This is due mainly to three reasons: (1) the time step of calculation in high-resolution process-based models (the first type of model) is determined by the shortest time-scale process, i.e. usually of the order of seconds or minutes, and truncation errors generated after each calculation time step can accumulate during continuous run cycles in a long-term model, giving rise to substantial bias between the final simulation results and reality; (2) detailed time series of data (e.g. flows, waves and sediment) covering such a long time span serving as model boundary input are absent; and (3) the variation of selleck products bathymetry occurring in a stochastically

short time period, e.g. in a wind storm period, may exceed the change in a longer time span (1 year). One way

of bridging the gap between the simulation of short-term hydrodynamics, sediment transport Selleck Ponatinib and morphological changes taking place over much longer timescales is to integrate the concepts of ‘reduction’ (de Vriend et al. 1993a,b, Latteaux 1995) and techniques of morphological update acceleration (Roelvink 2006, Jones et al. 2007) into high-resolution process-based models. Three approaches can be derived from the ‘reduction’ strategy: (1) model reduction, in which only the main driving terms on the scale of interest are considered, while small scale processes that can be smoothed over a longer time period are avoided or integrated into an average term; (2) input reduction, in which the input data Vasopressin Receptor should be refined into some representative data groups capable of producing similar results as the whole variety of real time series on the scale

of interest; and (3) behaviour-based models, in which small scale processes are replaced by observational knowledge. By ‘extracting’ the most important processes responsible for the long-term coastal morphological evolution based on the concepts of ‘reduction’ and combining the technique of morphological update acceleration, high-resolution process-based models are applied to long-term simulation. Decadal tidal inlet change (Cayocca 2001, Dissanayake & Roelvink 2007), decadal micro-tidal spit-barrier development (Jiménez & Arcilla 2004), millennial tidal basin evolution (Dastgheib et al. 2008) and millennial delta evolution (Wu et al. 2006) were all simulated by such models, in which promising results were obtained. Recently, a modeling methodology was developed by the authors for simulating the decadal-to-centennial morphological evolution of wave-dominated barrier islands in the southern Baltic Sea (Zhang et al. 2010). The methodology consists of two main components: (1) a preliminary analysis of the key processes driving the morphological evolution of the study area based on statistical analysis of meteorological data and sensitivity studies, and (2) a multi-scale process-based morphodynamic model, in which the ‘reduction’ concepts and techniques for morphological update acceleration are implemented.