A Model-Derived Empirical Formulation for Wave Run-Up on Naturally Sloping Beaches

Abstract

A new set of empirical formulations has been derived to predict wave run-up at naturally sloping sandy beaches. They are obtained by fitting the results of hundreds of XBeach-NH+ model simulations. The simulations are carried out over a wide range of offshore wave conditions (wave heights ranging from 1 to 12 m and periods from 6 to 16 s), and surf zone (Dean parameters aD ranging from 0.05 to 0.30) and beach geometries (slopes ranging from 1:100 to 1:5). The empirical formulations provide estimates of wave set-up, incident and infragravity wave run-up, and total run-up R2%. Reduction coefficients are included to account for the effects of incident wave angle and directional spreading. The formulations have been validated against the Stockdon dataset and show better skill at predicting R2% run-up than the widely used Stockdon relationships. Unlike most existing run-up predictors, the relations presented here include the effect of the surf zone slope, which is shown to be an important parameter for predicting wave run-up. Additionally, this study shows a clear relationship between infragravity run-up and beach slope, unlike most existing predictors.

The Role of Different Total Water Level Definitions in Coastal Flood Modelling on a Low-Elevation Dune System

“…A better calculation of runup could also be developed and implemented in future works. The usage of new empirical formulas that show better results than Stockdon et al [47] could result in a better prediction of runup [68]. Furthermore, empirical methods are less detailed when compared to numerical modelling [69], leading to a higher uncertainty in the results.…”

A Hybrid Framework for Rapidly Locating Transition Zones: A Comparison of Event‐ and Response‐Based Return Water Levels in the Suwannee River FL

“…Data‐driven surrogate models empirically approximate the relationship between the inputs (and parameters) and the outputs of a complex model without attempting to emulate any of its internal parts (Razavi et al., 2012). Past applications of surrogate models in fluvial and coastal flooding studies range from conceptually simple look‐up tables (Apel et al., 2008) and empirical formulations (van Ormondt et al., 2021) to more complex approaches including Gaussian process models (Malde et al., 2016; Parker et al., 2019; Rohmer et al., 2022), kriging (Parker et al., 2019; Rohmer & Idier, 2012), 3D scatter interpolation (Serafin et al., 2019), bilinear interpolation (Couasnon et al., 2022), radial basis functions (Camus, Mendez, Medina, et al., 2011; Gouldby et al., 2014; Medellín et al., 2016; Rueda et al., 2016), support vector regression (Bermúdez et al., 2019; Chen et al.,. 2020; Jhong et al., 2017), random forests (Zahura & Goodall, 2022; Zahura et al., 2020), and artificial neural networks (Bermúdez et al., 2018; Peters et al., 2006; Santos et al., 2019).…”