Identification of landslide hazard and risk ‘hotspots’ in Europe View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-12-27

AUTHORS

Christian Jaedicke, Miet Van Den Eeckhaut, Farrokh Nadim, Javier Hervás, Bjørn Kalsnes, Bjørn Vidar Vangelsten, Jessica T. Smith, Veronica Tofani, Roxana Ciurean, Mike G. Winter, Kjetil Sverdrup-Thygeson, Egil Syre, Helge Smebye

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 × 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. More... »

PAGES

325-339

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10064-013-0541-0

DOI

http://dx.doi.org/10.1007/s10064-013-0541-0

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1036035742


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54 goal
55 good agreement
56 good overview
57 hazard results
58 hazards
59 hazards model
60 high landslide hazard
61 high landslide risk
62 higher relative exposure
63 hotspots
64 humans
65 identification
66 independent models
67 information
68 infrastructure
69 investment
70 kilometers
71 kilometers of roads
72 knowledge
73 knowledge of location
74 landslide events
75 landslide hazard
76 landslide inventory
77 landslide risk
78 landslides
79 levels
80 location
81 measures
82 mitigation measures
83 model
84 moving landslides
85 national level
86 need
87 number
88 objective analysis
89 organization
90 overview
91 parameters
92 part
93 parts of Europe
94 people
95 physical processes
96 pixel resolution
97 political will
98 population
99 previous landslide events
100 problem
101 process
102 railway
103 ranking
104 relative area
105 relative exposure
106 resolution
107 results
108 risk
109 risk hotspots
110 road
111 rockfalls
112 same country
113 scores
114 serious problem
115 shallow landslides
116 simple ranking
117 slow moving landslides
118 small Alpine Countries
119 statistical model
120 study
121 susceptibility
122 susceptibility factors
123 terms
124 total value
125 validation
126 values
127 weight
128 will
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