between AI technology adoption, AI capital stock accumulation, and wealth disparity. The analysis further indicates a complex interaction between income and wealth disparities, suggesting a mutually reinforcing cycle. This study fills a significant gap in the existing literature by offering a novel perspective on the distributional impact of AI. Our results underscore the importance of considering the broader socioeconomic implications of AI, extending beyond considerations of immediate productivity and economic growth. This study offers valuable insights for policy formulation and business decision making, emphasizing the necessity of a comprehensive understanding of the influence of AI on wealth distribution.

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.

An assessment of the impact of boundary conditions in dynamical downscaling techniques for fetch-limited waves

ABSTRACT The handling of boundary conditions (BCs) is a key issue for the development of dynamical downscaling techniques in which nearshore wave models require the definition of the sea conditions at the offshore boundaries. This study addresses the implications of using simplified BCs represented by bulk or partitioned wave parameters with respect to using the complete information from directional wave spectra. As a large-scale model, we used WAVEWATCH III (WW3) running over the Mediterranean Sea providing the wave BCs for the small-scale SWAN model. One year of measured directional wave spectra from three deepwater buoys allowed the assessment of the WW3 model accuracy. The nested SWAN model simulations propagated the wave field over three nearshore areas. The accuracy of SWAN simulations was assessed through the comparison of computed results with wave measurements from nearshore buoys. Being the wave conditions dominated by unimodal seas, modest differences of error metrics are found between the datasets forced by different types of BCs. Conversely, focusing the attention on multimodal seas, an accurate definition of wave BCs achieved by the use of the directional wave spectra or, in alternative, by the reconstruction of wave spectra from partitioned wave parameters becomes crucial for nearshore wave predictions.

A boundary perturbation approach for regional wave ensemble forecast

In this study, we developed and validated two wave ensemble prediction systems (WEPS) to forecast wave conditions along the southeastern coast of Australia. Using the SWAN model (GEN3 ST6), we integrated complex bathymetric features with an unstructured grid and validated model outputs against buoy observations from Sydney, Port Kembla, and Batemans Bay. The two WEPS, SWAN-WW3 and SWAN-Pert, utilize different methodologies: SWAN-WW3 derives boundary conditions from NCEP’s Global Wave Ensemble System, while SWAN-Pert employs Latin Hypercube Sampling for boundary perturbations based on historical data. Our results demonstrate that both systems effectively predict significant wave height (Hs), with SWAN-Pert showing improved forecast accuracy in certain metrics compared to SWAN-WW3. Despite underdispersion in spread-skill diagrams, both WEPS exhibited good agreement with observed data. Additionally, rank histograms revealed that SWAN-Pert is more reliable at shorter lead times. This study highlights the potential of integrating statistical sampling methods and ensemble systems for enhancing regional wave forecasting accuracy.

Wave Downscaling Approach with TCN model, Case Study in Bengkulu, Indonesia

When conducting marine operations that rely on wave conditions, such as maritime trade, the fishing industry, and ocean energy, accurate wave downscaling is important, especially in coastal locations with complicated geometries. Traditional approaches for wave downscaling are usually obtained by performing nested simulations on a high-resolution local grid from global grid information. However, this approach requires high computation resources. In this paper, to downscale global wave height data into a high-resolution local wave height with less computation resources, we propose a machine learning-based approach to downscaling using the Temporal Convolutional Network (TCN) model. To train the model, we obtain the wave dataset using the SWAN model in a local domain. The global datasets are taken from the ECMWF Reanalysis (ERA-5) and used to train the model. We choose the coastal area of Bengkulu, Indonesia, as a case study. The results of TCN are also compared with other models such as LSTM and Transformers. It showed that TCN demonstrated superior performance with a CC of 0.984, RMSE of 0.077, and MAPE of 4.638, outperforming the other models in terms of accuracy and computational efficiency. It proves that our TCN model can be alternative model to downscale in Bengkulu’s coastal area.

Experimental study on the variation characteristics of focused wave packets with wave spectra scaled from different stages of wave growth

This study investigated the relationship between wave breaking and spectral evolution, focusing on the variations in wave spectra caused solely by breaking under different initial spectral shapes. Laboratory experiments utilizing a controlled wave focusing technique were conducted to generate breaking wave trains with input spectra derived from SWAN model simulations representing 12 distinct stages of wave growth under varying wind durations. Experiment data were combined with HOS simulations to isolate the breaking spectral changes from nonlinearity. Results reveal that the Benjamin-Feir instability dominates the nonlinear interactions for young waves, exhibiting an asymmetric shift towards higher wavenumbers. The characteristics of breaking-induced spectral changes depend on the initial spectral shape and consistent with the “two-phase behavior” of wind wave breaking. The study reveals new insights into the increase in the low-frequency energy, finding that this gain was primarily associated with the breaking of dominant waves near the spectral peak, whereas high-frequency breaking played a minimal role in transferring energy towards lower frequencies. A strong correlation between Qp and the proportion of spectral dissipation in different frequency ranges was revealed, suggesting that Qp can serve as a valuable parameter for characterizing the spectral evolution of breaking waves at various stages of development.

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.

Trends of extreme waves around Hainan Island during typhoon processes

Extreme waves mostly occur during typhoon processes in China Seas. This study statistically shows the trends of extreme wave height over the past several decades around Hainan Island, based on the reconstruction of historical wave processes during the typhoon events from 1949 to 2022. Totally 807 events in the tropical cyclone (TC) best track datasets are numerically simulated by the widely used wave model SWAN. The driving wind fields are reconstructed as a combination of an empirical cyclone wind model with the reanalysis wind field data, in order to better simulate waves near cyclone centers with higher spatial resolution. Based on the reconstructed wave data, the spatial distribution of wave characteristics around Hainan Island has been provided. The Mann-Kendall test shows a descending trend of extreme wave height in the southeast side of Hainan Island, where the maximum descending rate could reach up to 5 cm/yr. The influence of the long-term trend on the design extreme wave height is also discussed. Comparing to the variation of the design extreme wave height with sampling period, the long-term trend is also a significant impact factor.

Application of the high-resolution wave model for forecasting 1D sediment transport and beach inundation as a component of a short-term storm warning system

The main purpose of the research was to develop appropriate tools for forecasting the effects of storms. The use of the high-resolution SWAN wave model powered by AROME wind fields to couple it with the SWANOneSed sediment transport model and a model using the Ruggiero formula, predicting the extent of the flooded shore, allowed the development of reliable tools for forecasting the effects of storms. Validation using qualitative analysis showed that the results obtained from the coupled SWANOneSed model are characterized by high consistency in intensification and direction of changes. The model using the Ruggiero formula obtained a high value of the correlation coefficient, i.e. 0.970 and 0.992 for the Satbałtyk-Xbeach model data, based on the Hunt&Mase formula, and the high-resolution Pleiades Neo satellite data, respectively. By combining models from the SWAN family, i.e. the SWAN and SWANOneSed wave model and a model using the Ruggiero formula, it was possible to obtain reliable and compatible tools enabling an uninterrupted and synchronized operational process of generating forecasts of storm effects.

Thetis-SWAN: A Python-interfaced wave–current interactions coupled system

Wave–Current Interactions (WCI) emerge in nearshore coastal areas, prompting the development of coupled modelling systems to simulate these phenomena. We present a new multi-scale parallelised Python-interfaced WCI coupled system adopting a component-based approach enabling model-component integration without inhibiting their respective development. The underlying principles emphasise model equitability, flexibility and language interoperability. The hybrid model comprises the spectral wave model SWAN and the 2-D shallow-water equation model, Thetis. The coupling is performed through the Basic Model Interface. The coupled WCI model is the first to employ a Python interface, while maintaining the efficiency of different lower-level compiled programming languages, Fortran for SWAN and C for Thetis. We discuss the system implementation, architecture, and underlying physics considered. The coastal waters of Duck, NC, serve as a practical demonstration in simulating WCI. We then elaborate on the rationale for the coupled system design to inform the development of coupled modelling frameworks for environmental systems.

Risk level assessment of typhoon-induced wave fields around a large-scale suspended mussel farm

Suspended shellfish aquaculture, utilizing longlines and buoys fixed on the sea surface, is a crucial source of global seafood. However, typhoons, as the most common disasters in coastal regions, can generate massive waves and pose a threat to the safety of suspended aquaculture facilities. Therefore, it is essential to investigate the risk levels associated with suspended aquaculture farms during typhoon waves. The example of a large-scale suspended mussel farm in the East China sea was examined in this study. The wave conditions under extreme wind conditions (25m/s) and different typhoon paths were predicted using the SWAN model and modeled results compared well with the observed wave and wind data. The results indicated that when the center of the east-side typhoon was located at the same latitude as the farm, it will face the highest risk level. However, for the west-side and intermediate sweeping typhoons, the risk level reached highest in the farm, when the typhoon center is lower than the latitude of it. Under the combined effects of wind fetch and topography, the risk level in the southeast farming area of the island is the highest under different wind conditions. The farms to the northwest of the island face a higher risk level during the passage of east-side sweeping typhoons, while the southern farms of the island experience an elevated risk level during the west-side and intermediate sweeping typhoons. It is suggested that farming regions with lower risk levels are more conducive to the cultivation of species with longer culture cycles. Conversely, regions considered at higher risk are more suitable for species with shorter culture cycles. The study affords a reference for the optimization and insurance of suspended aquaculture farms.

Wave resource characterization and co-location with offshore wind in the Irish Sea

One barrier affecting progress in the wave energy sector is detailed knowledge of the spatiotemporal distribution of waves in shelf sea regions, including their inter- and intra-annual variability. Here, a recent decade (2012–2021) of waves is simulated at high-resolution in the Irish Sea — a region with much offshore energy infrastructure. The spectral wave model SWAN is forced with ERA5 wind fields. There is a strong seasonal cycle in wave height and power. In all months except for July, large waves (significant wave height greater than 5 m) can penetrate into the northern part of the Irish Sea, but the most energetic region is the Celtic Sea, where monthly mean wave power exceeds 30 kW/m in December. In this region, wave power strongly correlates with the North Atlantic Oscillation (NAO) from September to March. To investigate the potential for co-location, i.e. to reduce costs through shared infrastructure, wave and wind power were compared at a leased floating wind site in the Celtic Sea. Over the simulated decade, r2∼0.5, demonstrating modest potential for co-location of wind and wave energy technologies in this part of the Irish Sea — considerably less favourable than other sites in the North Atlantic that experience greater swell.

Wave environment analysis at Norwegian harbours for land-based aquaculture facilities using a combined phase-averaging and phase-resolving numerical modelling approach

Numerical wave modelling of Norwegian coastal areas is challenging due to the drastically varying bathymetry, irregular coastline, and large domains of interest. The widely used phase-averaging models are limited by such bathymetric and topographic conditions. Phase-resolving models provide higher accuracy regarding strong nonlinear wave transformations with the trade-off of a higher computational cost. This study provides a combined phase-averaging and phase-resolving modelling approach to analyse the wave conditions for on-shore aquaculture facilities at the site candidates Fiskenes and Breivik, Andøya, Norway. The phase-averaging spectral model SWAN is used for the offshore sea state analysis based on the offshore hind-cast data. The analysis is performed on cascade nested grids, with increasing accuracy closer to the proposed harbour locations. A novel interpolation algorithm is proposed to provide comprehensive sea state information with every offshore wave directions without requiring additional simulations. The critical wave conditions are identified from SWAN and used as input to the open-source phase-resolving fully nonlinear potential flow model REEF3D::FNPF. Four scenarios are investigated and densely spaced wave gauges in the entire computational domain provide the distribution of significant wave height. The combined and cascade simulation approach helps to achieve a balance between computational efficiency and accuracy for large-scale marine environment assessment. The results show detailed wave statistics in the entire area of relevance near both harbours and provide a quantitative reference for both the site choice and the harbour design.

Spatiotemporal variability of wave climate in the Gulf of Riga

Basic properties of wind wave climate in the Gulf of Riga, the Baltic Sea, are evaluated based on modelled wave fields, instrumentally measured and historical visually observed wave properties. Third-generation spectral wave model SWAN is applied to the entire Baltic Sea for 1990–2021 with a spatial resolution of 3 nautical miles (nmi, about 5.5 km) forced by the wind field of ERA5, to the Gulf of Riga and its entrance area with a resolution of 1 nmi (about 1.85 km), and to nearshore areas of this gulf with a resolution of 0.32 nmi (about 600 m). The calculations are performed for an idealised ice-free climate. Wave properties are represented by 36 directional and 32 frequency bins. The simulations are complemented by five sessions of instrumental measurements in the 2000s and two sets of historical visual wave observations from the island of Ruhnu and the Sõrve Peninsula for 1954–2011. Predominantly representing fetch-limited windseas, the wave climate in the gulf is milder and more intermittent than in the open Baltic Sea. The average significant wave height is mostly in the range of 0.6–0.8 m and peaks at 0.82 m inside the gulf. Typical wave periods are shorter than in the Baltic proper. The spatial pattern of wave heights, with higher wave intensity in the northern and eastern parts of the basin, follows anisotropy in wind conditions. Interannual variations are highly synchronised in different parts of the gulf. Their magnitude is less than 10% of the long-term average wave height. No long-term trend has been found in significant wave height and no distinct decadal variation exists inside the gulf.

Environmental variables and its association with faecal coliform at Madh Island beaches of megacity Mumbai, India

The abundance of fecal-indicating bacteria in water bodies are said to be influenced by environmental conditions. The effect of environmental parameters on the changing microbial population plays an important role in coastal water quality assessment. It's crucial to comprehend these patterns in order to support management initiatives for pollution control. There are studies on faecal bacteria contamination of surface water and how environmental conditions affect it, however majority of them are based on large river basins and temperate regions of developed countries. In this paper, we study the different physicochemical and physical factors of the five recreational beaches in Mumbai, India and examine how these environmental factors affect the levels of faecal indicating bacteria, namely, coliforms in the region. A relatively high number of coliform was identified from the water and sediment samples of Marve Beach (>1600 MP N/100 ml or g), followed by Erangal Beach and Danapani Beach (∼150–350 MP N/100 ml or g), exceeding the normal standard limits of <100 MP N/100 ml set by the Central Pollution Control Board (CPCB) of India. Of the physiochemical parameters examined, BOD showed to have a strong positive correlation with the coliform bacteria. The nutrients and heavy metal concentrations did not show any major effect on the microbial population. The MIKE CMAP predicted tide, MIKE3 Flow Model FM simulated coastal current, ERA5 hourly wind, as well as SWAN model simulated nearshore wave together considerably exhibit alliance of microbial behavior in the beaches with ambient physical processes. This study reveals that Mumbai's coastal water contain significant concentrations of faecal bacteria, which, if not monitored regularly could have future consequences on the recreational activities of the region, thereby on public health. This assessment might help in simulation of water quality prediction, and for optimizing the coastal zone management and development across recreational beaches.

The Wave Period Parameterization of Ocean Waves and Its Application to Ocean Wave Simulations

The wave period is a wave parameter that is significantly influenced by factors such as wind speed and bottom topography. Previous research on wave period parameterization has primarily focused on wind-dominated sea areas and may not be applicable to certain regions, such as the equatorial calm or coastal areas dominated by swell waves. To address this limitation, this paper utilizes the third-generation wave numerical model SWAN to perform wave numerical simulations for a portion of the Northwest Pacific Ocean. The simulation incorporates observational data from nearshore stations, buoys, and satellite altimeters for error analysis. To develop a new wave parameterization scheme (WS-23), we employ extensive NDBC buoy data and incorporate the exponential rate and wave age characteristics that were previously established by predecessors. Our scheme introduces a judgement mechanism to distinguish between wind waves, swell waves, and mixed waves. The resulting ocean wave factor enhances the mean wave period values calculated using the model and other parameterization schemes. The experimental results demonstrate that our new parameterization scheme effectively improves the abnormal peak of the fitting data. Comparing the output values of the mean wave period element output of the SWAN model with our new parameterization scheme, we observe a reduction in the mean values of Ea, Ec, and RMSE by 0.231, 1.94%, and 0.162, respectively, while increasing the average r by 0.05.