Multi-timescale dynamics of extreme river flood and storm surge interactions in relation with large-scale atmospheric circulation: Case of the Seine estuary

Turki, Imen; Deloffre, Julien; Lecoq, N.; Gilbert, R.; Mendoza, Edgar; Salameh, Edward; Barceló, Ángel David Gutiérrez Gutiérrez; Fournier, Matthieu; Massei, Nicolas

doi:10.1016/j.ecss.2023.108349

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…However, it is important to note that the DWT is sensitive to the length of the time series [41]. To overcome this, the maximal overlap DWT (MODWT) was implemented in this study [42,43]. It is particularly suitable for handling non-stationary and irregularly sampled data using overlapping segments.…”

Section: Frequency Components and Wavelet Analysismentioning

confidence: 99%

“…This phenomenon has been studied in various research articles (see [7,45,46]). Furthermore, the percentage of energy that each spectral component contributes to the total variability is estimated, indicating the importance of each detail [43]. Figure 10 illustrates the spatial evolution of the decomposed components (d1-d9) energy along the wave flume using a JONSWAP (γ = 3.3) signal of nine wave trains and a nonlinearity of ε 0 = 0.53.…”

Section: Frequency Components and Wavelet Analysismentioning

confidence: 99%

“…This phenomenon has been studied in various research articles (see [7,45,46]). Furthermore, the percentage of energy that each spectral component contributes to the total variability is estimated, indicating the importance of each detail [43]. Components d5 and d4, which correspond to the peak frequency and its first harmonic, experience a decrease in energy while the other components exhibit an increase (Figure 10).…”

Section: Frequency Components and Wavelet Analysismentioning

confidence: 99%

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A Multi-Approach Analysis for Monitoring Wave Energy Driven by Coastal Extremes

Matar,

Abcha,

Abroug

et al. 2024

Water

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This research investigates the behavior and frequency evolution of extreme waves in coastal areas through a combination of physical modeling, spectral analysis, and artificial intelligence (AI) techniques. Laboratory experiments were conducted in a wave flume, deploying various wave spectra, including JONSWAP (γ = 7), JONSWAP (γ = 3.3), and Pierson–Moskowitz, using the dispersive focusing technique, covering a broad range of wave amplitudes. Wave characteristics were monitored using fifty-one gauges at distances between 4 m and 14 m from the wave generator, employing power spectral density (PSD) analysis to investigate wave energy subtleties. A spectral approach of discrete wavelets identified frequency components. The energy of the dominant frequency components, d5 and d4, representing the peak frequency (fp = 0.75 Hz) and its first harmonic (2fp = 1.5 Hz), respectively, exhibited a significant decrease in energy, while others increased, revealing potential correlations with zones of higher energy dissipation. This study underscores the repeatable and precise nature of results, demonstrating the Multilayer Perceptron (MLP) machine learning algorithm’s accuracy in predicting the energy of frequency components. The finding emphasizes the importance of a multi-approach analysis for effectively monitoring energy in extreme coastal waves.

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Section: Frequency Components and Wavelet Analysismentioning

confidence: 99%

Section: Frequency Components and Wavelet Analysismentioning

confidence: 99%

“…This phenomenon has been studied in various research articles (see [7,45,46]). Furthermore, the percentage of energy that each spectral component contributes to the total variability is estimated, indicating the importance of each detail [43]. Components d5 and d4, which correspond to the peak frequency and its first harmonic, experience a decrease in energy while the other components exhibit an increase (Figure 10).…”

Section: Frequency Components and Wavelet Analysismentioning

confidence: 99%

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A Multi-Approach Analysis for Monitoring Wave Energy Driven by Coastal Extremes

Matar,

Abcha,

Abroug

et al. 2024

Water

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Anthropogenic Impact on the Changes in the Neva Bay Bottom Morphology in the 19th – 21st Centuries

Kozinets,

Badenko,

Zotov

2024

Power Technol Eng

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Projection of Compound Wind and Precipitation Extreme Events in the Iberian Peninsula Based on CMIP6

Alvarez,

Pereira,

Picado

et al. 2024

Earth Syst Environ

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This study aims to investigate the potential changes in the co-occurrence of strong precipitation and wind events over the Iberian Peninsula using simulations from the Coupled Model Intercomparison Project (CMIP) Phase 6 under two scenarios (SSP2 − 4.5 and SSP5 − 8.5). Projected changes indicate a significant regional variability during all seasons. In winter, the western regions are projected to experience an increase in compound events as the century progresses under both scenarios, with a significantly larger area being affected by the end of the century. In spring, summer, and autumn, a general decline in the occurrence of these events is anticipated throughout the century, accompanied by a reduction in the area affected by them. However, in the northwesternmost area (Galicia), an increase in the occurrence of compound events is expected during the spring towards the end of the century, particularly under the SSP5-8.5 scenario.

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Multi-timescale dynamics of extreme river flood and storm surge interactions in relation with large-scale atmospheric circulation: Case of the Seine estuary

Cited by 4 publications

References 44 publications

A Multi-Approach Analysis for Monitoring Wave Energy Driven by Coastal Extremes

A Multi-Approach Analysis for Monitoring Wave Energy Driven by Coastal Extremes

Anthropogenic Impact on the Changes in the Neva Bay Bottom Morphology in the 19th – 21st Centuries

Projection of Compound Wind and Precipitation Extreme Events in the Iberian Peninsula Based on CMIP6

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