A comparative study of models to predict storm impact on beaches

Santiago, Iñaki de; Morichon, Denis; Abadie, Stéphane; Reniers, A.J.H.M.; Liria, Pedro

doi:10.1007/s11069-017-2830-6

Cited by 19 publications

(7 citation statements)

References 43 publications

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“…Although the use of XBeach in coastal flood analysis is commonly applied in 1D mode (cross‐shore profiles) (e.g., Brown, Prime, & Plater, ; Christie et al, ; de Santiago, Morichon, Abadie, Reniers, & Liria, ; Gallien, ; Harley, Armaroli, & Ciavola, ; Prime et al, ), its use in 2D mode is increasing despite its computational cost (Damlamian, Kruger, Turagabeci, & Kumar, ; Pearson et al, ; Poelhekke et al, ; Schambach, Grilli, Grilli, Hashemi, & King, ). The extensive work of Elsayed and Oumeraci () underlined the effectiveness of XBeach in flood simulation in terms of overland water volumes.…”

Section: Resultsmentioning

confidence: 99%

Multihazard simulation for coastal flood mapping: Bathtub versus numerical modelling in an open estuary, Eastern Canada

David

Baudry

Bernatchez

et al. 2018

J Flood Risk Management

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Funding informationFonds Québécois de la recherche sur la nature et les technologies; Québec Ministry of Public Security Coastlines along the St. Lawrence Estuary and Gulf, Eastern Canada, are under increasing risk of flooding due to sea level rise and sea ice shrinking. Efficient and validated regional-scale coastal flood mapping approaches that include storm surges and waves are hence required to better prepare for the increased hazard. This paper compares and validates two different flood mapping methods: numerical flood simulations using XBeach and bathtub mapping based on total water levels, forced with multihazard scenarios of compound wave and water level events. XBeach is validated with hydrodynamic measurements. Simulations of a historical storm event are performed and validated against observed flood data over ã 25 km long coastline using multiple fit metrics. XBeach and the bathtub method correctly predict flooded areas (66 and 78%, respectively), but the latter overpredicts the flood extent by 36%. XBeach is a slightly more robust flood mapping approach with a fit of 51% against 48% for the bathtub maps. Deeper floodwater by~0.5 m is expected with the bathtub approach, and more buildings are vulnerable to a 100-year flood level. For coastal management at regional-scale, despite similar flood extents with both flood mapping approaches, results suggest that numerical simulati on with XBeach outperforms bathtub flood mapping.

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Section: Resultsmentioning

confidence: 99%

Multihazard simulation for coastal flood mapping: Bathtub versus numerical modelling in an open estuary, Eastern Canada

David

Baudry

Bernatchez

et al. 2018

J Flood Risk Management

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“…Particularly for the area of the Colombian Caribbean coast, works have focused on evaluating the threat of flooding from extreme waves, without considering changes in beach morphology produced by the studied storm (Andrade et al, 2013;Orejarena-Rondón et al, 2019). However, recent studies have examined the impact of both threats simultaneously using the eXtreme Beach Behavior (XBeach) model as a tool (Stockdon et al, 2014;de Santiago et al, 2017;Sanuy and Jiménez, 2019;Enríquez et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The role of morphodynamics in predicting coastal flooding from storms on a dissipative beach with sea level rise conditions

Cueto

Díaz

Ospino-Ortiz

et al. 2022

Nat. Hazards Earth Syst. Sci.

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Abstract. We investigate the role of morphodynamic changes in the flooding of a micro-tidal dissipative beach for both current and sea level rise scenarios. By considering beach morphodynamics and flood processes associated with highly energetic waves, the study allows one to evaluate threats to coastal zones. Coupling of SWAN and XBeach models is employed to propagate offshore wave conditions to the swash zone, estimating morphological changes and flooding associated with wave conditions during cold fronts and hurricanes that affected Cartagena de Indias (Colombia). The numerical models were calibrated from previous research in the study area. The results indicate that numerical modeling of flooding on microtidal dissipative beaches under extreme wave conditions should be approached by considering beach morphodynamics, because ignoring them can underestimate flooding by ∼ 15 %. Moreover, model results suggest that beach erosion and flooding are intensified by sea level rise, resulting in the most unfavorable condition when extreme events are contemporaneous with high tides. In this case, the increase in erosion and flooding is ∼ 69 % and ∼ 65 %, respectively, when compared with the present conditions of sea level.

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“…This scale was originally derived to classify storm impact intensity based on the relation between wave-induced maximum water level and topographic elevations of the different sections of a natural beach. Recently, this approach has been extended to the estimation of storm impact intensity at an engineered beach backed by a seawall [4].…”

Section: Introductionmentioning

confidence: 99%

Automatic Creation of Storm Impact Database Based on Video Monitoring and Convolutional Neural Networks

et al. 2021

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Data about storm impacts are essential for the disaster risk reduction process, but unlike data about storm characteristics, they are not routinely collected. In this paper, we demonstrate the high potential of convolutional neural networks to automatically constitute storm impact database using timestacks images provided by coastal video monitoring stations. Several convolutional neural network architectures and methods to deal with class imbalance were tested on two sites (Biarritz and Zarautz) to find the best practices for this classification task. This study shows that convolutional neural networks are well adapted for the classification of timestacks images into storm impact regimes. Overall, the most complex and deepest architectures yield better results. Indeed, the best performances are obtained with the VGG16 architecture for both sites with F-scores of 0.866 for Biarritz and 0.858 for Zarautz. For the class imbalance problem, the method of oversampling shows best classification accuracy with F-scores on average 30% higher than the ones obtained with cost sensitive learning. The transferability of the learning method between sites is also investigated and shows conclusive results. This study highlights the high potential of convolutional neural networks to enhance the value of coastal video monitoring data that are routinely recorded on many coastal sites. Furthermore, it shows that this type of deep neural network can significantly contribute to the setting up of risk databases necessary for the determination of storm risk indicators and, more broadly, for the optimization of risk-mitigation measures.

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A comparative study of models to predict storm impact on beaches

Cited by 19 publications

References 43 publications

Multihazard simulation for coastal flood mapping: Bathtub versus numerical modelling in an open estuary, Eastern Canada

Multihazard simulation for coastal flood mapping: Bathtub versus numerical modelling in an open estuary, Eastern Canada

The role of morphodynamics in predicting coastal flooding from storms on a dissipative beach with sea level rise conditions

Automatic Creation of Storm Impact Database Based on Video Monitoring and Convolutional Neural Networks

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