Identification of landslide hazard and risk ‘hotspots’ in Europe

Abstract

Landslides are a serious problem for humans and infrastructure in many parts of Europe. Experts know to a certain degree which parts of the continent are most exposed to landslide hazard. Nevertheless, neither the geographical location of previous landslide events nor knowledge of locations with high landslide hazard necessarily point out the areas with highest landslide risk. In addition, landslides often occur unexpectedly and the decisions on where investments should be made to manage and mitigate future events are based on the need to demonstrate action and political will. The goal of this study was to undertake a uniform and objective analysis of landslide hazard and risk for Europe. Two independent models, an expert-based or heuristic and a statistical model (logistic regression), were developed to assess the landslide hazard. Both models are based on applying an appropriate combination of the parameters representing susceptibility factors (slope, lithology, soil moisture, vegetation cover and other-factors if available) and triggering factors (extreme precipitation and seismicity). The weights of different susceptibility and triggering factors are calibrated to the information available in landslide inventories and physical processes. The analysis is based on uniform gridded data for Europe with a pixel resolution of roughly 30 m 9 30 m. A validation of the two hazard models by organizations in Scotland, Italy, and Romania showed good agreement for shallow landslides and rockfalls, but the hazard models fail to cover areas with slow moving landslides. In general, the results from the two models agree well pointing out the same countries with the highest total and relative area exposed to landslides. Landslide risk was quantified by counting the number of exposed people and exposed kilometers of roads and railways in each country. This process was repeated for both models. The results show the highest relative exposure to landslides in small alpine countries such as Lichtenstein. In terms of total values on a national level, Italy scores highest in both the extent of exposed area and the number for exposed population. Again, results agree between the two models, but differences between the models are higher for the risk than for the hazard results. The analysis gives a good overview of the landslide hazard and risk hotspots in Europe and allows a simple ranking of areas where mitigation measures might be most effective.

Potential future exposure of European land transport infrastructure to rainfall-induced landslides throughout the 21st century

“…The obtained landslide exposure, which is based on the selected CI, is then analysed and discussed against the background of these additional environmental features that are widely used in landslide studies (e.g. Jaedicke et al, 2014;Papathoma-Köhle and Glade, 2013;Van Den Eeckhaut et al, 2012).…”

“…Gariano et al, 2017b;Gariano and Guzzetti, 2016;Jaedicke et al, 2014;Promper et al, 2014;Van Den Eeckhaut et al, 2012;Crozier, 2010).…”

Pan-European landslide susceptibility mapping: ELSUS Version 2

“…Evaluating landslide susceptibility at small scales (<1:200,000) over large areas (entire nations or continents) generally suffers from high generalization, low resolution of spatial input data and incomplete landslide inventory information, making data-driven statistical modelling very difficult. Therefore, landslide susceptibility mapping at global (Hong, Adler, & Huffman, 2007;Nadim, Kjekstad, Peduzzi, Herold, & Jaedicke, 2006), continental European (Günther et al, 2013;Jaedicke et al, 2014) and national scales (e.g. Sakkas, Misailidis, Sakellariou, Kouskouna, & Kaviris, 2016) are often performed without landslide information.…”

Spatially distributed rainfall information and its potential for regional landslide early warning systems

“…There is an abundance of related geostatistical literature available that readers are referred to for more in-depth information (e.g. Isaaks and Srivastava 1989;Cressie 1993;Goovaerts 1997;Webster and Oliver 2001).…”