A review of quantification methodologies for multi-hazard interrelationships

Tilloy, Aloïs; Malamud, Bruce D.; Winter, Hugo; Joly-Laugel, Amélie

doi:10.1016/j.earscirev.2019.102881

Cited by 201 publications

(162 citation statements)

References 110 publications

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“…As discussed in the beginning of this section, extreme (hot) temperature and wildfire are interrelated. Indeed, extreme (hot) 655 temperature may promote the development of wildfires (Witte et al, 2011;Sutanto et al, 2020) According to Tilloy et al (2019), this is a change condition interrelation (i.e., one hazard changes environmental parameter that moves toward a change in the likelihood of another hazard). We then estimate the conditional exceedance probability curve (Sect.…”

Section: B)mentioning

confidence: 99%

“…The JT-KDE and Cond-Ex models are suitable for higher dimensions (Davison and Huser, 2015;Cooley et al, 2019), although, 745 these have not been tested for high dimensional multi-hazard modelling yet (Tilloy et al, 2019). Recent research conducted suggest pair-copula construction (PCC, also known as vine copula) (Bedford and Cooke, 2002;Hashemi et al, 2016;Bevacqua et al, 2017, Lui et al, 2018 and non-parametric Bayesian networks (NPBN) (Hanea et al, 2015;Couasnon et al, 2018) can be used to model multi-hazard events with more than two hazards.…”

Section: Datasetmentioning

confidence: 99%

“…Multi-hazard events have the potential to cause damage to infrastructure and people that may differ greatly from the associated risks posed by a single hazard (Terzi et al, 2019). Here, we consider two main mechanisms in natural hazards interrelations defined in Tilloy et al (2019): (i) cascade interrelations (i.e., when there is a temporal order and causality between natural hazards); (ii) compound interrelations (i.e., when several natural hazards are statistically dependent 25 without causality).…”

Section: Introduction 20mentioning

confidence: 99%

“…For a given mechanism, interrelations also vary in strength and intensity. Additionally, as highlighted in Tilloy et al (2019), different modelling approaches have been developed to quantify interrelations between variables. Here we focus on stochastic models that include copulas (Nelsen, the results of simulation study.…”

Section: Introduction 20mentioning

confidence: 99%

“…CC BY 4.0 License. Extreme rainfall and extreme wind have a compound interrelation according to Tilloy et al (2019). We then estimate the joint exceedance probability curve, corresponding to a PAND probability (Sect.…”

mentioning

confidence: 99%

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Evaluating the efficacy of bivariate extreme modelling approaches for multi-hazard scenarios

Tilloy¹,

Malamud²,

Winter³

et al. 2020

Preprint

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Abstract. Estimating risks generated by multi-hazard scenarios remains a challenge for practitioners. Here we evaluate the efficacy of bivariate extreme modelling approaches by fitting six distinct stochastic models to synthetic datasets. The properties of the synthetic datasets (marginal distributions, tail dependence structure) are chosen to match bivariate time series of environmental variables. The six models are copulas (one non-parametric, one semi-parametric, four parametric). We build 60 distinct synthetic datasets based on different parameters of log-normal margins and two different copulas. We contrast the model strengths (model flexibility) and weaknesses (poorer fits to the data). We find that no one model fits our synthetic data for all parameters, but rather a range of models are more appropriate To highlight the benefits of the systematic modelling framework developed, we consider the following environmental data: (i) daily precipitation and maximum wind gust in London, UK; (ii) daily mean temperature and wildfire number in Porto district, Portugal. In both cases there is good agreement in the estimation of bivariate return periods between models selected from the systematic framework developed in this study. Within this framework, we have explored a way to model multi-hazard events and identify the most efficient models for a given set of synthetic data and hazard sets.

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Section: B)mentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

Section: Introduction 20mentioning

confidence: 99%

Section: Introduction 20mentioning

confidence: 99%

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confidence: 99%

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Evaluating the efficacy of bivariate extreme modelling approaches for multi-hazard scenarios

Tilloy¹,

Malamud²,

Winter³

et al. 2020

Preprint

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Continuous Satellite Missions, Data Availability, and Nature of Future Satellite Missions with Implications to Polar Regions

Mishra

and²,

Pandey³

2022

Advances in Remote Sensing Technology and the Three Poles

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A multi‐hazard analysis framework for earthquake‐damaged tall buildings subject to thunderstorm downbursts

Song

Skalomenos

Martínez-Vázquez

2023

Earthq Engng Struct Dyn

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This study develops a generalised multi‐hazard analysis framework for evaluating the impact of secondary hazard events on structures that have previously been damaged by major hazardous events. More specifically, the present work investigates the effects of thunderstorm downbursts on earthquake‐damaged tall steel buildings giving an emphasis in assessing the revised accumulated earthquake‐wind ductility demands. The novel approach is validated on a 20‐storey steel building. The structure is initially subjected to a ductility‐controlled pushover analysis to simulate the earthquake‐induced damage, and then, a parametric dynamic analysis is performed using damaged structure models under synthetic downburst time‐histories developed for various wind profiles, wind velocities and terrain types. The method accurately controls the damage level induced by the primary hazard and separately assesses secondary hazard effects which enables a direct quantification of the new multi‐hazard design requirements. This can provide effective guidance in the stage of preliminary structural design or post‐hazard assessment of structures identifying new serviceability limits, as well as support repair procedures and decision‐making frameworks for existing structures to prevent collapse. The results demonstrate a significant increase up to three to four times in ductility demands of the structure after the occurrence of the strong downburst winds, compared with the ductility demands that were initially imposed by the varying severity degrees of the earthquake.

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A review of quantification methodologies for multi-hazard interrelationships

Cited by 201 publications

References 110 publications

Evaluating the efficacy of bivariate extreme modelling approaches for multi-hazard scenarios

Evaluating the efficacy of bivariate extreme modelling approaches for multi-hazard scenarios

Continuous Satellite Missions, Data Availability, and Nature of Future Satellite Missions with Implications to Polar Regions

A multi‐hazard analysis framework for earthquake‐damaged tall buildings subject to thunderstorm downbursts

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