Slope Angle as Indicator Parameter of Landslide Susceptibility in a Geologically Complex Area

Donnarumma, Angelo; Revellino, Paola; Grelle, Gerardo; Guadagno, Francesco M.

doi:10.1007/978-3-642-31325-7_56

Cited by 27 publications

(12 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A more extensive geomorphological interpretation of covariate effects (e.g., Donnarumma et al, ; Johnston& Christensen, ; Li et al, ; McFadden et al, ; Meunier et al, ; Parker, , ; Selby, ) is provided in the supporting information.…”

Section: Resultsmentioning

confidence: 99%

Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides

et al. 2019

View full text Add to dashboard Cite

We investigate earthquake-induced landslides using a geostatistical model featuring a latent spatial effect (LSE). The LSE represents the spatially structured residuals in the data, which remain after adjusting for covariate effects. To determine whether the LSE captures the residual signal from a given trigger, we test the LSE in reproducing the pattern of seismic shaking from the distribution of seismically induced landslides, without prior knowledge of the earthquake being included in the model. We assessed the landslide intensity, that is, the expected number of landslides per mapping unit, for the area in which landslides triggered by the Wenchuan and Lushan earthquakes overlap. We examined this area to test our method on landslide inventories located in near and far fields of the earthquake. We generated three models for both earthquakes: (i) seismic parameters only (proxy for the trigger); (ii) the LSE only; and (iii) both seismic parameters and the LSE. The three configurations share the same morphometric covariates. This allowed us to study the LSE pattern and assess whether it approximated the seismic effects. Our results show that the LSE reproduced the shaking patterns for both earthquakes. In addition, the models including the LSE perform better than conventional models featuring seismic parameters only. Due to computational limitations we carried out a detailed analysis for a relatively small area (2,112 km 2 ), using a data set with higher spatial resolution. Results were consistent with those of a subsequent analysis for a larger area (14,648 km 2 ) using coarser-resolution data.

show abstract

Section: Resultsmentioning

confidence: 99%

Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Slope angles are known to be a key parameter in estimating susceptibility to developing earth flows (Donnarumma et al, 2013). The TRI is defined as the mean difference between a central pixel and its surrounding cells (Wilson et al, 2007) and can be used to quantify landscape heterogeneities, which could exert influence on the localisation of the triggering area of shallow landslides (Persichillo et al, 2016).…”

Section: Methodsmentioning

confidence: 99%

Potential future exposure of European land transport infrastructure to rainfall-induced landslides throughout the 21st century

Schlögl

Matulla

2018

Nat. Hazards Earth Syst. Sci.

View full text Add to dashboard Cite

Abstract. In the face of climate change, the assessment of land transport infrastructure exposure towards adverse climate events is of major importance for Europe's economic prosperity and social wellbeing. In this study, a climate index estimating rainfall patterns which trigger landslides in central Europe is analysed until the end of this century and compared to present-day conditions. The analysis of the potential future development of landslide risk is based on an ensemble of dynamically downscaled climate projections which are driven by the SRES A1B socio-economic scenario. Resulting regional-scale climate change projections across central Europe are concatenated with Europe's road and railway network. Results indicate overall increases of landslide occurrence. While flat terrain at low altitudes exhibits an increase of about 1 more potentially landslide-inducing rainfall period per year until the end of this century, higher elevated regions are more affected and show increases of up to 14 additional periods. This general spatial distribution emerges in the near future (2021-2050) but becomes more pronounced in the remote future (2071-2100). Since largest increases are to be found in Alsace, potential impacts of an increasing amount of landslides are discussed using the example of a case study covering the Black Forest mountain range in Baden-Württemberg by further enriching the climate information with additional geodata. The findings derived are suitable to support political decision makers and European authorities in transport, freight and logistics by offering detailed information on which parts of Europe's ground transport network are at particularly high risk concerning landslide activity.

show abstract

“…Both variables contributed to increase the landslide intensity (hence the susceptibility) in all the SUs. From a geomorphological perspective, terrain slope controls the balance of the retaining and the destabilising forces acting on a slope (Taylor, 1948;Wu and Sidle, 1995;Donnarumma et al, 2013), and in many areas and Figure 9: Regression coefficients of all covariates, whose coefficients were significant in at least one of the fitted models, Mod1 (pink), Mod2 (blue), Mod3 (orange), Mod4 (yellow), and Mod5 (brown). Diamonds show posterior means; triangles show pointwise 97.5 and 2.5 posterior percentiles bracketing 95% pointwise credible intervals.…”

Section: Covariates Effectsmentioning

confidence: 99%

Space-Time Landslide Predictive Modelling

Lombardo¹,

Opitz²,

Ardizzone³

et al. 2020

Preprint

View full text Add to dashboard Cite

Landslides are nearly ubiquitous phenomena and pose severe threats to people, properties, and the environment in many areas. Investigators have for long attempted to estimate landslide hazard in an effort to determine where, when (or how frequently), and how large (or how destructive) landslides are expected to be in an area. This information may prove useful to design landslide mitigation strategies, and to reduce landslide risk and societal and economic losses. In the geomorphology literature, most of the attempts at predicting the occurrence of populations of landslides by adopting statistical approaches are based on the empirical observation that landslides occur as a result of multiple, interacting, conditioning and triggering factors. Based on this observation, and under the assumption that at the spatial and temporal scales of our investigation individual landslides are discrete "point" events in the landscape, we propose a novel Bayesian modelling framework for the prediction of the spatio-temporal occurrence of landslides of the slide type caused by weather triggers. We build our modelling effort on a Log-Gaussian Cox Process (LGCP) by assuming that individual landslides in an area are the result of a point process described by an unknown intensity function. The modelling framework has two stochastic components: (i) a Poisson component, which models the observed (random) landslide count in each terrain subdivision for a given landslide "intensity", i.e., the expected number of landslides per terrain subdivision (which may be transformed into a corresponding landslide "susceptibility"); and (ii) a Gaussian component, used to account for the spatial distribution of the local environmental conditions that influence landslide occurrence, and for the spatio-temporal distribution of "unobserved" latent environmental controls on landslide occurrence. We tested our prediction framework in the Collazzone area, Umbria, Central Italy, for which a detailed multi-temporal landslide inventory covering the period from before 1941 to 2014 is available together with lithological and bedding data. We subdivided the 79 km 2 area into 889 slope units (SUs). In each SU, we computed the percentage of 16 morphometric covariates derived from a 10 m × 10 m digital elevation model, and 13 lithological and bedding attitude covariates obtained from a 1:10,000 scale geological map. We further counted how many of the 3,379 landslides in the multi-temporal inventory affect each SU and grouped them into six periods. We used this complex space-time information to prepare five models of increasing complexity. Our "baseline" model (Mod1) carries the spatial information only through the covariates mentioned above. It does not include any additional information about the spatial and temporal structure of the data, and it is therefore equivalent to a "traditional" landslide susceptibility model. The second model (Mod2) is analogous, but it allows for time-interval-specific regression constants. Our next two models are more complex. In partic...

show abstract

Slope Angle as Indicator Parameter of Landslide Susceptibility in a Geologically Complex Area

Cited by 27 publications

References 12 publications

Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides

Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides

Potential future exposure of European land transport infrastructure to rainfall-induced landslides throughout the 21st century

Space-Time Landslide Predictive Modelling

Contact Info

Product

Resources

About