A new formula for predicting movable bed roughness coefficient in the Middle Yangtze River

Computing movable bed roughness plays an important role in the modeling of flood routing and bed deformation, and the magnitude of movable bed roughness is closely associated with complex bedform configurations that change with the sand wave motion. The motion of sand wave is dependent on the incoming flow and sediment conditions and channel boundary. After the operation of the Three Gorges Project, the flow and sediment regime changed remarkably in the Middle Yangtze River (MYR), followed by significant channel adjustments. A dramatic decrease in sediment concentration caused continuous channel degradation and significant variations in cross-sectional profiles of the MYR. These adjustments in the channel boundary influence the motion of sand wave, which can further affect the magnitude of movable bed roughness. A new formula for predicting the movable bed roughness coefficient is developed, which can be expressed by a power function of both Froude number and relative water depth. The proposed formula was first calibrated using 1266 datasets of measurements at five hydrometric stations in the MYR during 2001–2012. A back-calculation process shows that the roughness coefficients calculated by the proposed formula agree well with the observations, with the determination coefficient being equal to 0.88. The proposed formula was further verified using 651 datasets of measurements at these hydrometric stations during 2013–2017. Furthermore, four common roughness formulas selected from the literature were tested for comparison. The results indicate that the calculation accuracy of the proposed formula is significantly higher than that of the previous formulas, and the Manning roughness coefficients predicted by the proposed formula have the errors less than ±30% for 96% of the datasets. Therefore, the new bed roughness predictor proposed in this study can accurately calculate the roughness coefficients straightforwardly without iterative solution and graphical interpolation, and the parameters required in the roughness predictor are easily obtained from the hydrometric observations.

Large and medium-scale morpho-sedimentary features of the Messina Strait: insights on bottom-current controlled sedimentation and interaction with downslope processes

The Messina Strait is a ∼ 3-8 km wide and 40 km-long extensional area that connects the Tyrrhenian Sea with the Ionian Sea (Mediterranean Sea), and where tectonics, oceanographic and erosive-depositional downslope processes strongly interact each other. Based on the analysis of high-resolution multibeam data, we present an updated morpho-sedimentary framework that reveals a complex seabed morphology, characterized by a variety of features linked to bottom-currents and downslope processes. In particular, we recognize a suite of large to medium-scale erosive and depositional features, related to different bottom-currents (e.g., reverse tidal flows, residual flows, internal waves) acting over diverse time periods. Large scale bottom-current features are represented by contourite drifts and channels developed over long periods (> thousands of years). Medium-scale features formed during shorter time periods and include scours, furrows, transverse ridges (pinnacles) and narrow longitudinal bodies in the sill sector, along with several sand wave fields, located at greater depths on the Ionian and Tyrrhenian sides of the Messina Strait. Downslope processes encompass channelized features originated by sedimentary gravity flows, coarse-grained aprons and fans and submarine landslides. They mostly occur along strait margins and become predominant in the southern exit where the axial Messina canyon and its tributaries are present.Overall, our study shows that the MS is a fruitful area in which to investigate the interaction between recent erosive-depositional sedimentary and oceanographic processes, also modulated by sea-level fluctuations, during the last eustatic cycle. Moreover, the observed seabed morphologies and the associated processes provide insights for interpreting similar features in modern and ancient similar straits and seaways.

Modelling the Past and Future Evolution of Tidal Sand Waves

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.

Numerical Analysis of Modified Seabed Topography Due to the Presence of Breakwaters of Varying Reflection Characteristics using Physics-based Morphology Model [SeoulFoam]

Numerical simulations were implemented to look into the modified seabed topography due to the presence of breakwaters of varying reflection characteristics. The numerical model was composed of OlaFlow, an OpenFoam-based tool box, and a physics-based morphology model [Seoul Foam]. In doing so, the interaction between the seabed, which undergoes deformation due to siltation and scouring, and the incoming waves was described using Dynamic Mesh. The rubble-mound, vertical, and curved slit caisson breakwaters with varying reflection characteristics resulted in standing waves that differ from each other, shown to have a significant influence on the seabed topography. These results are in line with Nielsen’s study (1993) that sands saltated under the surface nodes of standing waves, where the near-bed velocities are most substantial, convected toward the surface antinodes by boundary-layer drift. Moreover, the crest of sand waves was formed under the surface antinodes of standing waves, and the trough of sand waves was formed under the surface antinodes. In addition, sand wave amplitude reaches its peak in the curved slit caisson with a significant reflection coefficient, and the saltation of many grains of sand would cause this phenomenon due to the increased near-bed velocity under the nodes when the reflection coefficient is getting large.

LSTM-Based Remote Sensing Inversion of Largescale Sand Wave Topography of the Taiwan Banks

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.

Managing Pipeline Integrity and Dynamic Free Spans on Mobile Seabed in the Southern North Sea

This paper describes the continuous improvement efforts to manage the integrity status of the Southern North Sea subsea pipeline system in the context of free spanning. The dynamic free-spanning threat is typically attributed to a mobile seabed. Current and wave action are constantly moving and eroding sediment by means of sand wave migration and scouring. It can lead to a fluctuation in span characteristics with respect to span length, span height and location over time. It makes pipeline integrity demonstration and spans remediation challenges. Focus areas include (1) identifying regions where operational pipelines are susceptible to critical span formation (2) understanding the broader context of seabed mobility, supported by several years of multibeam echo sound and met ocean data (3) risk-ranking & criticality of span formation (4) developing simplified calculation tool that allows fatigue damage to be estimated and accumulated for every location along the pipeline, conservatively (5) optimising and incorporating risk/event-based survey requirements (6) identification of suitable remediation solutions and developing a decision flow chart to facilitate selection of fit for purpose remediation solutions, with respect to span configuration and the surrounding seabed features. The outcome has improved the robustness of span management, reduced “reactive” span remediation activities, and allowed application of sound technical theory to allocate pipeline traffic light integrity status regarding the observed free spans.

Towards Characterizing and Developing Formation and Migration Cues in Seafloor Sand Waves on Topology, Morphology, Evolution from High-Resolution Mapping via Side-Scan Sonar in Autonomous Underwater Vehicles

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.