Modelling multi-hazard hurricane damages on an urbanized coast with a Bayesian Network approach

Verseveld, H.C.W. Van; Dongeren, Ap van; Plant, Nathaniel G.; Jäger, W.; Heijer, Kees den

doi:10.1016/j.coastaleng.2015.05.006

Cited by 57 publications

(47 citation statements)

References 21 publications

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“…Furthermore, if building characteristics are known, damage to structures could also be estimated using simple stage‐damage relationships or more sophisticated approaches where sufficient data is available. van Verseveld et al () and Jäger et al () have used Bayesian networks to predict direct economic damage to houses and infrastructure resulting from surge and wave‐induced flooding on sandy, urbanized coastlines.…”

Section: Discussionmentioning

confidence: 99%

“…The LLR is an indicator of predictive skill and model uncertainty that compares the prior predictions of a network with the posterior predictions made using additional information (Plant & Holland, ). The concept is explained in more detail by, van Verseveld et al (), Gutierrez et al (), and Poelhekke et al (). When the LLR is calculated for key parameters, it makes it possible to consider which parameters should be included in the Bayesian network, which parameter uncertainty should be constrained, and, thus, which field measurements are most important to collect.…”

Section: Methodsmentioning

confidence: 99%

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A Bayesian‐Based System to Assess Wave‐Driven Flooding Hazards on Coral Reef‐Lined Coasts

et al. 2017

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Many low‐elevation, coral reef‐lined, tropical coasts are vulnerable to the effects of climate change, sea level rise, and wave‐induced flooding. The considerable morphological diversity of these coasts and the variability of the hydrodynamic forcing that they are exposed to make predicting wave‐induced flooding a challenge. A process‐based wave‐resolving hydrodynamic model (XBeach Non‐Hydrostatic, “XBNH”) was used to create a large synthetic database for use in a “Bayesian Estimator for Wave Attack in Reef Environments” (BEWARE), relating incident hydrodynamics and coral reef geomorphology to coastal flooding hazards on reef‐lined coasts. Building on previous work, BEWARE improves system understanding of reef hydrodynamics by examining the intrinsic reef and extrinsic forcing factors controlling runup and flooding on reef‐lined coasts. The Bayesian estimator has high predictive skill for the XBNH model outputs that are flooding indicators, and was validated for a number of available field cases. It was found that, in order to accurately predict flooding hazards, water depth over the reef flat, incident wave conditions, and reef flat width are the most essential factors, whereas other factors such as beach slope and bed friction due to the presence or absence of corals are less important. BEWARE is a potentially powerful tool for use in early warning systems or risk assessment studies, and can be used to make projections about how wave‐induced flooding on coral reef‐lined coasts may change due to climate change.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Bayesian‐Based System to Assess Wave‐Driven Flooding Hazards on Coral Reef‐Lined Coasts

et al. 2017

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“…These are probabilistic models used to draw statistically significant correlations between identified variables in the modelled system [16]. This modelling approach has been utilised in coastal engineering applications to estimate storm erosion damage [17], surf zone processes [9,18] and sediment transport [16,19,20] to yield information on specific errors and uncertainty in the values for each output variable and model parameter. Bayesian Networks, however, require large observational datasets for model training and do not incorporate any of the underlying physics that allows model prediction at diverse sites where different processes may dominate [21].…”

Section: Introductionmentioning

confidence: 99%

Calibrating and assessing uncertainty in coastal numerical models

Simmons

Harley

Marshall

et al. 2017

Coastal Engineering

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“…In order to evaluate the three aspects mentioned above, three metocean models are considered by using different meteorological conditions (i.e., the wind and pressure fields) as input to the seastate prediction model: (1) the Mike 21 model driven by reanalysis data from the Climate Forecast System Reanalysis (CFSR) study 10 of the sustained wind speed V, the significant wave height H s , the peak spectral period T p , and the storm surge η are compared with corresponding buoy measurements during a set of 23 historical hurricanes at 107 locations. The prediction performance of the CFSR/Mike 21 model is compared with the hindcast data from another numerical metocean model, WaveWatch III (WW3), 11 which uses the same meteorological forcing, for the purpose of model validation. The Hybrid/Mike 21 model is compared with the CFSR/Mike 21 model to assess the influence of modeling meteorological conditions using the simple empirical Holland model compared with reanalysis data.…”

Section: Introductionmentioning

confidence: 99%

Validation and uncertainty quantification of metocean models for assessing hurricane risk

2019

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A reliable metocean model, with its uncertainty quantified and its accuracy validated for conditions appropriate to assessing risk, is essential to understand the risk posed by hurricanes to offshore infrastructure such as offshore wind turbines. In this paper, three metocean models are considered, with the seastate predicted using the commercial software Mike 21, and the meteorological forcing defined by three conditions. The three conditions include (1) reanalysis data within and surrounding the hurricane, (2) predictions from the empirical Holland model within the hurricane and reanalysis data surrounding the hurricane, and (3) predictions from the empirical Holland model within the hurricane and wind-free conditions surrounding the hurricane. The accuracy of the first metocean model is validated with (1) measurements of wind speed, wave height, wave period, and storm surge during 23 historical hurricanes from 1999 to 2012 and(2) a comparison to hindcast data from WaveWatch III, another numerical metocean model. The prediction performance of the second and third metocean models is then compared with that of the first to evaluate the impact of meteorological conditions on model predictions, as the third metocean model is necessary for risk analysis, where reanalysis data of meteorological conditions is not available. This study shows that the inconsistency between the modeling of meteorological conditions for risk assessment and for validation is influential for hurricanes with low maximum wind speeds, when model predictions are significantly better if the meteorological conditions surrounding the hurricane wind field are included. This study also shows that this inconsistency is effectively diminished when considering only events with high maximum wind speeds.Since high wind speeds are what is relevant to risk assessments, the third metocean model can be reasonably used to assess hurricane risk. Finally, the uncertainties, biases, and correlations of uncertainties in the model predictions for wind speed, wave height, wave period, and storm surge are quantified for the third metocean model, and a numerical example is constructed to illustrate the impact of including uncertainty on the assessment of risk to offshore infrastructure during hurricanes. The example demonstrates how uncertainty and correlation of uncertainty influence the size and shape of a 50-year environmental contour of wind speed and wave height. KEYWORDSnumerical model validation, offshore hurricane multi-hazard, risk analysis, uncertainty quantification

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Modelling multi-hazard hurricane damages on an urbanized coast with a Bayesian Network approach

Cited by 57 publications

References 21 publications

A Bayesian‐Based System to Assess Wave‐Driven Flooding Hazards on Coral Reef‐Lined Coasts

A Bayesian‐Based System to Assess Wave‐Driven Flooding Hazards on Coral Reef‐Lined Coasts

Calibrating and assessing uncertainty in coastal numerical models

Validation and uncertainty quantification of metocean models for assessing hurricane risk

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