AbstractCoastal wetlands fulfil important functions for biodiversity conservation and coastal protection, which are inextricably linked to typical morphological features like tidal channels. Channel network configurations in turn are shaped by bio-geomorphological feedbacks between vegetation, hydrodynamics and sediment transport. This study investigates the impact of two starkly different recruitment strategies between mangroves (fast/homogenous) and salt marshes (slow/patchy) on channel network properties. We first compare channel networks found in salt marshes and mangroves around the world and then demonstrate how observed channel patterns can be explained by vegetation establishment strategies using controlled experimental conditions. We find that salt marshes are dissected by more extensive channel networks and have shorter over-marsh flow paths than mangrove systems, while their branching patterns remain similar. This finding is supported by our laboratory experiments, which reveal that different recruitment strategies of mangroves and salt marshes hamper or facilitate channel development, respectively. Insights of our study are crucial to understand wetland resilience with rising sea-levels especially under climate-driven ecotone shifts.

. An ecomorphodynamic model was developed to study how
. Avicennia marina
. mangroves influence channel network evolution in sandy tidal embayments. The model accounts for the effects of mangrove trees on tidal flow patterns and sediment dynamics. Mangrove growth is in turn controlled by hydrodynamic conditions. The presence of mangroves was found to enhance the initiation and branching of tidal channels, partly because the extra flow resistance in mangrove forests favours flow concentration, and thus sediment erosion in between vegetated areas. The enhanced branching of channels is also the result of a vegetation-induced increase in erosion threshold. On the other hand, this reduction in bed erodibility, together with the soil expansion driven by organic matter production, reduces the landward expansion of channels. The ongoing accretion in mangrove forests ultimately drives a reduction in tidal prism and an overall retreat of the channel network. During sea-level rise, mangroves can potentially enhance the ability of the soil surface to maintain an elevation within the upper portion of the intertidal zone, while hindering both the branching and headward erosion of the landward expanding channels. The modelling results presented here indicate the critical control exerted by ecogeomorphological interactions in driving landscape evolution.
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Abstract. An artificial neural network (ANN) was developed to predict the depth-integrated alongshore suspended sediment transport rate using 4 input variables (water depth, wave height and period, and alongshore velocity). The ANN was trained and validated using a dataset obtained on the intertidal beach of Egmond aan Zee, the Netherlands. Root-mean-square deviation between observations and predictions was calculated to show that, for this specific dataset, the ANN (εrms=0.43) outperforms the commonly used Bailard (1981) formula (εrms=1.63), even when this formula is calibrated (εrms=0.66). Because of correlations between input variables, the predictive quality of the ANN can be improved further by considering only 3 out of the 4 available input variables (εrms=0.39). Finally, we use the partial derivatives method to "open and lighten" the generated ANNs with the purpose of showing that, although specific to the dataset in question, they are not "black-box" type models and can be used to analyze the physical processes associated with alongshore sediment transport. In this case, the alongshore component of the velocity, by itself or in combination with other input variables, has the largest explanatory power. Moreover, the behaviour of the ANN indicates that predictions can be unphysical and therefore unreliable when the input lies outside the parameter space over which the ANN has been developed. Our approach of combining the strong predictive power of ANNs with "lightening" the black box and testing its sensitivity, demonstrates that the use of an ANN approach can result in the development of generalized models of suspended sediment transport.
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