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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. [ABSTRACT FROM AUTHOR]

Inland navigation structures (INS) facilitate transportation of goods in rivers and canals. Transportation of goods over waterways is more energy efficient than on roads and railways. INS, similar to other civil structures, are aging and require frequent condition assessment and maintenance. Countries, in which INS are important to their economies, such as the Netherlands and the United States, allocate significant budgets for maintenance and renovation of exiting INS, as well as for building new structures. Timely maintenance and early detection of a change to material or geometric properties (i.e., damage) can be supported with the structural health monitoring (SHM), in which monitored data, such as load, structural response, environmental actions, are analyzed. Huge scientific efforts are realized in bridge SHM, but when it comes to SHM of INS, the efforts are significantly lower. Therefore, the SHM community has opportunities to develop new solutions for SHM of INS and convince asset owners of their benefits. This review article, first, articulates the need to keep INS safe to use and fit for purpose, and the challenges associated with it. Second, it defines and reviews sensors, sensing technologies, and approaches for SHM of INS. Then, INS and their components, including structures in ports, are identified, described, and illustrated, and their monitoring efforts are reviewed. Finally, the review article emphasizes the added value of SHM systems for INS, concludes on the current achievements, and proposes future trajectories for SHM of INS and ports. [ABSTRACT FROM AUTHOR]

The increasing number and severity of floods, driven by phenomena such as urbanization, deforestation, subsidence and climate change, creates a growing need for accurate and timely flood maps. This research focussed on creating flood maps using user generated content from Twitter. Twitter data has added value over traditional methods such as remote sensing and hydraulic models, since the data is available almost instantly, in contrast to remote sensing and requires less detail than hydraulic models. Deterministic flood maps created using these data showed good performance (F(2)=0.69) for a case study in York (UK). For York the main source of uncertainty in the probabilistic flood maps was found to be the error of the locations derived from the Twitter data. Errors in the elevation data and parameters of the applied algorithm contributed less to flood extent uncertainty. Although the generated probabilistic maps tended to overestimate the actual probability of flooding, they gave a reasonable representation of flood extent uncertainty in the area. This study illustrates that inherently uncertain data from social media can be used to derive information about flooding. [ABSTRACT FROM AUTHOR]

The attractiveness of beaches to people has led, in many places, to the construction of buildings at the beach–dune interface. Buildings change the local airflow patterns which, in turn, alter the sediment transport pathways and magnitudes. This induces erosion and deposition patterns around the structures. In this study, a numerical model is developed using the open-source computational fluid dynamics solver OpenFOAM. First, the model is used to predict the airflow patterns around a single rectangular building. The model predictions are validated with wind-tunnel data, which show good agreements. Second, a reference beach building is introduced and then the building dimensions are increased in length, width and height, each up to three times the reference building dimension. The impact of each dimensional extent on the near-surface airflow patterns is investigated. The results show that the near-surface airflow patterns are least dependent on the length of the building in the wind direction and they depend most on the width of the building perpendicular to the wind direction. Third, the convergence of the third-order horizontal near-surface velocity field is calculated to interpret the impact of changes in airflow patterns on potential erosion and deposition patterns around the building. The numerical predictions are compared with the observed erosion and sedimentation patterns around scale models in the field. The comparisons show satisfactory agreements between numerical results and field measurements. [ABSTRACT FROM AUTHOR]