Infrastructure exposure to shallow landslides in Upper Gudbrandsdalen; Predicting the impact of climate change using the TRIGRS and RAMMS models

Linear infrastructure networks, such as roads and railways, are of great social and economic importance in connecting the different regions of Norway. Landslides are cause of unexpected traffic flow disruptions of significant societal cost every year, costs which are expected to increase in the future with climatic and demographic changes. This study presents a combination of the two physically based models TRIGRS and RAMMS to produce a hazard chain from intense rainfall to subsequent landslide initiation and runout for a case study site in upper Gudbrandsdalen in South-Eastern Norway. By implementing a climate factor to the precipitation intensity, the climatic effect is traced through the hazard chain, successfully illustrating future change in infrastructure exposure to landslides. A significant increase of unstable slope areas with response to climate induced increase in precipitation intensity is observed for extreme rainfall events of 20-, 50-, 100- and 200-year return period. Similar results are observed for the runout simulations, where the climatic effect is seen as an increase in both runout volume and spatial distribution of flow. The runout analysis outlines the historically most active parts of the study area, with six out of seven documented landslides occurring within the modelled sections of exposed road and railway. The uncertainty of the model is well illustrated by a poor match to both release areas and runout footprint of the landslide events subject to calibration of the model. Model accuracy is limited due to shortcomings of data for the calibration event, input detail of physical characteristics of surface and sub-surface, as well as uncertainty related to the landslide release mechanisms. Although the practical utility of the coupled model is thereby limited in terms of accurate landslide prediction, the results illustrate how it may be utilized in remotely based assessments of hazard hot-spots and assist in selection of sites suitable for risk-reducing measures