A detailed description of the model construction and its modeling strategy for a long-term scale is introduced in Zhang et al. (2010). In this paper we introduce mainly the concepts of representative climate input conditions for the morphodynamic model and the methodology for generating representative climate input conditions.

The coastline changes of this area in the next 300 years, based on four different climate scenarios derived from different studies, are then projected by the model, through which the impacts of accelerated sea level rise and storm frequency on long-term coastline change are quantified. Simulation of the Pexidartinib decadal-to-centennial morphological evolution of the Darss-Zingst peninsula is based on a multi-scale morphodynamic model consisting of 8 modules to calculate different physical processes that drive the evolution of the specific

coastal environment. The two-dimensional vertically integrated circulation module, the wave module, the bottom boundary layer module, the sediment transport module, the cliff erosion module and the nearshore storm module are real-time calculation modules that aim to solve short-term Bortezomib mouse processes. A bathymetry update module and a long-term control function set, in which the ‘reduction’ concepts and technique for morphological update acceleration are implemented, are integrated to up-scale the effects of short-term processes to a decadal-to-centennial scale. Boundary input conditions for a long-term (decadal-to-centennial) morphodynamic model such as time series of tides, winds, waves and mass flux cannot be specified at a centennial time span owing to the lack of measurements. On the other hand, even if detailed, measured Farnesyltransferase time series of boundary conditions were provided, it would be an extremely time-consuming job for a high-resolution process-based model to calculate the centennial-scale coastal evolution with the measured time series. This is because the time step of calculation in high-resolution process-based models is determined by the shortest time scale process, which usually

has to be solved on a time scale of seconds or minutes. Representative input conditions, which are generated by the statistical analysis of the measured time series, provide an effective way of solving the input problem for the long-term model. The generation of representative input conditions is based on the concept of ‘input reduction’ for long-term modelling (de Vriend et al. 1993a,b). The criterion for judging the validity of the representative input conditions is whether the simulation results based on the representative input conditions are the same as the reference data. As the effects of tides can be neglected in the southern Baltic Sea, only the time series of winds are needed for generating the representative input conditions.