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The long-distance transport of Yellow River sediments has formed an omega-shaped ('Ω') distal muddy deposit in the Yellow Sea. Numerous studies have investigated the sediment dynamics and sedimentary evolution of the deposit, but several key questions remain unanswered due to insufficient observations. To reveal the mechanisms of sediment dynamics and quantify sediment transport and deposition processes, this study collected field datasets in the depocenter of the muddy deposit off the Shandong Peninsula. Bottom mounted tripods were deployed from August 18 to 27, 2017, and from February 23 to March 2, 2018, during which two strong wind events with speeds of over 10.8 m/s occurred. The results showed that near-bed suspended sediment concentration responded directly to varying hydrodynamic conditions, and significant differences in suspended sediment concentration between two wind events could be ascribed to a number of factors (e.g., tidal current, wave periods, wind stress, turbulence). The harmonic analysis demonstrated that suspended sediment variations during the spring tide were directed mainly by resuspension, while resuspension and advection contributed equally at neap tide. The variations in background suspended sediment concentration can be ascribed to the residual current. Furthermore, the sediment transport pattern is similar to the pattern of water circulation. Generally, the along-shore suspended sediment flux is about one order of magnitude higher than the cross-shore flux. In summer, the suspended sediment was transported mainly downcoast. In winter, sediment was transported downcoast during wavy periods and upcoast during calm periods. Advection dominated sediment flux in most cases, except during wavy periods when local resuspension increased significantly, and the contribution of resuspension term to along-shore flux was up to 87%. Meanwhile, cross-shore flux also increased significantly under strong winds, which was simultaneously modulated by downcoast and offshore winds. After statistical analysis, we found that offshore winds controlled the cross-shore sediment transport in winter. Using parameterized calculation and statistical analysis, the average accumulation rate of the study site was estimated to be 0.6 cm/yr. During calm periods, lower background sediment concentration in summer led to lower deposition rates than in winter. In addition, the frequent strong wind events in winter cause extremely high erosion rates. This study provides new perspectives on quantitative variations in suspended sediment transport and regional erosion/deposition from synoptic to seasonal variations in the distal mud deposit. • In-situ observations at the bottom boundary layer were conducted in summer and winter on the muddy deposit off the Shandong Peninsula. • The advection dominates sediment flux in most cases, except for during wind gusts when local resuspension increases significantly. • Combining parameterized method and observational data, the accumulation rate is estimated. [ABSTRACT FROM AUTHOR]

For moderate Shields number flows, the characteristic minimum sediment transport prediction error is estimated to be of the order of a factor of 2, even in stationary and horizontally homogeneous flows over a flat bottom. For oscillatory flows over ripples the same error is judged to be comparable to a factor of 4.In actual flows when the flow, sediments and bottom shape have to be predicted, moderate estimation errors for the near bottom turbulent stress results in characteristic minimum sediment transport error that is probably at least of the order of a factor of 10.The equations for the large-scale average flow require nonstandard parameterizations of the smaller scale ripple flow and boundary conditions. Only weakly founded closure relations are available for this. The error of the large-scale sediment transport can therefore not be quantified without extensive data comparisons. As extrapolated from the simpler estimates, it appears plausible that it could be significantly larger than a factor of 10.When the sediment transport variability is large, both in reality and in models, many models can probably be adjusted to fit the few data available. This may not be relevant for judging the prediction accuracy. [Copyright &y& Elsevier]