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Spectral bandwidth is a relevant parameter of water wave evolution and is commonly used to represent the number of wave components involved in wave—wave interactions. However, whether these two parameters are equivalent is an open question. Following the high-order spectral method and taking the weakly modulated Stokes wave train as the initial condition, the relationship between the spectral bandwidth and the number of wave components is investigated in this work. The results showed that the number of wave components can vary with the same spectral bandwidth and that distinct wave profiles emerge from different numbers of wave components. With a new definition of significant wave components, the characteristics of this parameter in the long-time wave evolution are discussed, along with its relationship with common parameters, including the wave surface maximum and the wave height. The results reveal that the wave surface evolution trend of different numbers of significant wave components ( N s ) is the same from a holistic perspective, while the difference between them also exists, mainly in locations where extreme waves occur. Furthermore, there is a negative correlation between r ( a j / a 0 ) and wave surface maximum ( η max / a 0 ) and wave height ( H max and H s ). The evolution trends of the relative errors (RE) of η max / a 0 , H max , and H s of different N s show the periodic recurrence of modulation and demodulation in the early stage when the Benjamin—Feir instability is dominated. The difference is that in the later stage, the RE of η max / a 0 and H max is chaotic and irregular, while those of H s gradually stabilize near an equilibrium value. Furthermore, we discuss the relationship between the mean relative error (MRE) and r . For η max / a 0 , MRE and r show a logarithmic relationship, while for H max and H s , a quadratic relationship exists between them. Therefore, the choice of N s is also important for extreme waves and is particularly meaningful for wave generation experiments in the wave flume.

A climatology of the wind waves in the Mediterranean Sea is presented. The climate patterns, their spatio-temporal variability and change are based on a 40-year (1980–2019) wave hindcast, obtained by combining the ERA5 reanalysis wind forcing with the state-of-the-art WAVEWATCH III spectral wave model and verified against satellite altimetry. Results are presented for the typical (50th percentile) and extreme (99th percentile) significant wave height and, for the first time at the regional Mediterranean Sea scale, for the typical and extreme expected maximum individual wave height of sea states. The climate variability of wind waves is evaluated at seasonal scale by proposing and adopting a definition of seasons for the Mediterranean Sea states that is based on the satellite altimetry wave observations of stormy (winter) and calm (summer) months. The results, initially presented for the four seasons and then for winter and summer only, show the regions of the basin where largest waves occur and those with the largest temporal variability. A possible relationship with the atmospheric parameter anomalies and with teleconnection patterns (through climate indices) that motivates such variability is investigated, with results suggesting that the Scandinavian index variability is the most correlated to the Mediterranean Sea wind-wave variability, especially for typical winter sea states. Finally, a trend analysis shows that the Mediterranean Sea typical and extreme significant and maximum individual wave heights are decreasing during summer and increasing during winter.

Background P‐wave dispersion (PWD) is believed to be caused by inhomogeneous atrial conduction. This statement, however, is based on limited little solid evidence. The aim of this study was to determine the relationship between atrial conduction and PWD by means of invasive electrophysiological studies. Methods Cross‐sectional study in 153 patients with accessory pathways and atrioventricular node reentry tachycardia (AVNRT) undergoing an electrophysiological study. Different atrial conduction times were measured and correlated with PWD. Results Only the interatrial (P‐DCS) and left intra‐atrial conduction times (ΔDCS‐PCS) showed a significant correlation with PWD, but this correlation was weak. Multivariate linear regression analysis determined that both P‐DCS (β = 0.242; P = .008) and ΔDCS‐PCS (β = 0.295; P < .001) are independent predictors of PWD. Performing the multivariate analysis for arrhythmic substrates, it is observed that only ΔDCS‐PCS continued to be an independent predictor of PWD. Analysis of the receiver operating characteristic curves showed that regardless of the types of arrhythmic substrates, PWD discriminates significantly, but moderately, to patients with P‐DCS and ΔDCS‐PCS ≥75 percentile. Conclusions Interatrial and intraleft atrial conduction times were directly and significantly correlated with PWD, but only weakly, and were independent predictors of PWD. In general, PWD correctly discriminates patients with high values in interatrial and intraleft atrial conduction times, but moderately. This is maintained in cases with accessory pathways; however, in patients with AVNRT it only does so for intraleft atrial conduction times. Interatrial and intraleft atrial conduction times weakly explains PWD. P‐wave dispersion (PWD) is considered by many to be an electrocardiographic parameter originating from regional differences in atrial conduction. The aim of this study was to determine the relationship between atrial conduction and PWD by means of invasive electrophysiological studies. Interatrial and intra left atrial conduction times were directly and significantly correlated with PWD, but weakly, and were independent predictors of PWD.

Typhoon-induced wind and wave can interrupt the operation and even threaten the safety of moving vehicles and bridges. However, the typhoon-induced maximum wind speed and maximum wave height are not coincident in time. Hence, it is a critical issue to include the time lag in the hazard model of wind and wave conditions for bridges. This paper adopts the concept of the pair-copula decomposed model to develop a trivariate joint probability model of typhoon-induced maximum wind speed, maximum wave height and their time lag. Pingtan Strait, where a sea-crossing bridge is being built, is taken as the example site. Considering the long-term measured wind and wave conditions under typhoons are not available, 58 tropical cyclones from 1990 to 2018 that influenced the example site are selected. The typhoon-induced maximum wind speed, wave height and their time lag at the example site are simulated using the validated SWAN + ADCIRC coupled numerical model. The trivariate joint probability modeling of wind, wave and time lag was carried out based on the simulated data. The trivariate environmental surfaces with the 50-year and 100-year return periods were finally obtained by the inverse first-order reliability method. The results show that more than 50% of typhoons have the maximum wind lagged behind the maximum wave at the example site. The higher typhoon-induced maximum wind speed and wave height tend to occur simultaneously. Two-parameter Weibull distribution is suitable to fit the distribution of the maximum wind speed and wave height, and the GEV distribution is ideal for the distribution of time lag. According to the trivariate environmental surface, neglecting the time lag might slightly overestimate the demand of the wind and wave loads. This study is of particular interest to the researchers and engineers in developing the metocean conditions.

Surface gravity waves play an important role in ocean engineering studies and their influence on the dynamics of the coastal zone is critical. Proper knowledge on wind-wave climatology is an area of immense interest to engineers and climate modellers. Climate change has influenced weather patterns over global oceans and at present is a matter of serious concern, as it can have long-term repercussions. There is a need to understand the recent trends in variability of wind-waves for planning operations. To improve climate projections the Intergovernmental Panel on Climate Change report highlights the need and importance for wind-wave climate study. With this motivation, we study the variability of recent trends in maximum wind speed (MWS) and maximum significant wave height (MSWH) exclusively based on altimeter data for the Indian Ocean basin. We use daily data of MWS and MSWH from eight satellite missions covering a period of 21 years (1992–2012). The findings indicate that regions in the Southern Ocean (between 45°S and 55°S) experienced the largest variability in wind-wave climate. Higher MSWH resulting from increased MWS has practical implications on swell generation field that eventually cross the hemisphere influencing wind-waves elsewhere. The study also reveals the impact of wind-wave activity for the Indian Ocean basin in the past decade.

This study discusses the impact of high-resolution winds on the coastal waves and analyses the effectiveness of the high-resolution winds in recreating the fine-scale features along the coastal regions during the pre-monsoon season (March–May). The influence of the diurnal variation of winds on waves is studied for the Tamil Nadu coastal region using wind fields from weather research and forecast (WRF) (3 km) and European Centre for Medium-Range Weather Forecasts (ECMWF) (27.5 km). The improvement in the coastal forecast is then quantified with wave rider buoy observations. The high-resolution wind fields simulated fine-scale features like land–sea breeze events and showed good agreement with observation results. The error in the wave height and period is reduced by 8% and 46%, respectively, with the use of high-resolution forcing winds WRF over ECMWF, although the overestimation of wave energy on high frequencies due to overestimated WRF winds remains as a challenge in forecasting. The analysis also shows the importance of accurate wave forecast during a short-duration sudden wind (~12 m/s) occurrence in southern Tamil Nadu near Rameswaram during the pre-monsoon period. Low pressure forms over Tamil Nadu due to the land surface heating, resulting in a sudden increase of winds. High winds and steep waves which cause damage to the property of the coastal community near Rameswaram also were well simulated in the high-resolution forecast system with WRF winds.

Two hundred sixty-eight years (about three centuries) since the 1755 Lisbon tsunami, which provoked large-scale damage in Portugal, Spain and Morocco. Scientists in several countries have mobilized to develop investigative methods to study this extraordinary tsunami, with the aim of reducing the impact of waves in future events and protecting and warning populations at risk. However, the most important question remains whether the entire damaged community has sufficient information about the impact to prepare for tsunami risks. The aim of this paper is to determine the maximum wave heights in selected cities located within high-risk tsunami zones in Morocco, as well as the tsunami arrival time for each site. We used the Non-linear Shallow Water model With Nested Grids (NSWING) code to model tsunami propagation. Six cities were determined as observation points as being the subject of tsunami studies in previous work, namely Tangier, Asilah, Rabat, Casablanca, El-Jadida and Safi. The maximum wave height calculated within the Atlantic coast of Morocco exceeds 5 m in some locations and the first waves reach the Moroccan coast in 60 minutes. The authorities might utilize these results to develop evacuation plans.

Extreme sea conditions caused by tropical cyclones pose significant risks to coastal safety, infrastructure, and ecosystems. Although existing models have advanced in predicting Significant Wave Height (SWH), their performance in predicting Maximum Wave Height (MWH) remains limited, particularly in capturing rapid wave fluctuations and localized meteorological dynamics. This study proposes a novel MWH prediction framework that integrates high-resolution buoy observations with deep learning. A moored buoy deployed in the Qiongzhou Strait provides precise nearshore observations, compensating for limitations in reanalysis datasets. Light Gradient Boosting Machine (LightGBM) is employed for key feature selection, and a hybrid Bidirectional Temporal Convolutional Network-Bidirectional Gated Recurrent Unit (BiTCN-BiGRU) model is constructed to capture both short- and long-term temporal dependencies. The results show that BiTCN-BiGRU outperforms BiGRU, reducing MAE by 6.11%, 5.41%, and 14.09% for 1-h, 3-h, and 6-h forecasts. This study also introduces the Time Distortion Index (TDI) into MWH prediction as a novel metric for evaluating temporal alignment. This study offers valuable insights for disaster warning, coastal protection, and risk mitigation under extreme marine conditions.

The ship-generated wave causes erosive damage to the slopes of inland waterways and confined waters such as the coastal zone. This critical issue is essentially associated with the navigational safety and sustainable development of coastlines, so it is vital to predicting the maximum wave height of the ship-generated wave ( H m ) in the coastline of confined waters. The prediction equations of prior research works are mostly based on measured data and multiple regression analysis; however, this paper aims to propose a novel methodology to predict the maximum wave height of ship-generated waves in confined waters. The maximum wave height caused by a self-propelled ship under various conditions with confined water is measured by employing a water flume. Furthermore, the relationships of functions in the prediction model equation are appropriately derived by dimensional analysis, and the prediction equation model is then solved via the particle swarm optimization algorithm (PSOA). The experimental results reveal that the maximum wave height of the ship-generated wave is seriously affected by the water depth of the channel ( h c ), the navigation speed of the ship ( V c ), and the distance from the forecast point to the navigation line of the ship ( S c ). In addition, the maximum wave height grows and then lessens, which touches its peak point in the region close to Fr h = 1. The dimensionless analysis indicates that the large wave height of the ship-generated wave can be expressed as a function of the channel depth ( h c ), ship speed ( V c ), and the distance from the measurement point to the ship’s navigation line ( S c ) as; H m S c = 1 F r h 2 f ( g S c V c   2 ) . Subsequently, the specific prediction equation is determined by a regression model according to the measured data under various working conditions. By adopting the model expression equation, namely, H m F r h 2 S c = k 1 ( g S c V c   2 ) k 2 , the coefficients k 1 and k 2 are evaluated via the PSOA, and the relationship between the water depth of the channel and the coefficients is suitably outlined to obtain the maximum wave height prediction model equations for ship-generated waves.