Predictive modelling and coastal storm responsea study of urban and natural coastal systems
The increase in frequency and intensity of storm events combined with the growth of population in the coastal zones are exacerbating risk in coastal areas . Proper mitigation plans are necessary to reduce the impact of extreme events on those areas. In many cases, the development of those plans is based on early warning systems, a combination of pre-established extreme values and numerical models to forecast the storm's possible impact. However, the local characteristics must be considered when establishing the extreme values and setting the numerical models to achieve reliable predictions. This PhD thesis presents the study of the application of numerical modelling to predict coastal storm behaviour in two sites with opposite characteristics: Urban and Natural systems. The chosen highly urbanised beach was the beach of Volano in the Emilia-Romagna region in Italy, while the considered natural coastal system was the Arborea Beach in Sardinia (Italy); the last one is characterised by the presence of seagrass Posidonia oceanica deposition on the foreshore, known as "banquette". The thesis comprehensively evaluates these models' role in predicting and mitigating coastal hazards by investigating the two different environments. The first topic addressed in this thesis is the flooding risk in urban areas, with the specific case study in the Emilia-Romagna region. The coastal area of this Italian region, located on the northern Adriatic coast, is heavily urbanised due to the presence of resorts, roads and coastal defences. The high infrastructure in a low-lying dune area makes the region susceptible to extreme events, being impacted at least once yearly. When defining the warning levels in a forecasted event, a common practice is establishing different thresholds according to the event's intensity. One standard value is the Total Water Level (TWL). However, the definition of TWL and how the extreme values are calculated change from country to country or even region to region. This can go from including different components to calculating the TWL components such as sea level, tidal components, wave setup, and storm surge or calculating the extreme value by combining individual extreme values or extracting directly by a combined time series. The different methodologies for calculating the extreme TWL were applied to the coastal area of Volano and then applied to the numerical model LISFLOOD-FP to observe their impact. The results of this section highlight the sensitivity of the numerical model to the different inputs, such as the inclusion of the wave runup in the TWL. To define the best TWL definition and method to calculate the extreme value, a storm that occurred on this coast on 22 November 2022 was used as a real-case scenario. Compared to the event, the best combination and method found was combining the individual components' time series into a common time series and the respective extracted values. The second topic of the thesis changes the focus to a natural coastal system, examining the role of Posidonia oceanica banquettes in protecting beaches from storm-induced erosion. Arborea Beach, located on the island of Sardinia, was selected due to the undisturbed natural environment and the frequent presence of Posidonia oceanica banquettes on the beach. Posidonia oceanica is banquettes, and their meadows are important in the beach's wave action effects. Over the years (2022-2024), extensive fieldwork was done covering the topography and bathymetry of Arborea Beach, and this was also combined with historical data collected from aerial data. The study demonstrated that banquettes can act as natural buffers of wave action, preventing erosion. The study also compared different sections of the beach, revealing that areas with dense Posidonia banquettes experienced significantly less erosion when compared with areas with fewer banquettes. This demonstrated the importance of banquettes in maintaining the beach's stability, although not entirely sufficient for nullifying erosion. The overall trend of the study area is eroding, suggesting a possible lack of sediment supply. The third Chapter focuses on using the fieldwork's high-quality data to explore the limitations of the current numerical modes in simulating the formation and protective characteristics of Posidonia oceanica banquettes. A widely used morphohydrodynamic model, XBeach, was used to simulate the impact of a storm on the Arborea beach. Two scenarios were tested: i) one where the beach morphology remained static, analysing the runup extension, and ii) another where the capability to simulate the development of banquettes on the beach was tested. The results of the simulation were compared with the fieldwork observations. While the XBeach accurately simulated the hydrodynamics during the event on a static morphology, it struggled to simulate the development of Posidonia banquettes. The model's inability to simulate the organic structure highlighted a current limitation of the current numerical models. This emphasises that the numerical models need improvements to include characteristics crucial to coastal protection. Future advancements in coastal modelling should incorporate better representation of those characteristics. Overall, the thesis highlights the need to adopt a mitigation plan according to the local areas; where the urban area is more focused on flood propagation, the natural area's adaptation requires a good morphological prediction. The combination of the prediction of both sectors would show the best results overall. However, this would require constant monitoring of the areas of interest. The constant monitoring of the beach and the proper calibration will allow the coastal managers to improve the coastal storm predictions and, therefore, anticipate and mitigate the impacts of extreme. In future research, the thesis recommends expanding the field of coastal modelling to include more comprehensive simulations of the peculiar characteristics of specific environments. Furthermore, continued improvement in data collection methods, such as the use of high-resolution satellite imagery and advanced monitoring systems, will be essential to enhance the predictive accuracy of these models.
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