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Measured trends and variability in shoreline position are used by coastal managers, scientists and engineers to understand and monitor coastal systems. This paper presents a new and generic method for automated shoreline detection from the largely unexplored collection of publicly available satellite imagery. The position of the obtained Satellite Derived Shoreline (SDS) is tested for accuracy for 143 images against high resolution in-situ data along a coastal stretch near the Sand Motor, a well-documented mega-scale nourishment along the Dutch coast. In this assessment, we quantify the effects of potential inaccuracy drivers such as the presence of clouds and waveinduced foam. The overall aim of this study is to verify whether the SDS is suitable to study structural coastline trends for coastal engineering practice.In the ideal case of a cloud free satellite image without the presence of waves, with limited morphological changes between the time of image acquisition and the date of the in-situ measurement, the accuracy of the SDS is with subpixel precision (smaller than 10-30 m, depending on the satellite mission) and depends on intertidal beach slope and image pixel resolution. For the highest resolution images we find an average offset of 1 m between the SDS position and the in-situ shoreline in the considered domain. The accuracy deteriorates in the presence of clouds and/or waves on the image, satellite sensor corrections and georeferencing errors. The case study showed that especially the presence of clouds can lead to a considerable seaward offset of the SDS of multiple pixels (e.g. order 200 m). Wave-induced foam results in seaward offsets in the order of 40 m.These effects can largely be overcome by creating composite images, which results in a continuous dataset with subpixel precision (10-30 m, depending on the satellite mission). This implies that structural trends can be detected for coastlines that have changed with at least the pixel resolution within the considered timespan.Given the accuracy of composite images along the Sand Motor in combination with the worldwide availability of public satellite imagery covering the last decades, this technique can potentially be applied at other locations with large (structural) coastline trends.

Aeolian sediment transport is influenced by a variety of bed surface properties, like moisture, shells, vegetation, and nonerodible elements. The bed surface properties influence aeolian sediment transport by changing the sediment transport capacity and/or the sediment availability. The effect of bed surface properties on the transport capacity and sediment availability is typically incorporated through the velocity threshold. This approach appears to be a critical limitation in existing aeolian sediment transport models for simulation of real-world cases with spatiotemporal variations in bed surface properties. This paper presents a new model approach for multifraction aeolian sediment transport in which sediment availability is simulated rather than parameterized through the velocity threshold. The model can cope with arbitrary spatiotemporal configurations of bed surface properties that either limit or enhance the sediment availability or sediment transport capacity. The performance of the model is illustrated using four prototype cases, the simulation of two wind tunnel experiments from literature and a sensitivity analysis of newly introduced parameters.