Characterizing and hindcasting ripple bedform dynamics: Field test of non-equilibrium models utilizing a fingerprint algorithm

DuVal, Carter; Trembanis, Arthur C.; Skarke, Adam

doi:10.1016/j.csr.2015.12.015

Cited by 8 publications

(6 citation statements)

References 39 publications

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“…For the much larger relict ripples (λ > 1 m) observed over October 7-20, the time frame would be over several months before ripples would reach 50% of their preliminary height if conditions remained below θ cr over that time. This mirrors observations made at a deeper site (28 m) off the state of Delaware, where large relict ripples were observed up to 4 months after formation due to subsequent minimal forcing at depth (DuVal et al, 2016). In both cases observed in this study, decay is attributable to macrofaunal bioturbation the majority of time, although macrofauna bioturbation led to the largest percentage of decay in the September 6-10 ripples (∼80% of observed decay) versus physical decay for the October 7-20 ripples (∼34% of observed decay).…”

Section: Ripple Morphology and Decaysupporting

confidence: 80%

“…Near-bed orbital velocity may be reasonably estimated from linear wave theory (see Soulsby, 2006;Voulgaris & Morin, 2008), and a strong correlation (r 2 = 0.85) was found between direct near-bed observations and orbital velocity estimated by linear wave theory at the nearby Redbird Reef (see DuVal et al, 2016). Raw rotary sonar imagery was slant range corrected, oriented (in degrees from North), and a Time-Varying Gain (TVG) was applied.…”

Section: Discussionmentioning

confidence: 99%

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A Regime‐State Framework for Morphodynamic Modeling of Seabed Roughness

2021

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In the dynamic near-shore environment, seabed roughness is constantly modified by the interplay of physical and biological processes. Seabed roughness directly influences near-bed hydrodynamics, sediment transport (Van Rijn, 2007), and underwater acoustics (Briggs et al., 2001). Accurate predictions of hydrodynamics, sediment transport processes, and morphodynamics are dependent upon the ability to quantify and parameterize seabed roughness (Brakenhoff et al., 2020). Measuring roughness in the field remains difficult (Brakenhoff et al., 2020), and is compounded by the continuously evolving contributions to seabed roughness, including bedforms and biology.

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Section: Ripple Morphology and Decaysupporting

confidence: 80%

Section: Discussionmentioning

confidence: 99%

A Regime‐State Framework for Morphodynamic Modeling of Seabed Roughness

2021

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“…The WW3 and TRDI burst-average significant wave heights were time-synchronized and correlated using linear regression. This regression was used to correct the WW3 wave heights before further analysis [36,37].…”

Section: Publically Available Datamentioning

confidence: 99%

“…Calculated run-ups of 0.5 m or more for each March 2018 ETCs further eroded beach features like the nourishment dune toe, as observed from the orthomosaics and volumetric change maps [9]. Given these limitations, the 95% exceedance values for H s (Table 2) have greater applications for the region, but require a permanent monitoring buoy within the DBE-where one currently does not exist-or more ground-truthing of WW3 forecasts, as they consistently under-predict extreme events [37]. The relationship between volumetric change and H s is indirect and nonlinear, as other parameters such as the tidal phase, storm duration, river discharge, and wind velocities are significant contributors within estuarine systems [65,66].…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Quantifying Estuarine Hydrometeorological Coastal Hazards Using a Combined Field Observation and Modeling Approach

Dohner

DuVal

2022

JMSE

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Coastal development and its associated site management have rapidly expanded to estuarine environments while continuing to increase worldwide. With the growth of coastal management projects, field observations are required to understand how anthropogenic construction, coastal defense, environmental restoration, and conservation areas will react to the typical, extreme, and long-term conditions at the proposed sites. To address these unknowns, we present a multi-faceted coastal risk assessment of a unique, recently nourished estuarine beach near the mouth of the Delaware Bay Estuary by merging rapid-response remote sensing platforms, hydrodynamic models, and publically available monitoring datasets. Specifically, hydrometeorological events from 2015 to 2019 were the focus of peak-over-threshold statistics, event type definition, and clustered event interval determination. The 95th percentile thresholds were determined to be the following: 0.84 m for the significant wave height, 13.5 m/s for the 10-m elevation wind speed, and 0.4 m for the total water level residuals. Tropical and extra-tropical cyclones, light gales, or cold and stationary fronts proved to be the meteorological causes of the sediment mobility, inducing the hydrodynamics at the site. Using these event types and exceedance instances, clustered meteorological events were defined as having an interval greater than twelve hours but less than five days to be considered clustered. Clustered events were observed to cause greater volumetric change than individual events, and are currently underrepresented in coastal risk planning and response in the region. Coastal monitoring field measurements should consider clustered events when conducting post-hazardous or erosional event response surveys. This work highlights the importance of clustered hydrometeorological events causing estuarine coastal risk, and how to quantify these effects through combined field observations and modeling approaches.

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“…1 shows examples of SAS images containing complex seabed textures. Accurate segmentation of SAS imagery provides several benefits including large-scale environmental understanding of the seabed [8]- [11] and insitu image interpretation to support AUV operations [12].…”

Section: Introductionmentioning

confidence: 99%

Iterative, Deep Synthetic Aperture Sonar Image Segmentation

Sun,

Gerg,

Monga

2022

Preprint

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Synthetic aperture sonar (SAS) systems produce high-resolution images of the seabed environment. Moreover, deep learning has demonstrated superior ability in finding robust features for automating imagery analysis. However, the success of deep learning is conditioned on having lots of labeled training data, but obtaining generous pixel-level annotations of SAS imagery is often practically infeasible. This challenge has thus far limited the adoption of deep learning methods for SAS segmentation. Algorithms exist to segment SAS imagery in an unsupervised manner, but they lack the benefit of state-of-theart learning methods and the results present significant room for improvement. In view of the above, we propose a new iterative algorithm for unsupervised SAS image segmentation combining superpixel formation, deep learning, and traditional clustering methods. We call our method Iterative Deep Unsupervised Segmentation (IDUS). IDUS is an unsupervised learning framework that can be divided into four main steps: 1) A deep network estimates class assignments. 2) Low-level image features from the deep network are clustered into superpixels. 3) Superpixels are clustered into class assignments (which we call pseudo-labels) using k-means. 4) Resulting pseudo-labels are used for loss backpropagation of the deep network prediction. These four steps are performed iteratively until convergence. A comparison of IDUS to current state-of-the-art methods on a realistic benchmark dataset for SAS image segmentation demonstrates the benefits of our proposal even as the IDUS incurs a much lower computational burden during inference (actual labeling of a test image). Because our design combines merits of classical superpixel methods with deep learning, practically we demonstrate a very significant benefit in terms of reduced selection bias, i.e. IDUS shows markedly improved robustness against the choice of training images. Finally, we also develop a semi-supervised (SS) extension of IDUS called IDSS and demonstrate experimentally that it can further enhance performance while outperforming supervised alternatives that exploit the same labeled training imagery.

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Characterizing and hindcasting ripple bedform dynamics: Field test of non-equilibrium models utilizing a fingerprint algorithm

Cited by 8 publications

References 39 publications

A Regime‐State Framework for Morphodynamic Modeling of Seabed Roughness

A Regime‐State Framework for Morphodynamic Modeling of Seabed Roughness

Quantifying Estuarine Hydrometeorological Coastal Hazards Using a Combined Field Observation and Modeling Approach

Iterative, Deep Synthetic Aperture Sonar Image Segmentation

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