Topographic site effects and the location of earthquake induced landslides

Meunier, Patrick; Hovius, Niels; Haines, John

doi:10.1016/j.epsl.2008.07.020

Cited by 430 publications

(337 citation statements)

References 45 publications

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“…These spatial distribution characteristics are similar to those derived by Meunier et al (2008). We speculate that this is because the frequency of rainfall-induced landslides increases significantly because of bank erosion, which is shown in the 25 lower right half of Fig.…”

Section: Landslide Scale and Spatial Distributionsupporting

confidence: 71%

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Scale and spatial distribution assessment of rainfall-induced landslides along mountain roads

Tseng

Chen

Wu³

2017

Preprint

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Abstract. This study focused on landslides along mountain roads that were caused by Typhoons Nanmadol (2011) and Kong-rey (2013). Image interpretation techniques were employed to interpret satellite images captured before and after the typhoons to derive the changes in slope surfaces. The multivariate hazard evaluation method was adopted to establish a 10 landslide susceptibility assessment model. We then mapped landslide susceptibility and locations to determine the relationship between the spatial distribution of landslide areas and the natural environment along mountain roads. The results can serve as a reference for preventing and mitigating slope disasters on mountain roads.

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Section: Landslide Scale and Spatial Distributionsupporting

confidence: 71%

“…Following the methods in previous studies (Meunier et al, 2008;Chue et al, 2015), we extracted the distances between the 5 highest point of a landslide area and the nearest ridge (dr), between the lowest point of the landslide area and the nearest stream (ds), and between the ridge and the stream (dt) (Fig. 7).…”

Section: Landslide Scale and Spatial Distributionmentioning

confidence: 99%

Scale and spatial distribution assessment of rainfall-induced landslides along mountain roads

Tseng

Chen

Wu³

2017

Preprint

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“…The earthquake-induced landslide hazard is defined in the first instance by the number, size, and location of landslides. These variables are correlated with a combination of local factors, such as the peak ground acceleration (Meunier et al, 2007(Meunier et al, , 2008, hillslope geometry (Parise and Jibson, 2000;Gorum et al, 2013), and the strength (Parise and Jibson, 2000;Gallen et al, 2015) and degree of hydrological saturation of near-surface materials, which are difficult to quantify due to their inhomogeneity across epicentral areas (Dreyfus et al, 2013). A simpler approach is to predict first-order variables such as the total volume and area of landsliding caused by an earthquake based on simple seismological considerations (e.g.…”

Section: Introductionmentioning

confidence: 99%

Prediction of the area affected by earthquake-induced landsliding based on seismological parameters

Marc

Meunier

Hovius

2017

Nat. Hazards Earth Syst. Sci.

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Abstract. We present an analytical, seismologically consistent expression for the surface area of the region within which most landslides triggered by an earthquake are located (landslide distribution area). This expression is based on scaling laws relating seismic moment, source depth, and focal mechanism with ground shaking and fault rupture length and assumes a globally constant threshold of acceleration for onset of systematic mass wasting. The seismological assumptions are identical to those recently used to propose a seismologically consistent expression for the total volume and area of landslides triggered by an earthquake. To test the accuracy of the model we gathered geophysical information and estimates of the landslide distribution area for 83 earthquakes. To reduce uncertainties and inconsistencies in the estimation of the landslide distribution area, we propose an objective definition based on the shortest distance from the seismic wave emission line containing 95 % of the total landslide area. Without any empirical calibration the model explains 56 % of the variance in our dataset, and predicts 35 to 49 out of 83 cases within a factor of 2, depending on how we account for uncertainties on the seismic source depth. For most cases with comprehensive landslide inventories we show that our prediction compares well with the smallest region around the fault containing 95 % of the total landslide area. Aspects ignored by the model that could explain the residuals include local variations of the threshold of acceleration and processes modulating the surface ground shaking, such as the distribution of seismic energy release on the fault plane, the dynamic stress drop, and rupture directivity. Nevertheless, its simplicity and first-order accuracy suggest that the model can yield plausible and useful estimates of the landslide distribution area in near-real time, with earthquake parameters issued by standard detection routines.

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“…Landslides are triggered by a number of factors, including earthquakes (Keefer 2002;Malamud et al 2004;Meunier et al 2008), rainfall (Iverson 2000;Zêzere et al 2005;Guzzetti et al 2007;Keefer and Larsen 2007;Marques et al 2008), temperature change (Dehn and Buma 1999;Chemenda et al 2005), glacial recession and permafrost degradation (Dramis et al 1995;Stoffel et al 2014) and anthropogenic factors such as the removal of slope toes at road cuts (Barnard et al 2001). For landslides, the hydro-meteorological trigger is often rainfall (Jakob and Weatherly 2003;Farahmand and Aghakouchak 2013), and empirical rainfall thresholds are often used to define minimum triggering conditions for landslides (Peruccacci et al 2012); however these are often localized, and depend greatly on the quality of rainfall data (Gariano et al 2015).…”

Section: Introductionmentioning

confidence: 99%

Landslides and synoptic weather trends in the European Alps

2016

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Landslides present a substantial geomorphological hazard in Alpine regions and there are expectations that climate change will alter their frequency and magnitude in the future. Understanding the spatial distribution and timing of landslides in the context of past change is therefore necessary if we are to assess their future behaviour. Using a regional landslide inventory for the European Alps we analyse the influence of weather types, specifically the COST733 database, on landslides. Monte Carlo permutation tests are used to assess which weather types are most likely associated with landslides. Weather types with high precipitation are consistent with more landslides, although there are also seasonal differences. Over the duration of the COST733 catalogue there has been a significant decrease in the number of days with weather types associated with low frequencies of landslides. During the spring and autumn months, the trend in observed landslide frequency and weather types are well matched. However while there is potential for weather typing to be used as a proxy for future landslide frequency, other external factors must be carefully considered.

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Topographic site effects and the location of earthquake induced landslides

Cited by 430 publications

References 45 publications

Scale and spatial distribution assessment of rainfall-induced landslides along mountain roads

Scale and spatial distribution assessment of rainfall-induced landslides along mountain roads

Prediction of the area affected by earthquake-induced landsliding based on seismological parameters

Landslides and synoptic weather trends in the European Alps

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