Global landslide and avalanche hotspots

Abstract

Allocating resources for natural hazard risk management has high priority in development banks and international agencies working in developing countries. Global hazard and risk maps for Rank landslides and avalanches were developed to identify the most ex- posed countries. Based on the global datasets of climate, lithology, earthquake activity, and topography, areas with the highest hazard, or “hotspots”, were identified. The applied model was based on classed values of all input data. The model output is a landslide and avalanche hazard index, which is globally scaled into nine levels. The model re- sults were calibrated and validated in selected areas where good data on slide events exist. The results from the landslide and avalanche hazard model together with global population data were then used as input for the risk assessment. Regions with the highest risk can be found in Colombia, Tajikistan, India, and Nepal where the estimated number of people killed per year per 100 km2 was found to be greater than one. The model made a reasonable prediction of the landslide hazard in 240 of 249 countries. More and better input data could im- prove the model further. Future work will focus on selected areas to study the applicability of the model on national and regional scales

Satellite radar data for back-analyzing a landslide event: the Ponzano (Central Italy) case study

“…Italy is one of the European countries with the highest percentage of landslide prone landscape (13%, according to Nadim et al 2006). The slope movements are responsible for a considerable annual economic loss for the Nation, estimated 0.3-0.4% of the gross domestic product (Canuti et al 2004).…”

Using an Unmanned Aerial Vehicle-Based Digital Imaging System to Derive a 3D Point Cloud for Landslide Scarp Recognition

“…Earthquakes, floods, and landslides are among the major threats to urban areas [1,2]. During the 1990s, nearly nine percent of the world's natural disasters were landslides, which are difficult to predict [3][4][5].…”

Assessment of data availability for the development of landslide fatality curves

“…In contrast to other natural hazards (e.g., floods or earthquakes), which affect large areas, landslides are generally limited in area but can occur with a high frequency in a specific region (JRC 2011). Landslides can be caused by both natural and anthropogenic actions, and landslide disasters are increasing worldwide (Andersson-Sköld et al 2014;Guzzetti 2016;Ho et al 2013;Nadim et al 2006;SOU 2007). The reasons are suspected to be increased susceptibility of surface soil to instability because of deforestation and overexploration of natural resources, greater vulnerability of exposed population as a result of urbanization and uncontrolled land use, and climate change resulting in a greater potential for weather extremes (Lacasse et al 2010;Nadim et al 2006).…”

“…Landslides can be caused by both natural and anthropogenic actions, and landslide disasters are increasing worldwide (Andersson-Sköld et al 2014;Guzzetti 2016;Ho et al 2013;Nadim et al 2006;SOU 2007). The reasons are suspected to be increased susceptibility of surface soil to instability because of deforestation and overexploration of natural resources, greater vulnerability of exposed population as a result of urbanization and uncontrolled land use, and climate change resulting in a greater potential for weather extremes (Lacasse et al 2010;Nadim et al 2006). Pereira et al (2014) have found that most landslides occur during the wettest months, reflecting the importance of rainfall-triggering mechanisms.…”