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Empirical Landslide Runout Relationships Data set V1.0

Understanding how far a landslide travels from its source, and what the landslide may impact, is important in order to determine the hazard and risk posed by a landslide. The type of landslide (e.g. a rock avalanche versus a debris flow) and size can determine how far a landslide may runout. To estimate the potential runout area of future landslides, back-analyses of landslides that have already occurred can be used to calculate empirical parameters that predict landslide runout. These parameters are the volume of the landslide and the Fahrböschung angle, which is the angle between the landslide source and the landslide deposit (refer to paper)

The dataset presented here is a compilation a global dataset of over 1100 back-analysed landslides from academic papers and reports where these parameters are reported. The data is presented as csv. files containing information on the landslide location, reference source, volume and all available empirical runout data. The datasets are split into different landslide types, including rock avalanches, debris avalanches, debris flows, fill slope failures and mine waste failures. These datasets can be used by researchers, and geotechnical professionals to conduct forward-looking probabilistic runout analysis for different landslide types.

Landslide Runout Data

Landslide Runout and F-Angle

References

Brideau, Marc-Andre; de Vilder, Saskia; Massey, Chris; Mitchell, Andrew; McDougall, Scott; Aaron, Jordan (2021) V1.0 Empirical Landslide Runout Relationships, compiled from international examples, for various landslide types. DesignSafe-CI. https://doi.org/10.17603/ds2-9qbx-n796.

Brideau M A, de Vilder S, Massey C, Mitchell A, McDougall S,. Aaron J. 2021. Empirical Relationships to Estimate the Probability of Runout Exceedance for Various Landslide Types in F. Guzzetti et al. (eds.), Understanding and Reducing Landslide Disaster Risk, ICL Contribution to Landslide Disaster Risk Reduction, © Springer Nature Switzerland AG 2021. https://doi.org/10.1007/978-3-030-60227-7_36

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