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NH3.11

Landslide hazard and risk assessment, and landslide management
Convener: Paola Reichenbach  | Co-Conveners: Fausto Guzzetti , Andreas Günther 
Orals
 / Tue, 09 Apr, 10:30–12:00  / 13:30–17:00  / Room G8
Posters
 / Attendance Tue, 09 Apr, 17:30–19:00  / Blue Posters

Landslides occur in all geographic regions in response to a wide range of conditions and triggering processes that include storms, earthquakes, and human activities. In addition to direct losses, landslides also cause significant environmental damage and societal disruption. The goal of many landslide studies is the determination of the risk posed by existing or future slope failures. To achieve this goal, information on landslide hazard and vulnerability to landslides is required. Based on landslide typology and assessment scales, a wide array of different evaluation techniques for hazard and risk analyses is available and under development.
The session is aimed at comparing qualitative or quantitative landslide hazard and risk estimates in different physio- and geographical settings for different kinds of processes affecting the environment at different spatial scales. Since it is necessary to assess both the spatial and temporal probability of landslide hazard before evaluating landslide risk, contributions dealing with landslide mapping and landslide hazard assessment are welcomed. Presentations on landslide hazard and risk assessment at local, regional or national scale and in different physiographic, climatic, and geological settings are solicited. Presentations focussing on adoption and integration of different modelling approaches for quantitative assessments are welcomed. Papers that provide information on quality, reliability and limitations of process-oriented or statistical models are encouraged. The session’s aim is to present contributions dealing with heuristic, statistical, deterministic or physical based methods and models to evaluate and compare landslide hazards and risk. Model fitting performance, model validation and model prediction skill should be critically analysed.