Incorporating wave-breaking data in the calibration of a Boussinesq-type wave model

Wave breaking is one of the main forcing mechanisms in coastal hydrodynamics, driving mean water levels and currents. Its accurate representation and modeling is therefore essential to develop a thorough understanding and predictive capabilities. In this work, the performance of a 1D Boussinesq Type Model is assessed by expanding the traditional approach of calibrating the models against profiles and time series of wave quantities data, with remotely sensed observations of the spatio-temporal occurrence of breaking events, on a laboratory setting considering one regular and one irregular wave condition over a fixed barred beach. A re-analysis of calibrated parameters from a wave breaking model based on the turbulent roller analogy is performed using observed wave-by-wave breaking events. The calibration process aims at matching the initiation/cessation of individual breaking events, and the histograms of breaking occurrence at each location. Results confirm that it is possible to find a physically sound parameter space that yields minimal differences in profiles of phase averaged quantities such as the root-mean-square wave height, wave setup, skewness, and asymmetry; nevertheless, not all of these parameter combinations succeed in representing the spatio-temporal occurrence of breaking events. These results suggest that models can reproduce the overall behavior of wave breaking dissipation, although this does not necessarily mean that the process itself is appropriately modeled. This could in turn influence the modeling capability of other quantities derived from these models, such as mean currents. It is thus considered that incorporating spatio-temporal wave breaking data is essential in assessing model performances. •The performance of a 1D Boussinesq-Type Model (BTM) is assessed by incorporating remotely sensed wave breaking data in the model calibration.•Wave breaking events recorded from a video camera station were used to derive the empirical fraction of breaking waves.•The inclusion of the fraction of breaking waves data in the calibration of a BTM yields a net benefit in model performance.