In recent decades numerical models have been developed and extensively used for landslide hazard and risk assessment. The reliability of the outcomes of these numerical simulations must be evaluated carefully as it mainly depends on the soundness of the physicalmodel of the landslide that in turn often requires the integration of several surface and subsurface surveys in order to achieve a satisfactory spatial resolution. Merging diverse sources of data may be particularly complex for large landslides, because of intrinsic heterogeneity and possible great data uncertainty. In this paper,we assess the spatial scale and data accuracy required for effective numerical landslide modeling. We focus on two particular aspects: the model extent and the accuracy of input datasets. The Ronco landslide, a deep-seated gravitational slope deformation (DSGSD) located in the North of Italy, was used as a test bed. Geological, geomorphological and geophysical data were combined and, as a result, eight models with different spatial scales and data accuracies were obtained. The models were used to run a back analysis of an event in 2002, during which part of the slope moved after intense rainfalls. The results point to the key role of a proper geomorphological zonation to properly set the model extent. The accuracy level of the input datasets should also be tuned. We suggest applying the approach presented here to other DSGSDs with different geological and geomorphological settings to test the reliability of our findings.

The role of the spatial scale and data accuracy on deep-seated gravitational slope deformation modeling: The Ronco landslide, Italy

LONGONI, LAURA;PAPINI, MONICA;BRAMBILLA, DAVIDE;ZANZI, LUIGI
2016

Abstract

In recent decades numerical models have been developed and extensively used for landslide hazard and risk assessment. The reliability of the outcomes of these numerical simulations must be evaluated carefully as it mainly depends on the soundness of the physicalmodel of the landslide that in turn often requires the integration of several surface and subsurface surveys in order to achieve a satisfactory spatial resolution. Merging diverse sources of data may be particularly complex for large landslides, because of intrinsic heterogeneity and possible great data uncertainty. In this paper,we assess the spatial scale and data accuracy required for effective numerical landslide modeling. We focus on two particular aspects: the model extent and the accuracy of input datasets. The Ronco landslide, a deep-seated gravitational slope deformation (DSGSD) located in the North of Italy, was used as a test bed. Geological, geomorphological and geophysical data were combined and, as a result, eight models with different spatial scales and data accuracies were obtained. The models were used to run a back analysis of an event in 2002, during which part of the slope moved after intense rainfalls. The results point to the key role of a proper geomorphological zonation to properly set the model extent. The accuracy level of the input datasets should also be tuned. We suggest applying the approach presented here to other DSGSDs with different geological and geomorphological settings to test the reliability of our findings.
Deep-seated gravitational deformations; Large landslide; Numerical modeling; Spatial scale; Earth-Surface Processes
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1005870
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