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Quick clay landslides are a special feature of Norwegian and Swedish geologies. Vibrations or small initial landslides can cause a quick clay layer to collapse and liquefy, resulting in rapid landslides with little or no time for evacuation, making them a real threat to human life. Research concentrating on damages due to landslides is scarce, and analyses of loss of human lives caused by quick clay landslides in the scientific literature are, to our knowledge, nonexisting. Fatality quantification can complement landslide risk assessments and serves as guidance for policy choices when evaluating efficient risk-reducing measures. The objectives of this study were to assess and analyze available damage information in an existing data set of 66 historical landslide events that occurred in Norway and Sweden between 1848 and 2009, and access its applicability for quantifying loss of human life caused by quick clay landslides. Fatality curves were estimated as functions of the number of exposed persons per landslide. Monte Carlo simulations were used to account for the uncertainties in the number of people actually exposed. The results of the study imply that the quick clay fatality curves are nonlinear, indicating that the probability of losing lives increases exponentially when the number of exposed persons increases. Potential factors affecting human susceptibility to landslides (e.g., landslide-, area-, or individual-specific characteristics) could not be satisfyingly quantified based on available historical records. Future research should concentrate on quantifying susceptibility factors that can further explain human vulnerability to quick clay landslides.

This study uses insurance claims as a proxy for property damage to analyse flood damage in Sweden between the years 1987 and 2013. The number of compensated insurance claims per year has risen rapidly during this period. As much as 70% of the claims are caused by flood damage occurring during the summer months June, July, and August, when intense rain with low predictability is common. To explore the damage trend a time series cross sectional analysis using four different fixed effect models was applied to the data set. Due to data scarcity, the time series had to be limited to 16 years and contain a total of 304 damage observations. The potentially explanatory climate related factor extreme rain, defined as >6 mm/15 min, and the socioeconomic factors gross regional product (GRP) per capita and housing stock were tested as explanatory factors. The GRP per capita and housing stock were found to be significant in two regression models. The estimated effect of extreme rainfall events exceeded the effects of GRP per capita and housing stock in the models. Extreme rain was robust to model specification and was found to have a highly significant impact on Swedish flood damage.