This paper examines the vulnerability to flooding and erosion of four open beach study sites in Europe. A framework for the quantitative estimation of present and future coastal flood and erosion risks is established using methods, data and tools from across a range of disciplines, including topographic and bathymetric data, climate data from observation, hindcast and model projections, statistical modelling of current and future climates and integrated risk analysis tools. Uncertainties in the estimation of future coastal system dynamics are considered, as are the consequences for the inland systems. Different implementations of the framework are applied to the study sites which have different wave, tidal and surge climate conditions. These sites are: Santander, Spain—the Atlantic Ocean; Bellocchio, Italy—the Adriatic Sea; Varna, Bulgaria—the Black Sea; and the Teign Estuary, UK—the northern Atlantic Ocean. The complexity of each system is first simplified by sub-division into coastal “impact units” defined by homogeneity in the local key forcing parameters: wave, wind, tide, river discharge, run-off, etc. This reduces the simulation to that of a number of simpler linear problems which are treated by applying the first two components of the Source–Pathway–Receptor–Consequence (S–P–R–C) approach. The case studies reveal the flexibility of this approach, which is found useful for the rapid assessment of the risks of flooding and erosion for a range of scenarios and the likely effectiveness of flood defences
This contribution presents a new Artificial Neural Network (ANN) tool that is able to predict the main parameters describing the wave-structure interaction processes: the mean wave overtopping discharge (q), the wave transmission and wave reflection coefficients (Kt and Kr).This ANN tool is trained on an extended database (based on the CLASH database) of physical model tests, including at least one of the three output parameters, for a total number of nearly 18,000 tests. The selected 15 non-dimensional ANN input parameters represent the most significant effects of the structure type (geometry, amour size and roughness) and of the wave attack (wave steepness, breaking, shoaling, wave obliquity). The model can be used for design purposes, leading to a greater accuracy than existing formulae and similar tools for complex geometries for the prediction of Kr and Kt, and it has at least a similar accuracy as the CLASH ANN for predicting q.
New numerical and laboratory investigations on wave overtopping at dikes with crown walls were carried out. The main objective of the experiments, presented for the first time in this contribution, is to investigate the effects of the inclusion of bullnoses on the top of crown walls to reduce the average overtopping discharge q. The study extends the experience available on structures with bullnoses, which is so far limited to dikes with promenades under non-breaking wave conditions. The new data on q resulting from the campaign of experiments are compared with the existing predicting formulae for q of the EurOtop manual (2016), in order to verify and upgrade their range of validity. A formulation for a new correction coefficient γ** to be included in the formulae is proposed to account for the effects of the bullnose also in case of structures subjected to breaking waves. A simple solution to represent the geometry of the bullnoses in the EurOtop Artificial Neural Network (ANN) is investigated. The solution, which avoids the ANN re-training and does not require the inclusion of new input parameters, applied to new and existing data gives promising results.
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