Probability distributions of landslide volumes

By M. Brunetti, Fausto Guzzetti, Mauro Rossi. 2009

205 citations recorded by the source corpus.

We examine 19 datasets with measurements of landslide volume, VL, for sub-aerial, submarine, and extraterrestrial mass movements. Individual datasets in- clude from 17 to 1019 landslides of different types, includ- ing rock fall, rock slide, rock avalanche, soil slide, slide, and debris flow, with individual landslide volumes rang- ing over 10 4 m 3 VL 10 13 m 3 . We determine the prob- ability density of landslide volumes, p(VL), using kernel density estimation. Each landslide dataset exhibits heavy tailed (self-similar) behaviour for their frequency-size dis- tributions, p(VL) as a function of VL, for failures exceed- ing different threshold volumes, V L , for each dataset. These non-cumulative heavy-tailed distributions for each dataset are negative power-laws, with exponents 1.0 1.9, and averaging 1.3. The scaling behaviour of VL for the en- semble of the 19 datasets is over 17 orders of magnitude, and is independent of lithological characteristics, morpho- logical settings, triggering mechanisms, length of period and extent of the area covered by the datasets, presence or lack of water in the failed materials, and magnitude of gravita- tional fields. We argue that the statistics of landslide volume is conditioned primarily on the geometrical properties of the slope or rock mass where failures occur. Differences in the values of the scaling exponents reflect the primary landslide types, with rock falls exhibiting a smaller scaling exponent (1.1 1.4) than slides and soil slides (1.5 1.9). We argue that the difference is a consequence of the disparity in the mechanics of rock falls and slides.

Subfields: Ecology, Global and Planetary Change, Management, Monitoring, Policy and Law

View Paper: https://www.semanticscholar.org/paper/d66c3edec2de71965c3408b4084cfa93a3484166

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Citations in the corpus

Scaling properties of rainfall induced landslides predicted by a physically based model (2013, methodology): “This is important for the application of rainfall thresholds in landslide warning systems (Chleborad, 2003; Aleotti, 2004; Godt et al., 2006; Guzzetti et al., 2008; Brunetti et al., 2009b).”
Temporal correlations and clustering of landslides (2010, background): “…for many datasets that occur in nature (Turcotte, 1997; Guzzetti and Tonelli, 2004; Newman, 2005; Cello and Malamud, 2006; White et al., 2008; Brunetti et al., 2009), and because of this, certain ‘standard’ techniques (e.g. that require data to be equally spaced and with a Gaussian size…”
Landslide susceptibility estimation by random forests technique: sensitivity and scaling issues (2013, background): “, 2013) shows a scaling in agreement with the classical double-Pareto recognized in most of the landslide inventories worldwide (Stark and Hovius, 2001; Guzzetti et al., 2002; Stark and Guzzetti, 2009; Brunetti et al., 2009; Van den Eeckhaut et al., 2007) with exponents of 0.”
Space-time landslide predictive modelling (2019, background): “, 2004), volume (Malamud et al., 2004; Brunetti et al., 2009), area-to-volume ratio (Guzzetti et al.”