Faith Taylor (1), Michele Santangelo (2), Bruce D. Malamud (1), Ivan Marchesini (2), and Fausto Guzzetti (2), 2014, A Stochastic Monte-Carlo Model to Study the Impact of Large Triggered Landslide Events on Road Networks, 9th Alexander von Humboldt International Conference, Istanbul, 24 - 28/3/2014,
URL: http://www.cnr.it/prodotto/i/304959

Triggers such as earthquakes or heavy rainfall can result in hundreds to thousands of landslides occurring across a region within a short space of time. These landslides can in turn result in blockages across the road network, impacting how people move about a region. Here, we show the development and application of a semi-stochastic model to simulate how landslides intersect with road networks during a triggered landslide event. This was per- formed by creating "synthetic" triggered landslide inventory maps and overlaying these with a road network map to identify where road blockages occur. Our landslide-road model has been applied to two regions_ (i) the Collazzone basin (79 km 2 ) in Central Italy where 422 landslides were triggered by rapid snowmelt in January 1997, (ii) the Oat Mountain quadrangle (155 km 2 ) in California, USA, where 1,350 landslides were triggered by the Northridge Earthquake (M = 6.7) in January 1994. Initial results show reasonable agreement between model output and the observed landslide inventories in terms of the number of road blockages. In Collazzone (length of road network = 153 km, landslide density = 5.2 landslides km -2 ), the median number of modelled road blockages over 100 model runs was 5 (±2.5 standard deviation) compared to the mapped inventory observed number of 5 road blockages. In Northridge (length of road network = 780 km, landslide density = 8.7 landslides km -2 ), the median number of modelled road blockages over 100 model runs was 108 (±17.2 standard deviation) compared to the mapped inventory observed number of 48 road blockages. As we progress with model development, we believe this semi- stochastic modelling approach will potentially aid civil protection agencies to explore different scenarios of road network potential damage as the result of different magnitude landslide triggering event scenarios.

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