Validation of swash model for run-up prediction on a natural embayed beach




Run-up, coastal video monitoring, SWASH model, UAV, MeteOcean wave database


The work presents the evaluation of performance in wave run-up predicting of the well-known numerical model SWASH (Zijlema et alii, 2011), used to reproduce the swash zone hydrodynamics of a natural embayed beach. Field data collected at the bay of Torre Lapillo (Lecce, Italy) by means of video-analysis techniques are used for such purpose. A new automated coastal remote video-monitoring system has been recently installed in collaboration with the Apulian Basin Authority (AdBP), allowing image processing and geo-correction, shoreline extraction and results storage in real time on a public web portal. The model-numerical chain is based on coupling different models, starting from waves predicted by the MeteoOean model (Mentaschi et alii, 2015) up to a one-way nesting of 2D SWAN-SWASH models, in order to obtain an accurate representation of wave processes from deep to shallow waters. The topographic input derives from the combination of different surveys performed by means of Multibeam probe and GPS d-RTK together with a DSM reconstructed from an Unmanned Aerial Vehicle, resulting in a submerged as well as emerged digital model with a high resolution of about 0.015 m. Results show a good agreement between the measured and the simulated values, and confirm the possibility of reconstructing, even on non-instrumented beaches, the hydrodynamics of the nearshore area by numerical means. Simulations are performed with the default empirical coefficients as documented for the model, and a sensitivity analysis is presented on the key wave-breaking parameters (α, b) and the minimum depth threshold (δ), in order to optimize them to most accurately reproduce the observations.




How to Cite

Damiani, L., Saponieri, A., & Valentini, N. (2018). Validation of swash model for run-up prediction on a natural embayed beach. Italian Journal of Engineering Geology and Environment, 27–37.