Aeolian Sediment Flux Derived from a Natural Sand Trap

In 2011, a mega-nourishment (the ‘Sand Motor’) was constructed along the Dutch Coast. Since it is a pilot project, its evolution is closely monitored. This paper presents first results on the temporal variation in aeolian sediment transport across the nourishment, based on (a) the rate of infill over a 4 year period of a small lake in the nourishment, (b) one year of semi-hourly collected video imagery and (c) four year of hourly-averaged wind data. It appeared that, apart from approximately the first half year, the infill occurred quite linearly over time at an average rate of about 1.9·104 m3/yr. The rate of infill in the first half year period was equivalent to an annual rate of 8.4·104 m3/yr. From the combination of video image data and wind data, it was derived that aeolian sand transport (by saltation) was only observed at hourly averaged wind speeds of at least 7 m/s. The monthly frequency of occurrence of above 7 m/s wind speed, was reasonably well correlated with monthly frequency of occurrence of aeolian transport (r=0.79). Nevertheless, when hourly wind speed exceeded 7 m/s, transport was only observed about 23% of the time, indicating the importance of supply limiting conditions for aeolian transport from the Sand Motor

Mangrove forest drag and bed stabilisation effects on intertidal flat morphology

Mangrove trees influence their physical environment by exerting drag on tidal flows and waves while also stabilising the sediment bed of intertidal flats. These processes influence sediment accretion, the mangrove habitat and their resilience to sea level rise. However, little is known about the magnitude and spatial extent of the effects of mangrove forests on sediment transport and the morphology of the intertidal flat. We use manipulated simulations with an extended process-based numerical model, to study the influence of mangrove forests on intertidal flat morphology on a yearly timescale. The model includes the influence of mangrove trees on tidal flows, waves and sediment dynamics. The model is calibrated and validated with a comprehensive set of measurement data including hydrodynamics, sediment transport and morphological processes from an expanding mangrove forest in the sediment-rich Firth of Thames estuary in Aotearoa New Zealand. Sediment accretion on the upper intertidal flat is predominantly influenced by the characteristic morphology of the established mangrove forest, with increased bed stability at higher mudflat elevations related to prolonged aerial exposure and drying of the bed. Our results show that, in comparison to the situation without mangroves, sediment accretion increases in the most seaward fringe area of the forest. The unvegetated intertidal flat fronting the mangrove forest captures less sediment compared to the situation without mangroves. The mangrove forest drag triggers the development of a steeper, convex-up-shaped, upper intertidal flat profile, especially during periods with higher water levels and waves. These effects are expected to influence the development and storm-recovery of natural and restored mangrove forests and may contribute to the resilience and persistence of mangrove-vegetated intertidal flats for coastal flood risk reduction.</p

Mangrove forest drag and bed stabilisation effects on intertidal flat morphology

Mangrove trees influence their physical environment by exerting drag on tidal flows and waves while also stabilising the sediment bed of intertidal flats. These processes influence sediment accretion, the mangrove habitat and their resilience to sea level rise. However, little is known about the magnitude and spatial extent of the effects of mangrove forests on sediment transport and the morphology of the intertidal flat. We use manipulated simulations with an extended process-based numerical model, to study the influence of mangrove forests on intertidal flat morphology on a yearly timescale. The model includes the influence of mangrove trees on tidal flows, waves and sediment dynamics. The model is calibrated and validated with a comprehensive set of measurement data including hydrodynamics, sediment transport and morphological processes from an expanding mangrove forest in the sediment-rich Firth of Thames estuary in Aotearoa New Zealand. Sediment accretion on the upper intertidal flat is predominantly influenced by the characteristic morphology of the established mangrove forest, with increased bed stability at higher mudflat elevations related to prolonged aerial exposure and drying of the bed. Our results show that, in comparison to the situation without mangroves, sediment accretion increases in the most seaward fringe area of the forest. The unvegetated intertidal flat fronting the mangrove forest captures less sediment compared to the situation without mangroves. The mangrove forest drag triggers the development of a steeper, convex-up-shaped, upper intertidal flat profile, especially during periods with higher water levels and waves. These effects are expected to influence the development and storm-recovery of natural and restored mangrove forests and may contribute to the resilience and persistence of mangrove-vegetated intertidal flats for coastal flood risk reduction

Predicting Urban Flooding Due to Extreme Precipitation Using a Long Short-Term Memory Neural Network

Extreme precipitation events can lead to the exceedance of the sewer capacity in urban areas. To mitigate the effects of urban flooding, a model is required that is capable of predicting flood timing and volumes based on precipitation forecasts while computational times are significantly low. In this study, a long short-term memory (LSTM) neural network is set up to predict flood time series at 230 manhole locations present in the sewer system. For the first time, an LSTM is applied to such a large sewer system while a wide variety of synthetic precipitation events in terms of precipitation intensities and patterns are also captured in the training procedure. Even though the LSTM was trained using synthetic precipitation events, it was found that the LSTM also predicts the flood timing and flood volumes of the large number of manholes accurately for historic precipitation events. The LSTM was able to reduce forecasting times to the order of milliseconds, showing the applicability of using the trained LSTM as an early flood-warning system in urban areas