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Coastal zones are vulnerable to both erosion and flood risk, which can be assessed using coupled hydro-morphodynamic models. However, the use of such models as decision support tools suffers from a high degree of uncertainty, due to both incomplete knowledge and natural variability in the system. In this work, we show for the first time how the multilevel Monte Carlo method (MLMC) can be applied in hydro-morphodynamic coastal ocean modelling, here using the popular model XBeach, to quantify uncertainty by computing statistics of key output variables given uncertain input parameters. MLMC accelerates the Monte Carlo approach through the use of a hierarchy of models with different levels of resolution. Several theoretical and real-world coastal zone case studies are considered here, for which output variables that are key to the assessment of flood and erosion risk, such as wave run-up height and total eroded volume, are estimated. We show that MLMC can significantly reduce computational cost, resulting in speed up factors of 40 or greater compared to a standard Monte Carlo approach, whilst keeping the same level of accuracy. Furthermore, a sophisticated ensemble generating technique is used to estimate the cumulative distribution of output variables from the MLMC output. This allows for the probability of a variable exceeding a certain value to be estimated, such as the probability of a wave run-up height exceeding the height of a seawall. This is a valuable capability that can be used to inform decision-making under uncertainty. • First use of MLMC (multilevel Monte Carlo) with a coupled hydro-morphodynamic model. • Large drop in computational cost for same accuracy using MLMC instead of Monte Carlo. • Determine the distribution of variables of interest in coastal zone cases using MLMC. [ABSTRACT FROM AUTHOR]

Abstract: Coastal dunes can act as a method of soft coastal protection against inundation and direct impact of waves during storms if they are substantially large enough in volume to withstand erosion without breaching. However, the time evolution of sand dunes under direct wave impact is not well understood and many available models require site specific calibration and have had limited verification at field scales. Here we test three models of varying complexity in their ability to predict both dry beach erosion volumes and dune to a retreat for an East Coast Low storm event that occurred on the Gold Coast, Australia. The process-based model, XBeach, which models the entire profile was able to reproduce both dune toe retreat and dry beach volume, however, was sensitive to calibration parameters. The two parametric models that only modeled erosion above the initial dune toe position were capable of accurately predicting dune toe retreat, however, under-estimated dry beach erosion volumes. With no calibration, the parametric model proposed by Palmsten and Holman (2012) produced the smallest errors of dune toe retreat with mean error in final dune position of 6.6m, or 18% of the total measured dune retreat. With minimal calibration estimated absolute error in average dune toe retreat was less than 13% of observed retreat for all three models. [Copyright &y& Elsevier]