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- Biotic and abiotic factors governing dune response to storm eventsPublication . Garzon, Juan L.; Costas, Susana; Ferreira, OscarThe alongshore response of dunes to storm events can be extremely variable and,consequently, their capacity to maintain their services, including the protection ofhinterland communities. In this study, the role of biotic and abiotic factors determin-ing the magnitude of dune retreat driven by a severe storm along a 60 km barrierisland system was investigated. Data from high-resolution satellite imagery, digitalterrain models, and wave propagation models were used in this assessment. Theassessed abiotic factors included the backshore volume, dune height, downdrift inletdistance, and incident wave power. The evaluated biotic factor was the vegetationcover, characterized by a vegetation index retrieved from the multispectral imagery.The results revealed large alongshore variability on dune retreat, ranging from negli-gible impact to ca. 40 m of retreat. All combined factors allowed us to explain up to70% of the dune retreat variability through a multi-regression analysis. Among allinvestigated factors, the major contributor controlling the magnitude of dune retreatwas the backshore volume (more robust berms reduced the retreat) followed by thewave power (normal and longitudinal components). Moreover, the removal of localsalient features in the dune line caused the straightening of the coastline, highly con-tributing to the development of dune retreat hotspots. The other evaluated factorshad a smaller influence on reducing coastal retreat, including the vegetation, whosecontribution to dune protection was around one order of magnitude lower than thatprovided by the backshore volume. The results highlight the importance of regionalassessments to understand the causes behind the large alongshore variability ofstorm impacts at dunes. They also state the relatively low influence of the vegetationfrom this climatic region to enhance dune resistance to storms.
- Uncertainty analysis related to beach morphology and storm duration for more reliable early warning systems for coastal hazardsPublication . Garzon, Juan L.; Plomaritis, T. A.; Ferreira, OscarEarly warning systems (EWSs) for coastal erosion are highly cost-effective instruments for disaster risk reduction. Among other aspects, an adequate pre-storm beach morphology and the storm characteristic definition are relevant in determining EWSs prediction reliability. Here, XBeach simulations were used to investigate the beach-dune response to different storm events with varying duration and pre-storm morphologies. Severity was defined using wave height return periods (from 5 to 50 years) and duration variability was established by confidence intervals after an adjustment with wave height. Beach morphology variability included different berm morphologies, including erosional and accretional conditions. Three erosion indicators were used: remaining berm width, dune retreat, and eroded volume. Regarding the pre-storm morphology variability: i) pre-storm conditions highly determined the final berm width for the 5 and 10-year events; ii) antecedent morphology affected dune retreat variability mostly for the 50 year events, and; iii) eroded volume depended on the pre-storm conditions, but the percentage of the eroded volume, relative to the initial conditions, was similar regardless of the morphology. Regarding the storm duration effect: i) this variable had a limited impact on the remaining berm width for the 5-year event; ii) storm duration influenced dune retreat mainly for the 50-year event, determining dune breaching occurrence, and; iii) eroded volume response to changes in duration was similar regardless of storm intensity, except for the 50-year event. According to the obtained results, the implementation of reliable EWSs for coastal erosion needs to assess the uncertainties related to initial/forcing conditions, namely pre-storm morphology and storm duration.
- Modeling of coastal erosion in exposed and groin-protected steep beachesPublication . Garzon, Juan L.; Ferreira, Oscar; Plomaritis, Theocharis A.Process-based models are suitable tools for reproducing storm-driven erosion. However, their performance has been mainly examined on mild-slope sandy beaches and their use on steep beaches still represents a challenge. Here, open-source process-based model XBeach experiments were combined with topographical measurements collected for two storms (16- and 5-year return period) to obtain a reliable model. The model parameters “facua” (parameterized wave asymmetry and skewness sediment transport component), “bermslope” (upslope transport term for semireflective beaches), and “wetslope” (critical avalanching submerged slope) were utilized for calibration and validation. The 16-year storm simulations on an exposed beach revealed that whether bermslope increased and “facua” must be reduced, and vice versa, to properly simulate erosion. Adding bermslope provided excellent results for these storms when using facua and wetslope values close to the recommended values. In a groin-protected site, XBeach was successfully calibrated and validated for the tested storms using these parameters, although with different values. These experiments demonstrated that the appropriate use of these parameters can satisfactorily simulate morphological changes on steep beaches for different hydrodynamic conditions and coastal settings (exposed and groin protected).
- Early Warning System development: Quarteira and Praia de FaroPublication . Garzon, Juan L.; Zozimo, Catarina; Ferreira, Andreia M. Marques; Ferreira, Oscar; Fortes, Conceição Juana; Reis, Maria TeresaMany coastal zones worldwide are heavily populated and host very important socio-economic sectors. Portugal is a good example of countries whose economy is highly dependent on tourism activities, especially those sea-related activities. The two sites selected in this project (Quarteira and Faro) receive thousands of national and international visitors annually, not only during the summertime but also in the rest of the seasons because of the favorable weather conditions. However, these sites have been acknowledged as coastal risk hotspots due to their exposure to wave-induced flooding and erosion. Under this threat, the implementation of effective disaster risk reduction (DRR) plans is vital for minimizing damages in occupied areas. In this regard, Early Warning Systems (EWSs) play an important role in allowing for preparedness, namely, timely site evacuation or effective intervention prior to the approaching storm. The successful implementation of EWSs is one of the most cost-effective and efficient measures for disaster risk reduction and the saving of lives. EWSs can rely on complex tools such as process-based models to simulate coastal hazards namely erosion and flooding. However, they are normally highly time-consuming and this aspect might represent a major limitation for operational systems. Conversely, Bayesian Networks (BNs) can provide risk probabilities instantly after being trained and they have been successfully used to make predictions of storm impacts in several coastal applications. The main disadvantage of Bayesian Networks is that they are data-intensive, requiring large input information in order to derive the probabilistic relationships used in their predictions. Under the lack of field observations, process-based models can be used to generate this required information. Once trained, the BN can be used as a surrogate for a process-based model in an EWS.
- Modelling wave attenuation by saltmarsh using satellite-derived vegetation propertiesPublication . Figueroa-Alfaro, Richard W.; Van Rooijen, Arnold; Garzon, Juan L.; Evans, Martin; Harris, AngelaSaltmarshes are increasingly recognised an important asset in coastal management as they dissipate wave energy and thus reduce the potential for coastal flooding. The frontal surface area (FSA) and the drag coefficient (C-d) are parameters commonly used in wave attenuation models to express the resistance of vegetation structure to incident waves. The FSA of vegetation represents the vertical surface area facing incoming waves which is calculated as the product of height, diameter and density whereas C-d is often used as tunable parameter that represents the vegetation-wave interactions that relies on both vegetation properties and wave conditions. Despite their importance in numerical modelling, substantial uncertainty remains in obtaining these parameters in the field due to the time-intensive and relatively expensive nature of data collection. An alternative structural vegetation parameter that can be included in wave attenuation models is the leaf area index (LAI). The primary advantage of the LAI is that it can be readily derived from satellite imagery, and thus provides a low-cost, fast alternative to field data collection. However, to date, its incorporation in widely-used coastal engineering models is lacking. The aim of this paper is to verify the use of remote-sensed LAI in numerical wave models as an alternative to FSA. Here, the widely used XBeach model for simulating storm impacts on a range of coastal systems is applied to two open coast sites with extensive saltmarsh; Chesapeake Bay, USA, and Brancaster, UK. To assess the performance of wave attenuation modelling using both methods, we compared the use of remote-sensed LAI from satellite imagery and field-based FSA as inputs into the model. The LAI-based model provides similar levels of accuracy as the FSA-based model. Likewise, higher uncertainties related to plant height, diameter, and density were found in the FSA-based model than in the LAI-based model. Therefore, the LAI-based model provides the advantage of a low-cost and fast method to accurately estimate and predict wave attenuation by vegetation using numerical models such as XBeach. Our practical application in the Brancaster site exemplifies an easy and fast approach to obtaining structural parameters of saltmarsh vegetation and estimating wave attenuation between natural and artificial saltmarshes as well as between seasons.
- Development of a Bayesian network-based early warning system for storm-driven coastal erosionPublication . L. Garzon, Juan; Ferreira, Óscar; Plomaritis, T. A.; Zózimo, A. C.; Fortes, C. J. E. M.; Pinheiro, L. V.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%-72%) when including three nodes as input conditions of the Bayesian Network. In the operational phase, the system was integrated into the architecture of a forecast and early warning system that predicts emergencies in coastal and port zones in Portugal, and the alerts are issued to authorities every day. This study demonstrated that the two-phase approach developed here can provide fast and high-accuracy predictions of erosion impacts. Also, this methodology can be easily implemented on other sandy beaches constituting a powerful tool for disaster management.
- Development of a Bayesian networks-based early warning system for wave-induced floodingPublication . Garzon, Juan L.; Ferreira, Óscar; Zózimo, A. C.; Fortes, C. J. E. M.; Ferreira, A. M.; Pinheiro, L. V.; Reis, M. T.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 characterization of the associated impacts (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.
- Conceptual and quantitative categorization of wave-induced flooding impacts for pedestrians and assets in urban beachesPublication . Garzon, Juan L.; Ferreira, Ó.; Reis, M. T.; Ferreira, A.; Fortes, C. J. E. M.; Zózimo, A. C.Coastal fooding is a major threat to communities living in low-lying areas and the increase in the anthropogenic pressure in coastal zones and the efects of climate change (e.g., sea-level rise, increase in storminess and its frequency) are promoting an enhancement of the existing risks for population and properties 1–4 . Coastal fooding results from the interaction of oceanic and atmospheric processes with the local and regional features (topography, nearshore bathymetry, continental shelf, and land use). Among the diferent oceanic agents that might drive coastal fooding, wave-related processes have been found to be the dominant component in large areas of the globe compared to storm surges and tides 5 . When waves approach the shoreline, a large part of the wave energy is dissipated across the surf zone by wave breaking. However, a portion of the remaining energy is converted to potential energy in the form of wave runup on the beach foreshore 6 contributing to boosting the extreme water levels 3 . When the existing natural or man-made coastal protection structure (constructed on land) is lower than the maximum level that water can reach by wave attack, a discharge occurs over the structure and propagates inland. It can be called green water 7 (non-impulsive), when a layer of water passes over the crest, or white water 7 (or impulsive conditions) when waves break on the seaward face of the structure and produce signifcant volumes of splash or spray (not considered here). Terefore, wave runup (and overtopping) is important to coastal planners and engineers because it delivers much of the energy responsible for causing a fooding event 8. Besides disruptions in local services and transportation, during such events, seawater can travel with high velocities, which in turn can afect the integrity of urban elements and properties, and severely injure people.
- Application of SWASH to Compute Wave Overtopping in Ericeira Harbour for operational purposesPublication . Manz, Anika; Zózimo, Ana Catarina; Garzon, Juan L.This work aimed at testing the capability of the numerical model SWASH to be implemented in the prototype of the overtopping and flooding forecast system HIDRALERTA for Ericeira harbour. In contrast to the neural network NN_OVERTOPPING2, which is currently implemented in HIDRALERTA, SWASH is able to estimate the flood extension and wave propagation along the domain, which makes it a possible improvement to NN_OVERTOPPING2. The one-dimensional version of the SWASH model was implemented to simulate overtopping at two different profiles (antifer and tetrapods) and calibrated for three storms in 2019 by comparing the simulated overtopping discharge to NN_OVERTOPPING2 results. For the calibration, the Manning coefficient was used to represent the friction of the armour layer. Then, for operational purposes, four expressions to calculate the Manning coefficient were developed based on: the relative crest freeboard, the wave steepness, the incident wave angle and the type of armour layer. The expressions showed small errors between the calculated and calibrated Manning coefficients and highlighted the importance of the incident wave angle to obtain an accurate calibration. Despite an underestimation of the overtopping discharge in some cases, the SWASH model was found to provide overall good results when applied with calculated Manning coefficients and suitable to be implemented in HIDRALERTA.