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Sand waves constitute ubiquitous geomorphology distribution in the ocean. In this paper, we quantitatively investigate the sand wave variation of topology, morphology, and evolution from the high-resolution mapping of a side scan sonar (SSS) in an Autonomous Underwater Vehicle (AUV), in favor of online sequential Extreme Learning Machine (OS-ELM). We utilize echo intensity directly derived from SSS to help accelerate detection and localization, denote a collection of Gaussian-type morphological templates, with one integrated matching criterion for similarity assessment, discuss the envelope demodulation, zero-crossing rate (ZCR), cross-correlation statistically, and estimate the specific morphological parameters. It is demonstrated that the sand wave detection rate could reach up to 95.61% averagely, comparable to deep learning such as MobileNet, but at a much higher speed, with the average test time of 0.0018 s, which is particularly superior for sand waves at smaller scales. The calculation of morphological parameters primarily infer a wave length range and composition ratio in all types of sand waves, implying the possible dominant direction of hydrodynamics. The proposed scheme permits to delicately and adaptively explore the submarine geomorphology of sand waves with online computation strategies and symmetrically integrate evidence of its spatio-temporal responses during formation and migration.

Sand waves in the Taiwan Shoal are characterized by two distinct spatial scales. Giant sand waves have a length of 2 kilometers with height between 5 m and 25 m, whilst small sand waves is less than 100-m long with height less than 5 m between giant sand wave peaks (crests). A series of five high-resolution multi-beam echo-sounding surveys between 2012 and 2020 in the middle of Taiwan Shoal indicated that artificial dredging on the giant sand waves had caused sand wave reform and evolution. Overall, the removal of giant sand waves significantly affected the migration of small sand waves adjacent to the dredging site, with the latter on both sides of the former appear to migrate towards the dredging pit. Moreover, in the dredging area, new sand waves emerged with wavelength much smaller than the original giant sand waves, while the convergent pattern of the small sand waves tends to store and form the giant sand waves, which might spread far beyond the survey period.

This study focuses on the hindcasting and forecasting of observed offshore tidal sand waves by using a state-of-the-art numerical morphodynamic model. The sand waves, having heights of several meters, evolve on timescales of years. Following earlier work, the model has a 2DV configuration (one horizontal and one vertical direction). First, the skill of the model is assessed by performing hindcasts at four transects in the North Sea where sand wave data are available of multiple surveys that are at least 10 years apart. The first transect is used for calibration and this calibrated model is applied to the other three transects. It is found that the calibrated model performs well: the Brier Skill Score is ‘excellent’ at the first two transects and ‘good’ at the last two. The root mean square error of calculated bed levels is smaller than the uncertainty in the measurements, except at the last transect, where the M2 is more elliptical than at the other three transects. The calibrated model is subsequently used to make forecasts of the sand waves along the two transects with the best skill scores.

The hypothesis that the formation and dynamics of large scale shoreline sand waves can be explained by a feedback mechanism between waves and nearshore morphology under very oblique wave incidence is explored with a quasi 2D nonlinear morphodynamic model. Using constant wave conditions it is found that if the wave incidence angle at the depth of closure is larger than about 45° the rectilinear coastline becomes unstable and a shoreline sand wavefield develops from small random perturbations. Shoreline sand waves develop with wavelengths between 2 and 5 km, they migrate downdrift at about 0.5 km/yr and they reach amplitudes up to 120 m within 13 years. Larger wave obliquity, higher waves and shorter wave periods strengthen the shoreline instability. Cross‐shore transport is essential for the instability and faster cross‐shore dynamics leads to a faster growth of the sand waves. Simulations with variable wave incidence angles (alternating between 60° and 30°) show that a large proportion of high angle waves is required for spontaneous sand wave formation (at least 80%). Insight is provided into the physical mechanism behind high angle wave instability and the occurrence of a optimal length scale for sand wave growth. The generic model results are consistent with existing observations of shoreline sand waves, in particular with those along the southwest coast of Africa. Key Points Shoreline sand waves emerge from the feedback between morphology and wave field Wave angles persistently larger than 45 deg are required at the depth of closure Sand waves with a length of 2‐5 km and amplitudes up to 120 m develop in 13 yr

We use a linear stability approach to develop a process-based morphodynamic model including a two-way coupling between tidal sand wave dynamics and benthic organisms. With this model we are able to study both the effect of benthic organisms on the hydro- and sediment dynamics, and the effect of spatial and temporal environmental variations on the distribution of these organisms. Specifically, we include two coupling processes: the effect of the biomass of the organisms on the bottom slip parameter, and the effect of shear stress variations on the biological carrying capacity. We discuss the differences and similarities between the methodology used in this work and that from ‘traditional’ (morphodynamics only) stability modelling studies. Here, we end up with a \\[2\\times 2\\] linear eigenvalue problem, which leads to two distinct eigenmodes for each topographic wave number. These eigenmodes control the growth and migration properties of both sand waves and benthic organisms (biomass). Apart from hydrodynamic forcing, the biomass also grows autonomously, which results in a changing fastest growing mode (FGM, i.e. the preferred wavelength) over time. As a result, in contrast to ‘traditional’ stability modelling studies, the FGM for a certain model outcome does not necessarily have to be dominant in the field. Therefore, we also analysed the temporal evolution of an initial bed hump (without perturbing biomass) and of an initial biomass hump (without perturbing topography). It turns out that these local disturbances may trigger the combined growth of sand waves and spatially varying biomass patterns. Moreover, the results reveal that the autonomous benthic growth significantly influences the growth rate of sand waves. Finally, we show that biomass maxima tend to concentrate in the region around the trough and lee side slope of sand waves, which corresponds to observations in the field.

High-resolution bathymetric surveys are interpreted to describe the morphology and movement of a field of sand waves near San Francisco, CA, USA. The sand wave size, shape, orientation, and migration are described. Portions of the field are nearly two dimensional, but the overall field is three dimensional, with changes in sand wave size, orientation, and structure. The trough-to-crest height of individual sand waves ranges from 2 to 9 m. There are a range of wavelengths and orientations. The dominant wavelength ranges from 90 to 100 m, and the orientation clusters around two directions: 40 and 68 degrees. The field is found to be highly dynamic but with stable characteristics over at least decadal timescales. A previously demonstrated paradox between sand wave shape asymmetry and migration direction is revisited using new measurements, showing that reversals in shape asymmetry were accompanied by changes in crest migration direction in one of seven surveys.

Tidal sand waves form a dynamic bed pattern, widely occurring in shallow shelf seas such as the North Sea. Their importance to coastal engineering has inspired many advances in process-based sand wave modelling, aimed at explaining physical mechanisms in the formation stage (‘linear regime’) and capturing the finite amplitude evolution to equilibrium states (‘nonlinear regime’). However, systematic validation of particularly the nonlinear sand wave models is still lacking. Here, we perform a two-step calibration and validation study of a sand wave model (specifically, their linear and nonlinear model versions) against field data from the North Sea. In the first step, the linear model is calibrated by seeking overall values of two uncertain input parameters (slip parameter, wave period) for which the modeled and observed wavelengths show the best agreement. In the second step, using the calibrated input parameters and preferred wavelengths from the linear model, equilibrium heights from the nonlinear sand wave model are validated against the observed sand wave heights. Our results show satisfactory agreement between observed and modeled sand wave lengths (from the linear sand wave model) and a systematic overprediction of sand wave heights (using the nonlinear model). Regression analysis can be used to rescale the nonlinear model results to obtain realistic predictions of sand wave heights.

The eastern North Pacific Ocean coastline (from the Salish Sea to the western Aleutian Islands) is highly glaciated with relic sediment deposits scattered throughout a highly contoured and variable bathymetry. Oceanographic conditions feature strong currents and tidal exchange. Sand wave fields are prominent features within these glaciated shorelines and provide critical habitat to sand lance (Ammodytes spp.). Despite an awareness of the importance of these benthic habitats, attributes related to their structure and characteristics remain undocumented. We explored the micro-bathymetric morphology of a subtidal sand wave field known to be a consistent habitat for sand lance. We calculated geomorphic attributes of the bedform habitat, analyzed sediment composition, and measured oceanographic properties of the associated water column. This feature has a streamlined teardrop form, tapered in the direction of the predominant tidal current. Consistent flow paths along the long axis contribute to well-defined and maintained bedform morphology and margin. Distinct patterns in amplitude and period of sand waves were documented. Strong tidal exchange has resulted in well-sorted medium-to-coarse-grained sediments with coarser sediments, including gravel and cobble, within wave troughs. Extensive mixing related to tidal currents results in a highly oxygenated water column, even to depths of 80 m. Our analysis provides unique insights into the physical characteristics that define high-quality habitat for these fish. Further work is needed to identify, enumerate, and map the presence and relative quality of these benthic habitats and to characterize the oceanographic properties that maintain these benthic habitats over time.

Shallow underwater topography has important practical applications in fisheries, navigation, and pipeline laying. Traditional multibeam bathymetry is limited by the high cost of largescale topographic surveys in large, shallow sand wave areas. Remote sensing inversion methods to detect shallow sand wave topography in Taiwan rely heavily on measured water depth data. To address these problems, this study proposes a largescale remote sensing inversion model of sand wave topography based on long short-term memory network machine learning. Using multi-angle sun glitter remote sensing to obtain sea surface roughness (SSR) information and by learning and training SSR and its corresponding water depth information, the sand wave topography of a largescale shallow sea sand wave region is extracted. The accuracy of the model is validated through its application to a 774 km2 area in the sand wave topography of the Taiwan Banks. The model obtains a root mean square error of 3.31–3.67 m, indicating that the method has good generalization capability and can achieve a largescale topographic understanding of shallow sand waves with some training on measured bathymetry data. Sand wave topography is widely present in tidal environments; our method has low requirements for ground data, with high application value.

This paper aims at addressing issues related to the misrepresentation of the turbulent stratified flow for sand wave simulation using the continuum coupled flow and morphodynamics modeling approach. continuum flow and morphodynamics frameworks often consider the contributions of (i) suspended sediment-load in the sediment mass-balance equation and (ii) stratification in the equations of fluid motion. We argue that omitting these contributions could impact the numerical simulation results of transverse sand wave development at the late stages of bed evolution. We support our argument by presenting a numerical analysis, which attempts to quantify the impact of the stratification and suspended sediment-load on the calculated bed topography during various stages of transverse sand wave evolution in a mobile bed flume.