Probabilistic modeling of ripple evolution

Abstract:

The presence of wave orbital ripples increases the roughness of the seafloor-water interface, which affects flow, sediment transport, and the acoustic response of the seafloor. Existing models of ripple formation and evolution typically use non-dimensional shear stress in the form of the Shield’s or mobility parameter to predict when ripples form. However, the shear stress depends on empirical parameters that are not easily constrained and on wave bottom orbital velocities that are rarely directly observed. Here, we present a model that predicts the times at which ripples evolve, or are "reset." This model requires significantly less input information than the Shield’s or mobility parameters, encapsulating the wave forcing into a new non-dimensional parameter that depends on only the depth, median grain size, and significant wave height. By representing ripple reset events as a stochastic point process, the model estimates the probability of ripples evolving within a given time interval and can quantify the uncertainty of those estimates. We train the model with wave forcing and ripple wavelength observations from three field sites along the Atlantic and Gulf coasts of the United States. The model accurately predicts the timing of ripple reset events across sites and seasons, and therefore has the potential to be successfully applied to additional sites to predict times of ripple evolution.