As the debate into a changing global climate continues, it is important that coastal engineers and scientists have the most advanced tools to quantify any resulting variation inthe coastal environment. This will aid the creation and implementation of effective shoreline management plans to mitigate these changes.This thesis presents a new combined Statistical-Process based Approach (SPA) for modelling storm driven, cross-shore, beach profile variability at a medium-term (annual todecadal) timescale. The methodology presented involves combining the detailed statistical modelling of offshore storm data and a process based morphodynamic model (XBeach), to assess, and quantify, the medium-term morphodynamic response of cross-shore beach profiles. Up until now the use of process-based models has been curtailed at the storm event timescale. This approach allows inclusion of the post-storm recovery period, inaddition to individual event impacts, thus allowing longer-term predictions. The use of aprocess-based model for simulating, both erosion and recovery, expands on previous work on the subject by allowing for the inclusion of antecedent beach profiles within the modelling framework.The XBeach model and the overall SPA procedure were calibrated and validated using measured wave and beach profile data from Narrabeen Beach, NSW, Australia. XBeachwas shown to give a good prediction of the post-storm profile for four varying storm events. In addition, by accounting for the hydrodynamic processes that govern accretion, and calibrating parameters accordingly, XBeach was also shown to provide a good representation of berm accretion during recovery periods. The combination of the erosion and accretion models was shown to produce extremely encouraging results at an annual timescale, by successfully following the trends in beach volume and the position of the 0m and 2m beach contours. The simulation of a longer sequence provided comparable medium-term erosion return levels.
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A statistical-process based approach for modelling beach profile variability