Characterizing and hindcasting ripple bedform dynamics: Field test of non-equilibrium models utilizing a fingerprint algorithm

Abstract

Ripple bedform response to near bed forcing has been found to be asynchronous with rapidly changing hydrodynamic conditions. Recent models have attempted to account for this time variance through the introduction of a time offset between hydrodynamic forcing and seabed response with varying success. While focusing on temporal ripple evolution, spatial ripple variation has been partly neglected. With the fingerprint algorithm ripple bedform parameterization technique, spatial variation can be quickly and precisely characterized, and as such, this method is particularly useful for evaluation of ripple model spatio-temporal validity. Using time-series hydrodynamic data and synoptic acoustic imagery collected at an inner continental shelf site, this study compares an adapted time-varying ripple geometric model to observed field observations in light of the fingerprint algorithm results. Multiple equilibrium ripple predictors are tested within the time-varying model, with the algorithm results serving as the baseline geometric values. Results indicate that ripple bedforms, in the presence of rapidly changing high-energy conditions, reorganize at a slower rate than predicted by the models. Relict ripples were found to be near peak-forcing wavelengths after rapidly decaying storm events, and still present after months of sub-critical flow conditions.