Our study aims to enhance process understanding of the long-term (decadal and longer) cyclic marsh dynamics by identifying the mechanisms that translate large-scale physical forcing in the system into vegetation change, in particular (i) the initiation of lateral erosion on an expanding marsh, and (ii) the control of seedling establishment in front of an eroding marsh-cliff. Short-term sediment dynamics (i.e., seasonal and shorter changes in sediment elevation) at the mudflat causes variation in mudflat elevation over time (δz_TF). The resulting difference in elevation between the tidal flat and adjacent marsh (ΔZ) initiates lateral marsh erosion. Marsh erosion rate was found to depend on sediment type and to increase with increasing ΔZ and hydrodynamic exposure. Laboratory and field experiments revealed that seedling establishment was negatively impacted by an increasing δz_TF. As the amplitude of δz_TF increases towards the channel, expanding marshes become more prone to lateral erosion the further they extend on a tidal flat, and the chance for seedlings to establish increases with the distance that marsh has eroded back towards the land. This process-based understanding, showing the role of sediment dynamics as explanatory factor for marsh cyclicity, is important for protecting and restoring valuable marsh ecosystems. Overall, our experiments emphasize the need for understanding the connections between neighbouring ecosystems such as mudflat and salt marsh.

Probabilistic estimates for coastal storm erosion volumes are increasingly being sought by contemporary risk based coastal zone management frameworks. Such estimates can be obtained via probabilistic models that incorporate a structural function element which calculates storm erosion (i.e. storm erosion model). Intuitively, the more sophisticated the storm erosion model embedded in the probabilistic model, the more accurate and robust the probabilistic storm erosion volumes should be, albeit at significant additional computational cost. This study assesses the relative performance of three storm erosion models with varying levels of complexity when embedded within Callaghan et al.'s (2008a) probabilistic framework for estimating storm erosion. The storm models tested are: the analytical Kriebel and Dean (1993) model, the more complex semi-empirical SBeach model and the highly complex and process-based XBeach model.The probabilistic model is applied at data rich Narrabeen beach, Australia. Kriebel and Dean (1993) and SBeach are used 'on-line' in the probabilistic simulations, while XBeach is used with an innovative off-line tabulation approach to facilitate reasonable computational times. SBeach is calibrated for a mid-range erosion event while XBeach is validated for the same single erosion event as well as for all measured storm erosion volumes during the 30. year study period. The Kriebel and Dean (1993) model is used with recommended parameter settings and therefore does not require calibration.When both SBeach and XBeach are calibrated against the single erosion event, SBeach provides the most accurate and robust probabilistic estimates of storm erosion. However, when XBeach is calibrated using the entire erosion volume data series, the results improve significantly raising the accuracy and robustness of the probabilistic estimates of storm erosion volumes obtained with XBeach to be on par with those obtained with SBeach. However, only XBeach predicts storm erosion volumes with the physically more plausible behaviour of a downward concave tail shape when plotted as cross-shore beach-erosion volume on a vertical linear axis against return period on a horizontal logarithmic axis.The simulation time (on a standard single processor) when using the simple Kriebel and Dean (1993) model is about 1. day, whereas for SBeach (on-line) and XBeach (tabulation), the simulation time is about 1000. h. However, the physically more plausible and the more accurate and robust results that can be obtained with SBeach or XBeach justifies the additional computational cost.