Nearshore bathymetry from video and the application to rip current predictions for the Dutch Coast

Sembiring, Leo Eliasta; Dongeren, Ap van; Winter, Gundula; Ormondt, Maarten van; Brière, Christophe; Roelvink, Dano

doi:10.2112/si70-060.1

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…This matches conditions at the Dutch North Sea coast, where subtidal sandbar variability typically occurs at scales larger than 50 m (e.g. De Winter et al, 2014;Sembiring et al, 2014). All surveys used in this study are presented in Fig.…”

Section: Field Site and Instrumental Setupsupporting

confidence: 72%

Sensitivity of rip current forecasts to errors in remotely-sensed bathymetry

Radermacher

Schipper

Reniers

2018

Coastal Engineering

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Sensitivity of rip current forecasts to errors in remotely-sensed bathymetry Radermacher, M.; de Schipper, M. A.; Reniers, A. J.H.M. A B S T R A C TOperational nearshore current forecasts based on numerical model simulations are gaining popularity as a measure to increase the safety of swimmers. Applying remotely-sensed bathymetry in these model simulations is often proposed in order to cope with rapidly changing nearshore bathymetry. Errors in the remotely-sensed bathymetry may negatively affect performance of the hydrodynamic model. Hence, this study aims to determine the sensitivity of modelled nearshore currents (with a strong focus on rip currents) to errors in remotelysensed bathymetries.The errors in the remotely-sensed bathymetries (depth inversion algorithm applied to video stream) were quantified with a length scale-aware validation technique, providing useful insights in the contribution of pattern and amplitude errors to the total error throughout the analysis domain and over a range of bathymetric length scales. Subsequently, simulations with a nearshore hydrodynamic model were performed, using both in-situ and remotely-sensed bathymetries as an input. A comparison of predicted rip currents on either bathymetry yielded performance statistics for operational current forecasts on remotely-sensed bathymetries, taking the model with in-situ bathymetry as a reference. Linking these performance statistics back to the quantified errors in the remotely-sensed bathymetry finally revealed the relation between errors in flow and bathymetry.Of all rip currents generated on an in-situ bathymetry, 55% were reproduced on the remotely-sensed bathymetry, showing that models predicting nearshore currents on remotely-sensed bathymetry have predictive value. Positive rip current predictions were promoted significantly by accurate reproduction of the pattern and amplitude of nearshore bars at length scales between 200 and 400 m. In contrast to the length-scale aware validation technique applied here, commonly used domain-wide bulk error metrics lack important information about spatial variations in the quality of remotely-sensed bathymetry.

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Section: Field Site and Instrumental Setupsupporting

confidence: 72%

Sensitivity of rip current forecasts to errors in remotely-sensed bathymetry

Radermacher

Schipper

Reniers

2018

Coastal Engineering

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“…Under energetic wave conditions and large tidal ranges, depths are generally estimated with a spatially varying accuracy of tens of centimetres; a mean error of 3 cm and 60 cm standard deviation [35]. Similar accuracy has been found in Holman et al [34], Sembiring et al [38] and Brodie et al [37]. These numbers are of a similar order in comparison to echo-sounders used in challenging conditions inherent to the nearshore area.…”

Section: Introductionsupporting

confidence: 67%

Storm Event to Seasonal Evolution of Nearshore Bathymetry Derived from Shore-Based Video Imagery

et al. 2019

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Coastal evolution occurs on a wide range of time-scales, from storms, seasonal and inter-annual time-scales to longer-term adaptation to changing environmental conditions. Measuring campaigns typically either measure morphological evolution on a short-time scale (days) with high frequency (hourly) or long-time scales (years) but intermittently (monthly). This leaves an important observational gap that limits morphological variability assessments. Traditional echo sounding measurements on this long time-scale and high-frequency sampling require a significant financial injection. Shore-based video systems with high spatiotemporal resolution can bridge this gap. For the first time, hourly Kalman filtered video-derived bathymetries covering 1.5 years of morphological evolution with an hourly resolution obtained at Porhtowan, UK are presented. Here, the long-term hourly dataset is used and aims to show its added value for, and provide an in-depth, morphological analyses with unprecedented temporal resolution. The time-frame includes calm and extreme (storm) wave conditions in a macro-tidal environment. The video-derived bathymetries allow hourly beach state classification while before this was not possible due to the dependence on foam patterns of wave breaking (e.g., saturation during storms). The study period covers extreme storm erosion during the most energetic winter season in 60 years (2013–2014). Recovery of the beach takes place on several time-scales: (1) an immediate initial recovery after the storm season (first 2 months), (2) limited recovery during low energetic summer conditions and (3) accelerated recovery as the wave conditions picked up in the subsequent fall—under wave conditions that are typically erosive. The video-derived bathymetries are shown to be effective in determining bar-positions, outer-bar three-dimensionality and volume analyses with an unprecedented hourly temporal resolution.

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“…In terms of drivers or boundary conditions, hydrodynamics such as wave conditions or tides are often measured frequently or can be approximated relatively accurately through wave or tidal models. Bathymetry, on the other hand, is often unknown or outdated, such that numerical models, that are all sensitive to bathymetry accuracy, will perform poorly and cannot be trusted [4,5]. This is a particular problem, as the focus shifts to coastal response of the world's coastlines to climate change [6,7].…”

Section: Introductionmentioning

confidence: 99%

Updates to and Performance of the cBathy Algorithm for Estimating Nearshore Bathymetry from Remote Sensing Imagery

Holman

Bergsma

2021

Remote Sensing

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This manuscript describes and tests a set of improvements to the cBathy algorithm, published in 2013 by Holman et al. [hereafter HPH13], for the estimation of bathymetry based on optical observations of propagating nearshore waves. Three versions are considered, the original HPH13 algorithm (now labeled V1.0), an intermediate version that has seen moderate use but limited testing (V1.2), and a substantially updated version (V2.0). Important improvements from V1.0 include a new deep-water weighting scheme, removal of a spurious variable in the nonlinear fitting, an adaptive scheme for determining the optimum tile size based on the approximate wavelength, and a much-improved search seed algorithm. While V1.2 was tested and results listed, the primary interest is in comparing V1.0, the original code, with the new version V2.0. The three versions were tested against an updated dataset of 39 ground-truth surveys collected from 2015 to 2019 at the Field Research Facility in Duck, NC. In all, 624 cBathy collections were processed spanning a four-day period up to and including each survey date. Both the unfiltered phase 2 and the Kalman-filtered phase 3 bathymetry estimates were tested. For the Kalman-filtered estimates, only the estimate from mid-afternoon on the survey date was used for statistical measures. Of those 39 Kalman products, the bias, rms error, and 95% exceedance for V1.0 were 0.15, 0.47, and 0.96 m, respectively, while for V2.0, they were 0.08, 0.38, and 0.78 m. The mean observed coverage, the percentage of successful estimate locations in the map, were 99.1% for V1.0 and 99.9% for V2.0. Phase 2 (unfiltered) bathymetry estimates were also compared to ground truth for the 624 available data runs. The mean bias, rms error, and 95% exceedance statistics for V1.0 were 0.19, 0.64, and 1.27 m, respectively, and for V2.0 were 0.16, 0.56, and 1.19 m, an improvement in all cases. The coverage also increased from 78.8% for V1.0 to 84.7% for V2.0, about a 27% reduction in the number of failed estimates. The largest errors were associated with both large waves and poor imaging conditions such as fog, rain, or darkness that greatly reduced the percentage of successful coverage. As a practical mitigation of large errors, data runs for which the significant wave height was greater than 1.2 m or the coverage was less than 50% were omitted from the analysis, reducing the number of runs from 624 to 563. For this reduced dataset, the bias, rms error, and 95% exceedance errors for V1.0 were 0.15, 0.58, and 1.16 m and for V2.0 were 0.09, 0.41, and 0.85 m, respectively. Successful coverage for V1.0 was 82.8%, while for V2.0, it was 90.0%, a roughly 42% reduction in the number of failed estimates. Performance for V2.0 individual (non-filtered) estimates is slightly better than the Kalman results in the original HPH13 paper, and it is recommended that version 2.0 becomes the new standard algorithm.

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Nearshore bathymetry from video and the application to rip current predictions for the Dutch Coast

Cited by 11 publications

References 14 publications

Sensitivity of rip current forecasts to errors in remotely-sensed bathymetry

Sensitivity of rip current forecasts to errors in remotely-sensed bathymetry

Storm Event to Seasonal Evolution of Nearshore Bathymetry Derived from Shore-Based Video Imagery

Updates to and Performance of the cBathy Algorithm for Estimating Nearshore Bathymetry from Remote Sensing Imagery

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