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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]

Understanding the dynamics of beach–dune systems is crucial for effective coastal management. The cellular automata model DuBeVeg provides a powerful tool for simulating and understanding the bio-geomorphological evolution of these systems, capturing key interactions of aeolian, hydro-, and vegetation dynamics in a simplified manner. In this study, we present an alternative representation of the aeolian transport component in DuBeVeg, aiming to better capture the saltation transport mode that prevails on beaches. This new representation is compared with the original aeolian transport representation in DuBeVeg, which is inspired by ripple migration. For three beach width scenarios, we considered the effects of the different aeolian transport representations on the predicted foredune morphology after 50 years, as well as the spatio-temporal evolution of the beach–dune system leading to that morphologic state. The saltation transport representation resulted in a more realistic simulation of the seaward expansion of the foredune compared with the original representation, particularly in scenarios with wide and prograding beaches. The new representation also more accurately captured the amplitude of aeolian bedforms emerging across the beach. These findings highlight the importance of selecting the representative transport mode when simulating the transient bio-geomorphological evolution of beach–dune systems. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Growth of coastal dunes requires a marine supply of sediment. Processes that control the sediment transfer between the subtidal and the supratidal zone are not fully understood, especially in sand flats close to inlets. It is hypothesised that storm surge events induce sediment deposition on sand flats, providing fresh material for aeolian transport and dune growth. The objective of this study is to identify which processes cause deposition on the sand flat during storm surge conditions and discuss the relationship between the supratidal deposition and sediment supply to the dunes. We use the island of Texel (NL) as a case study, of which multiannual topographic and hydrographic datasets are available. Additionally, we use the numerical model XBeach to simulate the most frequent storm surge events for the area. Results show that supratidal shore-parallel deposition of sand occurs in both the numerical model and the topographic data. The amount of sand deposited is directly proportional to surge level and can account for more than a quarter of the volume deposited at the dunes yearly. Furthermore, storm surges are also capable of remobilising the top layer of sediment of the sand flat, making fresh sediment available for aeolian transport. Therefore, in a sand flat setting, storm surges have the potential of reworking significant amounts of sand for aeolian transport in periods after the storm and as such can also play a constructive role in coastal dune development. [ABSTRACT FROM AUTHOR]

The increasing number and severity of floods, driven by phenomena such as urbanization, deforestation, subsidence and climate change, create a growing need for accurate and timely flood maps. In this paper we present and evaluate a method to create deterministic and probabilistic flood maps from Twitter messages that mention locations of flooding. A deterministic flood map created for the December 2015 flood in the city of York (UK) showed good performance (F(2) = 0.69; a statistic ranging from 0 to 1, with 1 expressing a perfect fit with validation data). The probabilistic flood maps we created showed that, in the York case study, the uncertainty in flood extent was mainly induced by errors in the precise locations of flood observations as derived from Twitter data. Errors in the terrain elevation data or in the parameters of the applied algorithm contributed less to flood extent uncertainty. Although these 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 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]

1. Assessing and monitoring the impact of offshore wind farms on marine fauna is vital if we want to achieve ecologically sustainable development of this renewable energy resource. Given the complexity of the marine environment, a method capable of accommodating spatio-temporal behaviour of specific species and their interrelation with other marine phenomena is an essential prerequisite for investigating whether or not there has been any measurable impact to date. 2. This paper presents a method based on geostatistical simulation to assess whether pre- and post-construction collected bird count data suggest displacement of birds due to the wind farm. The method takes into account spatial autocorrelation in species abundance at various scales, pre- and post-construction differences in environmental conditions and in survey effort and design. 3. We demonstrate that taking these factors into account influences the conclusions about a wind farm’s impact on bird life. In particular, incorporating spatial autocorrelation in seabird numbers is an important factor in reducing the risk of wrongly identifying an effect of a wind farm on bird abundance. 4. Synthesis and applications. The development of offshore wind farms is often in conflict with nature conservation interests. Environmental impact assessment and monitoring is essential to protect and manage the marine environment. The method described here will allow scarce data to be utilized effectively as a basis for well-informed environmental decisions. In addition, the method will assist in the design of optimal monitoring procedures at a given site, balancing costs and effectiveness in detecting potentially harmful impacts. [ABSTRACT FROM AUTHOR]

A number of techniques for non-linear analysis of time series data have been developed in recent years and applied in many environmental sciences. In this paper, some of these techniques are reviewed and their usefulness for coastal morphological data assessed, with examples in coastal morphology and related fields where these are available. The methods reviewed are time-delay embedding techniques, singular spectrum analysis, forecasting signatures, fractal analysis and neural networks. It is expected that readers from diverse backgrounds such as statistics, environmental modeling, and data measurement would be interested in this subject. Accordingly, introductions have been provided for some concepts and background material. These include the general purposes of data analysis, the approaches traditionally used by statisticians and physical scientists, the general nature of coastal morphological data, and the distinction between linear and non-linear analysis methods. It is concluded that the information potentially provided by these techniques is essential in understanding the generic behavior of coastal morphology on yearly and decadal timescales, and hence in constructing models and making forecasts of future morphological evolution. However, for these purposes, non-linear data analysis techniques usually require longer data series, and higher spatial and temporal resolution, than is available in present coastal morphological data sets. Data from remote sensing sources have the potential to meet these requirements. [ABSTRACT FROM AUTHOR]

A number of statistical techniques to analyze and model coastal morphological evolution over yearly and decadal (i.e., long-term) time scales based on field data are presented. After a general introduction to long-term morphological modeling, mainly linear methods are discussed, whereas nonlinear methods are treated in a companion paper (SOUTHGATE et al., 2001). The theoretical background to the methods introduced is summarized and examples of field applications are given to illustrate each method. High-quality field data sets from different sites in the world, including Germany, The Netherlands, and United States, were employed in these examples. The analysis and modeling techniques used encompassed bulk statistics (mean, standard deviation, correlation etc), random sine functions, empirical orthogonal functions, canonical correlation analysis, and principal oscillation pattern analysis. Besides an evaluation of how suitable respective technique is for analyzing and modeling long-term morphological evolution, some general observations are presented regarding scales of morphological response as derived from the field applications. Data describing the evolution of both natural and anthropogenically affected coastal systems were studied. All methods investigated proved their usefulness for extracting characteristics of long-term morphological evolution, as well as for modeling this evolution, when applied under the right circumstances. However, more sophisticated techniques rely on more data in time and space, which is typically the limiting factor in the application of statistical methods as those presented here. [ABSTRACT FROM AUTHOR]

Seven different bar types are distinguished to provide a framework for comparing morphodynamic studies conducted in different areas. Five types occur in semiprotected or open coast settings, of which two are intertidal and three are subtidal. Two types occur in highly protected settings. The occurrence of a certain bar type is generally determined by the wave energy and tidal range, although the nearshore slope may also be a differentiating boundary condition. The theory behind the generation, evolution and decay of bars has evolved most for the subtidal bars in the semiprotected and open coast settings, for which three types of competing mechanisms have been formulated (breakpoint, infragravity waves, self-organisational). Most research has focused on these processes on the time scale of storm events and post-storm recovery. However, to understand the longer-term behavior of bar systems, knowledge of the role of relaxation time and morphologic feedback is needed as well. At present, such knowledge is very limited. We think it can best be obtained from the analysis of long time series of morphology and forcing conditions, rather than from intensive field experiments. In case of a feedback-dominated response (self-organisational), we expect to find no correlation between the time series of external forcing and the morphologic response. In case of a relaxation time-dominated response, we do expect to find such a correlation, albeit filtered. This discussion is illustrated by a case study of the Dutch coast. [Copyright &y& Elsevier]

Inlet-driven processes are capable of modifying the adjacent shoreline. However, few studies have attempted to understand how these changes affect coastal dunes. The present study aims to understand how shoreline changes induced by shoal attachment affect coastal dunes. A barrier island in the Netherlands is used as a case study. Both bathymetric and topographic annual data were analysed, together with the application of a cellular automata model for dune development. The objective of the model is to explore idealised scenarios of inlet-driven shoreline movements. With the model, ten different scenarios were examined regarding beach width increase and rate of alongshore spreading of the shoal. Field data showed that, for the case study, dune volume and shoal attachments could not be directly linked. Instead, rates of dune volume change differed significantly only due to long-term ebb-tidal delta evolution. Such morphological evolution oriented the beach towards the main wind direction, increasing overall aeolian transport potential. Modelling results showed that shoals significantly increased dune volumes only on three out of ten scenarios. This suggests that beach width increase, and rate of alongshore sediment spreading, determine whether the shoal will influence dune growth. Therefore, within the studied time-scale, local rates of dune growth are only increased if shoals are capable of increasing the beach width significantly and persistently. [ABSTRACT FROM AUTHOR]

The composition of benthic species communities in the nearshore zone is closely related to the hydrodynamic and morphodynamic conditions. Sustainable management of the coastal ecosystem requires knowledge about the natural dynamics as well as human-induced changes on the ecosystem. To improve our knowledge of the benthic species distribution along a dissipative sandy shore with multiple breaker bars, an extensive dataset was collected in the nearshore zone of the barrier islands Ameland and Schiermonnikoog in the Dutch North Sea. From 2010 to 2014, every year, approximately 180 grab samples along 18 cross-shore transects were collected and analyzed for sediment characteristics and macrobenthic species composition. Mixed-effect-models and partial redundancy analysis were used to analyze the importance of morphological features (i.e., slopes, bar crests, and troughs) as an explanatory variable for the benthic species distribution. The results indicate that the morphological features in themselves explain three times more variation than the environmental parameters used. This demonstrates the importance of morphological features as a factor in explaining the distribution of benthic species communities in the nearshore. Detailed information on morphological features is easy to obtain from bathymetry maps or visual inspection. Incorporating morphological features in species distribution models will therefore help to improve sustainable management of our valuable sandy coastal systems. [ABSTRACT FROM AUTHOR]

In this paper, we explore an approach for annual-scale transport prediction from the intertidal beach, in which we aggregate the surface conditions of the intertidal beach, in particular moisture content and roughness, and use hourly monitoring data of wind speed and wind direction. For our case study area (Egmond Beach, The Netherlands), we include Argus video imagery in our analysis to assess the occurrence of aeolian sand transport. With the approach described to determine a characteristic moisture content value for aeolian transport, we obtained surface moisture values of 1.2% to 3.2% for wind average and wind gust respectively, implying that we need a quite dry beach. This indicates that the main area for aeolian transport corresponds to the upper part of the intertidal source, most likely the region between mean high tide line and spring high tide line. [ABSTRACT FROM AUTHOR]