Predicting coastal hazards for sandy coasts with a Bayesian Network

Poelhekke, L.; Jäger, W.; Dongeren, Ap van; Plomaritis, Theocharis A.; McCall, Robert; Ferreira, Óscar

doi:10.1016/j.coastaleng.2016.08.011

Cited by 88 publications

(102 citation statements)

References 35 publications

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“…Probable combinations that cannot be covered using existing records are represented by 5 synthetic designed storms (e.g. Poelhekke et al, 2016;Plomaritis et al, 2017;Jäger et al, 2017). Storm scenarios are defined as a combination of the involved variables within the BN.…”

Section: Source: Identification and Designmentioning

confidence: 99%

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Linking source with consequences of coastal storm impacts for climate change and risk reduction scenarios for Mediterranean sandy beaches

Sanuy¹,

Duo²,

Jäger³

et al. 2017

Preprint

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Abstract. Integrated risk assessment approaches to support coastal managers' decisions when designing plans are 10 increasingly becoming an urgent need. To enable efficient coastal management, possible present and future scenarios must be included, disaster risk reduction (DRR) measures integrated, and multiple hazards dealt with. In this work, the Bayesian Network approach to coastal risk assessment was applied and tested at two Mediterranean sandy coasts (Tordera Delta in Spain and Lido degli Estensi-Spina in Italy). Process-oriented models are used to predict hazards at the receptor scale based on a large number of storm characteristics. Hazards are converted into impacts through vulnerability relations. A Bayesian 15Network integrates all results to link forcing characteristics with expected impacts through conditional probabilities. The tool has been proven successful in reproducing current coastal responses at both sites. It has also shown great utility for scenario comparisons, and is able to output significant impact change trends, despite the inherent uncertainties of the approach. This work highlights the advantages of using such a tool for present and future coastal risk assessment and planning. 20

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Section: Source: Identification and Designmentioning

confidence: 99%

“…Using a BN approach, multiple multi-hazard results from process-oriented models can be integrated for joint assessment, as well as for different scenarios and alternatives (e.g. Gutierrez et al, 2011;Poelhekke et al, 2016), enabling the integration of socio-economic concepts (e.g. Van Verseveld et al, 2015).…”

mentioning

confidence: 99%

Linking source with consequences of coastal storm impacts for climate change and risk reduction scenarios for Mediterranean sandy beaches

Sanuy¹,

Duo²,

Jäger³

et al. 2017

Preprint

Self Cite

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“…In the bivariate case, many different copula families have proven to be useful (e.g., Salvadori et al 2014;Masina et al 2015, and references therein). For more than two oceanographic variables, nested (also called hierarchical) Archimedean copulas (Wahl et al 2012;Corbella and Stretch 2013;Lin-Ye et al 2016) and elliptical copulas, such as Gaussian or t, (Li et al 2014a;Wahl et al 2016;Rueda et al 2016) have been implemented and found valuable, but also dependence trees (Poelhekke et al 2016) and vines (De Michele et al 2007;Montes-Iturrizaga and Heredia-Zavoni 2016), which are a generalization thereof, have been proposed. Callaghan et al (2008) and Serafin and Ruggiero (2014) adopt two other approaches to model dependencies.…”

Section: A Vine-copula Model For Time Series Of Significant Wave Heigmentioning

confidence: 99%

“…1) whose size is determined by a peak value and a critical threshold value of, for example, wave height or water level together with the storm duration (e.g., Boccotti 2000). For instance, Corbella and Stretch (2012) and Poelhekke et al (2016) have used these triangles and the dependencies between variables at the peak to derive idealized storm time series with high resolution (~1h) to force numerical, physics-based models that compute resulting erosion and flooding. Differently, Wahl et al (2011) apply linear regression to parameterize and simulate the temporal evolution of total water levels during storm surges.…”

Section: A Vine-copula Model For Time Series Of Significant Wave Heigmentioning

confidence: 99%

A Vine-Copula Model for Time Series of Significant Wave Heights and Mean Zero-Crossing Periods in the North Sea

Jaeger

Morales-Nápoles

2017

ASCE-ASME J. Risk Uncertainty Eng. Syst., Part A: Civ. Eng.

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Stochastic description and simulation of oceanographic variables are essential for coastal and marine engineering applications. In the past decade, copula-based approaches have become increasingly popular to estimate the multivariate distribution of some variables at the peak of a storm along with its duration. The modeling of the storm shape, which contributes to its impact, is often simplified. This article proposes a vine-copula approach to characterize hourly significant wave heights and corresponding mean zero-crossing periods as a random process in time. The model is applied to a data set in the North Sea and time series with the duration of an oceanographic winter are simulated. The synthetic wave scenarios emulate storms as well as daily conditions. The results are for example useful as input for coastal risk analyses and for planning offshore operations. Nonetheless, selecting a vine structure, finding appropriate copula families and estimating parameters is not straightforward. The validity of the model, as well as the conclusions that can be drawn from it, are sensitive to these choices. A valuable by-product of the proposed vine-copula approach is the bivariate distribution of significant wave heights and corresponding mean zero-crossing periods at the given location. Its dependence structure is approximated by the flexible skew-t copula family and preserves the limiting wave steepness condition. INTRODUCTIONWind-induced sea waves affect coastlines, marine structures and offshore operations. Unfavorable conditions can cause significant morphological change, damages or downtime. Intuitively, the higher a wave, the more energy it carries and the more destructive

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“…Van Dongeren et al 2017). These include the use of Bayesian Network analysis to describe the interaction of multiple hazards with the socio-economic system (Poelhekke et al 2016;Plomaritis et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Coastal hazard risk assessment for small islands: assessing the impact of climate change and disaster reduction measures on Ebeye (Marshall Islands)

2018

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Small island states around the world are among the areas most vulnerable to climate change and sea level rise. In this paper, we present results from an innovative methodology for a quantitative assessment of multiple hazards on coastal risks, driven by different hydro-meteorological events, and including the effects of climate change. Moreover, we take an additional step by including in the methodology the option to assess and compare the effectiveness of possible disaster risk reduction measures. The methodology is applied to a real case study at the island of Ebeye (the Republic of the Marshall Islands). An example is provided in which a rock revetment is implemented as a risk reduction measure for the island. Results show that yearly expected damages may increase, by the end of the century, by a factor of three to four, depending on the sea level rise scenario considered, while the number of yearly affected people may double. Putting a cap on the temperature increase (e.g. 1.5 vs. 2°C) according to the Paris Agreement may reduce damages and number of affected people by about 20 and 15%, respectively. However, impacts for same warming levels can vary substantially among different emission scenarios. Disaster risk reduction measures can be useful for mitigating risks in current and future situations but should be incorporated within long-term adaptive planning for these islands.

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Predicting coastal hazards for sandy coasts with a Bayesian Network

Cited by 88 publications

References 35 publications

Linking source with consequences of coastal storm impacts for climate change and risk reduction scenarios for Mediterranean sandy beaches

Linking source with consequences of coastal storm impacts for climate change and risk reduction scenarios for Mediterranean sandy beaches

A Vine-Copula Model for Time Series of Significant Wave Heights and Mean Zero-Crossing Periods in the North Sea

Coastal hazard risk assessment for small islands: assessing the impact of climate change and disaster reduction measures on Ebeye (Marshall Islands)

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