| Summary: | Although various methods to carry out quantitative landslide risk analyses are available, applications are still rare and mostly dependent on the occurrence of disasters. In Iceland, two catastrophic snow avalanches killed 34 people in 1995. As a consequence the Ministry of the Environment issued a new regulation on hazard zoning due to snow avalanches and landslides in 2000, which aims to prevent people living or working within the areas most at risk until 2010. The regulation requires to carry out landslide and snow avalanche risk analyses, however, a method to calculate landslide risk adopted to Icelandic conditions is still missing. Therefore, the ultimate goal of this study is to develop such a method for landslides, focussing on debris flows and rock falls and to test it in Bíldudalur, NW-Iceland. Risk analysis, beside risk evaluation and risk management, is part of the holistic concept of risk assessment. Within this study, risk analysis is considered only, focussing on the risks to life. To calculate landslide risk, the spatial and temporal probability of occurrence of potential damaging events, as well as the distribution of the elements at risk in space and time, considering also changing vulnerabilities, must be determined. Within this study, a new raster-based approach is developed. Thus, all existent vector data are transferred into raster data using a resolution of 1m x 1m. The specific attribute data are attributed to the grid cells, resulting in specific raster data layers for each input parameter. The calculation of the landslide risk follows a function of the input parameters hazard, damage potential of the elements at risk, vulnerability, probability of the spatial impact, probability of the temporal impact and probability of the seasonal occurrence. Finally, results are upscaled to a resolution of 20m x 20m and are presented as individual risk to life and object risk to life for each process. Within the quantitative landslide risk analysis the associated uncertainties are estimated ...
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