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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]

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]

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]