Understanding the role of beach morphology in controlling wave overtopping volume will further minimise uncertainties in flood risk assessments at coastal locations defended by engineered structures worldwide. XBeach is used to model wave overtopping volume for a 1:200 year joint probability distribution of waves and water levels with measured, pre- and post-storm beach profiles. The simulation with measured bathymetry is repeated with and without morphological evolution enabled during the modelled storm event. This research assesses the role of morphology in controlling wave overtopping volumes for hazardous events that meet the typical design level of coastal defence structures. Results show that disabling storm-driven morphology under-represents modelled wave overtopping volumes by up to 39% under high $H_s$ conditions and has a greater impact on the wave overtopping rate than the variability applied within the boundary conditions due to the range of wave-water level combinations that meet the 1:200 year joint probability criterion. Accounting for morphology in flood modelling is therefore critical for accurately predicting wave overtopping volumes and the resulting flood hazard and to assess economic losses.

This paper provides a sensitivity analysis around how characterizing sandy, intertidal foreshore evolution in XBeach-X impacts on wave runup and morphological change of a vulnerable, composite gravel beach. The study is motivated by a need for confidence in storm-impact modeling outputs to inform coastal management policy for composite beaches worldwide. First, the model is run with the sandy settings applied to capture changes in the intertidal foreshore, with the gravel barrier assigned as a non-erodible surface. Model runs were then repeated with the gravel settings applied to obtain wave runup and erosion of the barrier crest, updating the intertidal foreshore from the previous model outputs every 5, 10 and 15 min, and comparing this with a temporally static foreshore. Results show that the scenario with no foreshore evolution led to the highest wave runup and barrier erosion. The applied foreshore evolution setting update is shown to be a large control on the distribution of freeboard values indicative of overwash hazard and barrier erosion by causing an increase (with 5 min foreshore updates applied) or a decrease (with no applied foreshore updating) in the Iribarren number. Therefore, the sandy, intertidal component should not be neglected in gravel barrier modeling applications given the risk of over- or under-predicting the wave runup and barrier erosion.

Safe port operations require accurate information on vessel location, routine monitoring and maintenance of navigation channels, and accurate information on coastal hydrodynamics.  Accurate bathymetric data enables port operators to have a high level of confidence in assuring sufficient water depth for vessels, and to effectively direct surveying and dredging operations to maintain navigation routes. However, this is not readily facilitated for nearshore approaches where migrating sandbanks and shoals pose a hazard to shipping.In this presentation, we present an innovative and novel data assimilation method of combining satellite data, hydrodynamic model (Delft3D) outputs and land-based radar data using machine learning and advanced statistical methods (Dynamic Mode Decomposition). To assimilate these data we use machine learning and statistical methods to detect "patterns" or "modes" in near- and far-field wave climate that are attributable to sub- and intertidal bathymetry and changes therein. We then combine the dominant modes into a low-order representation of the system, providing informed estimates of spatial resolutions and temporal scales where no measurements are physically performed. Satellite data and associated hydrodynamic model outputs are used to provide information on wave direction and height for the offshore-nearshore approaches while land-based marine radar located in the appropriate position provide wave data at higher temporal and more local spatial resolution. The data nexus we present in this presentation demonstrates significant improvements in capability above and beyond the use of a given technology in isolation.