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Discussion papers
https://doi.org/10.5194/nhess-2020-28
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-2020-28
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 28 Feb 2020

Submitted as: research article | 28 Feb 2020

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This preprint is currently under review for the journal NHESS.

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

Aloïs Tilloy1, Bruce D. Malamud1, Hugo Winter2, and Amélie Joly-Laugel2 Aloïs Tilloy et al.
  • 1Department of Geography, King’s College London, London WC2B 4BG, UK
  • 2EDF Energy R&D UK Centre, Croydon CR0 2AJ, UK

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.

Aloïs Tilloy et al.

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Aloïs Tilloy et al.

Data sets

European Climate Assessment & Dataset project Royal Netherlands Meteorological Institute, European Climate Assessment & Dataset project team https://doi.org/10.17616/R30015

The history and characteristics of the 1980–2005 Portuguese rural fire database M. G. Pereira, B. D. Malamud, R. M. Trigo, and P. I. Alves https://doi.org/10.5194/nhess-11-3343-2011

Aloïs Tilloy et al.

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Short summary
Estimating risks induced by interacting natural hazards remains a challenge for practitioners. An approach to tackle this challenge is to use multivariate statistical models. Here we evaluate the efficacy of six models. The models are compared against synthetic data which are comparable to time series of environmental variables. We find which models are more appropriate to estimate relations between hazards in a range of cases. We highlight the benefits of this approach with two examples.
Estimating risks induced by interacting natural hazards remains a challenge for practitioners....
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