Quantifying the sources of simulation uncertainty in natural catastrophe models

Abstract

The risk from natural catastrophes is typically estimated using complex simulation models involving multiple stochastic components in a nested structure. This risk is principally assessed via the mean annual loss, and selected quantiles of the annual loss. Determining an appropriate simulation strategy is important in order to achieve satisfactory convergence of these statistics, without excessive computation time and data storage requirements. This necessitates an understanding of the relative contribution of each of the stochastic components to the total variance of the statistics. A simple framework using random effects models and analysis of variance is used to partition the variance of the annual loss, which permits calculation of the variance of the mean annual loss with varying numbers of samples of each of the components. An extension to quantiles is developed using the empirical distribution function in combination with bootstrapping. The methods are applied to a European flood model, where the primary stochastic component relates to the frequency and severity of flood events, and three secondary components relate to defence levels, exposure locations and building vulnerability. As expected, it is found that the uncertainty due to the secondary components increases as the size of the portfolio of exposures decreases, and is higher for industrial and commercial business, compared with residential for all statistics of interest. In addition, interesting insights are gained as to the impact of flood defences on convergence.

Modulation of Economic Losses From European Floods by the North Atlantic Oscillation

“…This study uses the RMS Europe Inland Flood HD model, swhich was released in 2016 and has been adopted by a number of global insurance and reinsurance companies. The model was also used in previous research studies by Pall et al () and Kaczmarska et al (). The domain consists of 15 European countries: Austria, Belgium, Czech Republic, France, Germany, Hungary, Italy, Luxembourg, Liechtenstein, Poland, Principality of Monaco, Republic of Ireland, Slovakia, Switzerland, and United Kingdom; therefore, the results are limited to these countries.…”

“…This study uses the RMS Europe Inland Flood HD model, swhich was released in 2016 and has been adopted by a number of global insurance and reinsurance companies. The model was also used in previous research studies by Pall et al (2011) and Kaczmarska et al (2017). The domain consists of 15 European countries:…”

Adjusting catastrophe model ensembles using importance sampling, with application to damage estimation for varying levels of hurricane activity

“…However, if the YLT simulation process is itself very complex this may not be practical. For example, the flood model described by Kaczmarska et al (2018) and Zanardo et al (2019) simulates the years of the YLT using the output of continuous temporal simulations of the shallow water equations. This requires months of computation time and cannot be easily repeated.…”

“…In these flood models, events are derived from the output of continuous simulations of differential equations, and are locked into place in their simulated years in order to preserve the overall character and properties of that year (e.g. see the European flood model described by Kaczmarska et al (2018) and Zanardo et al (2019)). For these models the YLT adjustment method is a way to adjust the model while still conserving the properties of the individual simulation years.…”

“…For example, the flood model described by Kaczmarska et al . () and Zanardo et al . () simulates the years of the YLT using the output of continuous temporal simulations of the shallow water equations.…”