River deltas are built by cycles of lobe growth and abrupt channel shifts, or avulsions, that occur within the backwater zone of coastal rivers. Previous numerical models differ on the origin of backwater‐scaled avulsion nodes and their consistency with experimental data. To unify previous work, we developed a numerical model of delta growth that includes backwater hydrodynamics, river mouth progradation, relative sea level rise, variable flow regimes, and cycles of lobe growth, abandonment, and reoccupation. For parameter space applicable to lowland deltas, we found that flow variability is the primary mechanism to cause persistent avulsion nodes by focusing aggradation within the backwater zone. Backwater‐scaled avulsion nodes also occur under less likely scenarios of initially uniform bed slopes or during rapid relative sea level rise and marine transgression. Our findings suggest that flow variability is a fundamental control on long‐term delta morphodynamics.

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We analyze why the sediment discharges of fine-grained rivers are an order of magnitude larger than predicted by current theory.

River deltas grow by repeating cycles of lobe development punctuated by channel avulsions, so that over time, lobes amalgamate to produce a composite landform. Existing models have shown that backwater hydrodynamics are important in avulsion dynamics, but the effect of lobe progradation on avulsion frequency and location has yet to be explored. Herein, a quasi‐2‐D numerical model incorporating channel avulsion and lobe development cycles is developed. The model is validated by the well‐constrained case of a prograding lobe on the Yellow River delta, China. It is determined that with lobe progradation, avulsion frequency decreases, and avulsion length increases, relative to conditions where a delta lobe does not prograde. Lobe progradation lowers the channel bed gradient, which results in channel aggradation over the delta topset that is focused farther upstream, shifting the avulsion location upstream. Furthermore, the frequency and location of channel avulsions are sensitive to the threshold in channel bed superelevation that triggers an avulsion. For example, avulsions occur less frequently with a larger superelevation threshold, resulting in greater lobe progradation and avulsions that occur farther upstream. When the delta lobe length prior to avulsion is a moderate fraction of the backwater length (0.3– 0.5trueL‾b), the interplay between variable water discharge and lobe progradation together set the avulsion location, and a model capturing both processes is necessary to predict avulsion timing and location. While this study is validated by data from the Yellow River delta, the numerical framework is rooted in physical relationships and can therefore be extended to other deltaic systems.

Abstract. The entrainment and suspension of sand and gravel are important for the evolution of rivers, deltas, coastal areas, and submarine fans. The prediction of a vertical profile of suspended sediment concentration typically consists of assessing (1) the concentration near the bed using an entrainment relation and (2) the upward vertical distribution of sediment in the water column. Considerable uncertainty exists in regard to both of these steps, especially the near-bed concentration. Most entrainment relations have been tested against limited grain-size-specific data, and no relations have been evaluated for gravel suspension, which can be important in bedrock and mountain rivers. To address these issues, we compiled a database with suspended sediment data from natural rivers and flume experiments, taking advantage of the increasing availability of high-resolution grain size measurements. We evaluated 12 dimensionless parameters that may determine entrainment and suspension relations and applied multivariate regression analysis. A best-fit two-parameter equation (r2=0.79) shows that near-bed entrainment, evaluated at 10 % of the flow depth, decreases with the ratio of settling velocity to skin-friction shear velocity (wsi/u∗skin), as in previous relations, and increases with Froude number (Fr), possibly due to its role in determining bedload-layer concentrations. We used the Rouse equation to predict concentration upward from the reference level and evaluated the coefficient βi, which accounts for differences in the turbulent diffusivity of sediment from the parabolic eddy viscosity model used in the Rouse derivation. The best-fit relation for βi (r2=0.40) indicates greater relative sediment diffusivities for rivers with greater flow resistance, possibly due to bedform-induced turbulence, and larger wsi/u∗skin; the latter dependence is nonlinear and therefore different from standard Rouse theory. In addition, we used empirical relations for gravel saltation to show that our relation for near-bed concentration also provides good predictions for coarse-grained sediment. The new relations extend the calibrated parameter space over a wider range in sediment sizes and flow conditions compared to previous work and result in 95 % of concentration data throughout the water column predicted within a factor of 9.