Publications

Coastal hazards such as flooding and erosion can cause large economic and human losses. Under this threat, early warning systems can be very cost-effective solutions for disaster preparation. The goal of this study was to develop, test, and implement an operational coastal erosion early warning system supported by a particular method of machine learning. Thus, the system combines Bayesian Networks, and state-of-the-art numerical models, such as XBeach and SWAN, to predict storm erosion impacts in urbanized areas. This system was developed in two phases. In the development phase, all information required to apply the machine learning method was generated including the definition of hundreds of oceanic synthetic storms, modeling of the erosion caused by these storms, and characterization of the impact levels according to a newly defined eerosion iimpact index. This adimensional index relates the distance from the edge of the dune/beach scarp to buildings and the height of that scarp. Finally, a Bayesian Network that acted as a surrogate of the previously generated information was built. After the training of the network, the conditional probability tables were created. These tables constituted the ground knowledge to make the predictions in the second phase. This methodology was validated (1) by comparing 6-h predictions obtained with the Bayesian Network and with process-based models, the latest considered as the benchmark, and (2) by assessing the predictive skills of the Bayesian Network through the unbiased iterative k-fold cross-validation procedure. Regarding the first comparison, the analysis considered the entire duration of three large storms whose return periods were 10, 16, and 25 years, and it was observed that the Bayesian Network correctly predicted between 64% and 72% of the impacts during the course of the storms, depending on the area analyzed. Importantly, this method was also able to identify when the hazardous conditions disappeared after predicting potential consequences. Regarding the Regarding the second validation approach, second validation approach, the k-fold cross-validation procedure was applied to the peak of a set of varying storms and it demonstrated that the predictive skills were maximized (63%

A wave flume is used to study a scaled model of a stepped bottom dual-chamber Oscillating Water Column (OWC) installed onshore. Compared to traditional single-chamber OWCs, the use of dual-chambers is expected to widen the range of sea states in which the OWC is more efficient, and the step, in front of the first chamber, boosts the capturing energy capacity of the device. The Power Take-Off (PTO) system is modelled as an air opening in the pneumatic chambers. Several experiments combining regular incident waves and PTO damping conditions are performed. Results of the amplitudes of the free surface elevations inside the chambers show that the device can amplify the incident wave amplitude up to 1.57 times in chamber 2. Results of the primary efficiency show that the device is highly dependent on the PTO damping characteristics and can reach around 40% for a wide range of wave periods. Results of the reflection analysis show that the reflection coefficient is also dependent on the PTO damping characteristics but remain lower than 0.6 for all the studied cases. Sloshing phenomenon (mostly in the cross-section direction) was observed for a wavelength close to the cross-section dimension, impacting negatively on the device pneumatic performance.

Performance and hydrodynamics of an array of Oscillating Water Column (OWC) Wave Energy Converter (WEC) integrated into a vertical breakwater is studied. The FLUENT® software, in which the numerical model is based on the Reynolds-Averaged Navier-Stokes equations and the Volume of Fluid method for free surface flow modeling, is used in a 3D numerical wave tank. Three vertical breakwater configurations subject to the action of incident regular waves with periods from 6 to 12 s are studied: normal breakwater, with vertical walls parallel to the direction along the breakwater length; and two novel breakwater geometries, partially and fully convergent breakwaters, whose converging vertical walls are inclined

This study aims to validate a numerical model based on Reynolds-averaged Navier

Storms impacting sandy coastal areas produce hazards such as erosion and overwash that, in turn, promote risk to life and property damage in occupied areas. Coastal damage and risks willincrease in the future not only in association with climate change but also due to the growinghuman occupation in coastal areas (van Dongeren et al., 2018). Since the threatened coastal areas are often densely populated, there is a need to implement measures to prevent risks. One of such measures is the use of Early Warning Systems (EWS) that anticipate expected risks and, therefore, work as management tools to minimize or avoid disaster. This work presents the basis of an EWS prototype to alert about the potential consequences of overwash/overtopping and erosion induced by storms, in coastal areas.

Beaches combined with sloping structures are frequently the frst element of defense to protect urban areas from the impact of extreme coastal fooding events. However, these structures are rarely designed for null wave overtopping discharges, accepting that waves can pass above the crest and threat exposed elements in hinterland areas, such as pedestrians, urban elements and buildings, and vehicles. To reduce risks, Early Warning Systems (EWSs) can be used to anticipate and minimize the impacts of fooding episodes on those elements. A key aspect of these systems is the defnition of non-admissible discharge levels that trigger signifcant impacts. However, large discrepancies in defning these discharge levels and the associated impacts are found among the existing methods to assess foodings. Due to the lack of standardization, a new conceptual and quantitative four-level (from no-impact to high-impact) categorization of food warnings (EW-Coast) is proposed. EW-Coast integrates and unifes previous methods and builds on them by incorporating feld-based information. Thus, the new categorization successfully predicted the impact level on 70%, 82%, and 85% of the overtopping episodes afecting pedestrians, urban elements and buildings, and vehicles, respectively. This demonstrates its suitability to support EWSs in areas vulnerable to wave-induced fooding.

During physical scale model tests of rubble mound breakwaters, the assessment of the eroded volume of the armour layer subjected to incident sea waves can be determined from consecutive surveys of the surface of the armour layer after each test run. This enables one to assess the damage level of the struc ture by comparing erosion profiles and by the eroded volume between consecutive surveys of the tested section. The present study aimed to evaluate the damage evolu tion of a section of the Peniche harbour west breakwater, whose armour layer is made of tetrapods, A dimensionless damage parameter was computed, based on the eroded volume at the end of each test. The test program consisted of three test series (A, B and C) with different durations and wave conditions sequences, considering the low-water level (water depth of 0.20 m at the toe of the structure) and high-water level (0.24 m) and sea states with peak periods Tp = 1.70 s and Tp = 1.98 s and significant wave heights, Hm0, ranging between 0.12 m and 0.19 m. The model was built and operated according to Froude

Melby presents a formula to predict damage evolution in rubble-mound breakwaterswhose armour layer is made of rock, based on the erosion measured in scale-model tests and thecharacteristics of the incident sea waves in such tests. However, this formula is only valid for armourlayers made of rock and for the range of tested sea states. The present work aims to show howthe Melby methodology can be used to establish a similar formula for the armour layer damageevolution in a rubble-mound breakwater where tetrapods are employed. For that, a long-durationtest series is conducted with a 1:50 scale model of the quay section of the Ericeira Harbour breakwater.The eroded volume of the armour layer was measured using a Kinect position sensor. The damageparameter values measured in the experiments are lower than those predicted by the formulationfor rock armour layers. New ap and b coefficients for the Melby formula for the tested armour layerwere established based on the minimum root mean square error between the measured and thepredicted damage. This work shows also that it is possible to assess the damage evolution in scale model tests with rubble-mound breakwaters by computing the eroded volume and subsequently, thedimensionless damage parameter based on the equivalent removed armour units.

The objective of this work is the comparison of two methodologies used for run-up measurements in a two-dimensional physical model, which represented the protection breakwaters of the Peniche and Ericeira

Coastal flooding prediction systems can be an efficient risk-reduction instrument. The goal of this study was to design, build, test, and implement a wave-induced flooding early warning system in urban areas fronted by sandy beaches. The system utilizes a novel approach that combines Bayesian Networks and numerical models (SWAN + XBeach) and was developed in two phases. In the development phase, firstly, the learning information was generated including the creation of oceanic conditions, modeling overtopping discharges, the haracterization of the associated im pacts (no, low, moderate and high) in pedestrians, urban components and buildings, and vehicles, and secondly, the Bayesian Networks were designed that surrogated the previously generated information. After their training, the conditional probability tables were created representing the foundation to make predictions in the operational phase. This methodology was validated for several historical events which hit the study area (Praia de Faro, Portugal), and the system correctly predicted the impact level of around 80% of the cases. Also, the predictive skills varied depending on the level, with the no and high impact levels overcoming the intermediate levels. In terms of efficiency, one simulation (deterministic) of coastal flooding for 72 h by running SWAN + XBeach operationally would take more than two days on a one-logical processor workstation, while the current approach can provide quasi-instantaneously predictions for that period, including probability distributions. Moreover, the two-working phase approach is very flexible enabling the inclusion of additional features such as social components representing a powerful tool for risk reduction in coastal communities.

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