Insights into nearshore sandbar dynamics through process-based numerical and logistic regression modeling

Abstract

Process-based nearshore morphodynamic models are commonly used tools by coastal engineers and planners to predict the nearshore morphology change of sandy beaches across various spatiotemporal scales. Accurate modeling of the morphological response on medium and long time scales is imperative for quantitative assessments of coastal infrastructure over a project’s intended life-span. However, most previous modeling applications have focused on single/sub-seasonal storm events and are often limited to an assessment of the subaerial beach (i.e. berm and dune). This not only leaves uncertainty concerning the quality of morphology predictions on extended ($>$ weeks) time scales, but also the capacity of process-based models to emulate realistic nearshore sandbar dynamics and the corresponding exchange of sediment between the nearshore-beach system. To shed light on these meso-scale dynamics, CSHORE, a 1D phase-averaged, process-based nearshore morphodynamic model, was applied on an annual scale to a multi-barred, dissipative beach in Oysterville, WA, USA. Thousands of unique sediment transport and hydrodynamic parameter combinations were executed during model calibration. A large portion of these simulations displayed physically realistic sandbar dynamics, including the growth, decay, and migration of intertidal and subtidal sandbars. To explore the model mechanisms enabling realistic bar behavior, the binary and multinomial logistic regression model were used to quantify the relationship between model parameter selection and the probability of various categorical bar configurations occurring in the final predicted profile. The results indicate the most sensitive parameters associated with barred morphology, in this study, and support the use of separate sediment transport parameters for low and high wave energy conditions. The co-utilization of numerical and statistical modeling outlined in this publication is generalizable to future exploratory modeling and/or calibration routines concerned with categorical outcomes.