Accessing Insurance Flood Losses Using a Catastrophe Model and Climate Change Scenarios

Abstract

Impact Forecasting has developed a catastrophe flood model for Czechia to estimate insurance losses. The model is built on a dataset of 12,066 years of daily rainfall and temperature data for the European area, representing the current climate (LAERTES-EU). This dataset was used as input to the rainfall–runoff model, resulting in a series of daily river channel discharges. Using analyses of global and regional climate models dealing with the impacts of climate change, this dataset was adjusted for the individual RCP climate scenarios in Europe. The river channel discharges were then re-derived using the already calibrated rainfall–runoff models. Based on the changed discharges, alternative versions of the standard catastrophe flood model for the Czechia were created for the various climate scenarios. In outputs, differences in severity, intensity, and number of events might be observed, as well as the size of storms. The effect on the losses might be investigated by probable maximum losses (PML) curves and average annual loss (AAL) values. For return period 1 in 5 years for the worst-case scenario, the differences can be up to +125 percent increase in insurance losses, while for the return period 1 in 100 years it is a −40 percent decrease. There is no significant effect of adaptation measures for the return period 1 in 100 years, but there is a −20 percent decrease in the return period 1 in 5 years.

Categorizing and Harmonizing Natural, Technological, and Socio-Economic Perils Following the Catastrophe Modeling Paradigm

“…Hence, the only heterogeneous, silo-type, parts of the CAT modeling pipeline are all the steps leading to the assessment of the hazard intensity footprint I and of its rate λ. While the most common perils in CAT models [10][11][12] remain earthquakes [13,14], tropical and extratropical cyclones [15,16], and floods [17,18], others have been progressively added, such as convective storms with hail and tornados [19,20], cyber-attacks [21,22], epidemics [23,24], terrorist attacks [25,26], and wildfires [27]. Secondary perils, also included in some CAT models, include fire-following earthquakes [28], storm surges [29], and business interruption [30].…”

“…Armed conflicts (incl. terrorism): The size distribution follows Equation (18), with S = N as the number of fatalities [97]. In Ref.…”

“…Blackouts: The size distribution follows Equation (18), with S = N as the number of customers affected. In Ref.…”

“…Cyber-attacks: The size distribution follows Equation (18), with S = N the number of personal identity losses or data breach volume (used as the example in this case). In Ref.…”

“…Landslides: The size distribution follows Equation (18), with S = A [km 2 ] the landslide area or S = V [km 3 ] the landslide volume. Conversion from area to volume can be performed with the empirical scaling relationship V = cA 1.5 [104].…”