Hurricane Sandy (2012), which made landfall in New Jersey on October 29th, made devastating impact on the East Coast of the USA and struck major parts of New York City, including the economic centre of Manhattan. The total damage (in the USA and Caribbean) is in excess of 100 billion US$ with estimates ranging between 78 and 97 billion US$ for direct damage and over 10 to 16 billion US$ for indirect damage due to business interruption (M. Kunz et al., 2013). Modelling impact (e.g. damage, fatalities) in the coastal zone due to hazardous storm events is a hardly explored practice. It is difficult to predict damage correctly where damage observations are scarce and the physical processes causing the damage are complex, diverse and can differ from site to site and event to event. Moreover, an increasing interest exists in getting insights in the uncertainty of prediction. This report explores on the possibilities in coupling physics-based hydraulic and morphodynamic modelling to the practice of impact mapping by using Bayesian Belief Networks (BBN’s). Using BBN’s enables to look at the impact in a probabilistic context, which fits well to the highly unpredictable and rare nature of hurricanes. The morphodynamic storm impact model XBeach (Roelvink et al., 2009) is proposed as one of the process-based models, since Hurricane Sandy pointed out that morphodynamic aspects can be of great importance for the amount of damage. Part of the Rockaway Peninsula, NY, which has been severely damaged by Hurricane Sandy’s surges, is used as case study. Only damage to residential buildings is considered in present study, in which delivering a proof of concept for the presented approach is the main goal. In general it can be concluded that the approach succeeded.