2011
DOI: 10.1061/(asce)ww.1943-5460.0000068
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Depth Inversion in the Surf Zone with Inclusion of Wave Nonlinearity Using Video-Derived Celerity

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Cited by 27 publications
(23 citation statements)
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“…The traditional approach for estimating the wave celerity c relies in the following estimate: cΔx/Δt where Δx is the distance travelled by the wave in the time Δt (e.g., see Suhayda & Pettigrew, ). The Radon Transform (Radon, ) has also been used to estimate individual wave celerities in video timestacks by Yoo et al () and then Almar et al (). As these methods can introduce considerable noise, which has a dramatic effect in a modeling exercise, we follow the approach of Tissier et al () which makes use of the high‐resolution character of the data set.…”
Section: Methodsmentioning
confidence: 99%
“…The traditional approach for estimating the wave celerity c relies in the following estimate: cΔx/Δt where Δx is the distance travelled by the wave in the time Δt (e.g., see Suhayda & Pettigrew, ). The Radon Transform (Radon, ) has also been used to estimate individual wave celerities in video timestacks by Yoo et al () and then Almar et al (). As these methods can introduce considerable noise, which has a dramatic effect in a modeling exercise, we follow the approach of Tissier et al () which makes use of the high‐resolution character of the data set.…”
Section: Methodsmentioning
confidence: 99%
“…Individual waves have been tracked previously in video timestacks (e.g., Yoo et al, 2011), however, the only previous method to track changes in wave height and shape parameters, that is known to the authors, is a method that measured 10 individual waves using a video camera and vertical poles deployed in the surf zone for scale (Suhayda and Pettigrew, 1977). This paper presents a novel method for tracking individual waves across multiple pressure transducers on a cross-shore transect in the surf zone.…”
Section: Introductionmentioning
confidence: 98%
“…In the last decade, remote‐sensing‐based methods have become increasingly popular to monitor coastal evolution [e.g., Holman and Stanley , ]. These methods often rely on the estimation of an average wave celerity from spatially coherent, instantaneous (∼1 Hz) time series of pixel intensity, which is used as a proxy to retrieve water depth [e.g., Stockdon and Holman , ; Yoo et al ., ; Almar et al ., ; Holman et al ., ]. This so‐called depth‐inversion is an interesting alternative to in situ bathymetric surveys, as it, when repeated with time, provides an estimate of bathymetric change over large areas at relatively low cost; however, its accuracy depends crucially on the functional relationship between time‐averaged wave celerity and water depth, that is to say on the choice of the parametrization for the celerity [see Catalán and Haller , , for a review].…”
Section: Introductionmentioning
confidence: 99%
“…Most efforts to improve the prediction of wave celerity have focused on this nonlinear effect. Taking into account the wave height through the use of nonlinear celerity models indeed leads to improved predictions and reduces depth estimation errors in the last stages of shoaling and in the surf zone [ Grilli , ; Catalán and Haller , ; Yoo et al ., ]; however, relatively large discrepancies between time‐averaged measured and nonlinearly predicted celerities still remain in the inner surf zone [ Tissier et al ., ].…”
Section: Introductionmentioning
confidence: 99%