Modeling of coastal infragravity waves using the spectral model WAVEWATCH Ⅲ

Zheng, Zhenjun; Ma, Xiaozhou; Ma, Yuxiang; Huang, Xuegang; Dong, Guohai

doi:10.1016/j.coastaleng.2021.104016

Cited by 16 publications

(2 citation statements)

References 40 publications

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“…These infragravity waves, often generated by the swell in the shallow water (Longuet‐Higgins & Stewart, 1962), can pose a threat to coastal areas (Hattori et al., 2020; Matsuba et al., 2020). They frequently contribute to coastal flooding, erosion, sediment transport, and the formation of dangerous rip currents (Beach & Sternberg, 1988; Bertin et al., 2018; Osborne & Greenwood, 1992; Sheremet et al., 2014; Zheng et al., 2021). By understanding and analyzing the characteristics of infragravity waves, such as their amplitude, frequency, and propagation patterns, coastal authorities and communities can enhance their ability to predict and mitigate the impacts of these waves.…”

Section: Discussionmentioning

confidence: 99%

Detection of Earthquake Infragravity and Tsunami Waves With Underwater Distributed Acoustic Sensing

Xiao,

Spica,

et al. 2024

Geophysical Research Letters

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Underwater Distributed Acoustic Sensing (DAS) utilizes optical fiber as a continuous sensor array. It enables high‐resolution data collection over long distances and holds promise to enhance tsunami early warning capabilities. This research focuses on detecting infragravity and tsunami waves associated with earthquakes and understanding their origin and dispersion characteristics through frequency‐wavenumber domain transformations and beamforming techniques. We propose a velocity correction method based on adjusting the apparent channel spacing according to water depth to overcome the challenge of detecting long‐wavelength and long‐period tsunami signals. Experimental results demonstrate the successful retrieval of infragravity and tsunami waves using a subsea optical fiber in offshore Oregon. These findings underscore the potential of DAS technology to complement existing infragravity waves detection systems, enhance preparedness, and improve response efforts in coastal communities. Further research and development in this field are crucial to fully utilize the capabilities of DAS for enhanced tsunami monitoring and warning systems.

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Section: Discussionmentioning

confidence: 99%

Detection of Earthquake Infragravity and Tsunami Waves With Underwater Distributed Acoustic Sensing

Xiao,

Spica,

et al. 2024

Geophysical Research Letters

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“…Therefore, an accurate prediction of waves is of great importance to a better understanding of the physical process in marine environments and engineering design. Numerical models, for example, SWAN (Hsu & Holland, 2007;Muraleedharan et al, 2022;Rusu & Soares, 2013) or WAVE WATCH III (Campos et al, 2022;Zheng et al, 2021) are popular models of predicting waves nowadays, in which waves are simulated with the combination of physics-based models (Hokimoto et al, 2003;Reikard & Rogers, 2011), for example, energy balance equation (Nitsure et al, 2012). Although numerical models can capture the regional pattern by nesting global ocean wave models to coastal and near-shore high-resolution wave models (Sandhya et al, 2014), it sometimes fails to forecast local wave parameters.…”

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confidence: 99%

Improved Prediction of Local Significant Wave Height by Considering the Memory of Past Winds

Zhang

Yang

Zhang

et al. 2023

Water Resources Research

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Wave and water depth were measured with an instrumented tripod in the Yellow River Delta from 9 December 2014 to 29 April 2015. Concurrent wind data were also collected from a nearby wind station. A high‐precision model for predicting local significant wave height (Hs) with wind speed (vw) is constructed using an improved data‐driven approach. The proposed model realized high accuracy as it solves the problem that the Hs falls too fast during the wind‐decreasing periods. It was tackled by considering the remaining influence of historical vw on the present Hs via incorporating a memory curve of the past wind effect. This innovative approach significantly improves the prediction (R2 from 0.60 to 0.83). The winds in the past 24 hr still left an influence on the waves at the observation site although the influence decreases with time. Physically, it is an implicit but simpler consideration of wind fetch/duration. Further data modeling experiments indicated that the decisive factor for the Hs at the site is the wind speed. Wind directions slightly improve the prediction, indicating that waves are slightly affected by the underwater seabed slope along different wind directions, and northwest winds cause the strongest waves at the site. Adding atmospheric pressure or water depth even reduces the accuracy, which indicated that storm surges and wave deformations under different tide levels have a weak impact on Hs. The proposed local wave model can be easily constructed with available wind and wave data, making it expandable to other regions dominated by wind waves.

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