Relationship between rain and/or meltwater, pore-water pressure and displacement of a reactivated landslide

Matsuura, Sumio; Asano, Satoshi; Okamoto, Takashi

doi:10.1016/j.enggeo.2008.03.007

Cited by 115 publications

(53 citation statements)

References 21 publications

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“…As the strength along the slip surface is already at, or close to, the residual value the low magnitude velocities illustrated in Figure 13 were expected and are characteristic of a reactivation on an already defined rupture surface. For example, Schulz et al (2009) described how the Slumgullion Landslide displayed daily movement patterns where increased velocity seemingly coincided with the diurnal low tides of the atmosphere as changes in air pressure altered the frictional resistance along the shear surface leading to daily velocity cycles that have a peak of roughly 2-3mm/hour, and Matsuura et al (2008) reported the monitoring of a reactivated landslide under constant creep in Japan that experienced an average hourly displacement of 0.7mm.…”

Section: Ae Rate-and Velocity-time Deformation Event Profilesmentioning

confidence: 99%

Quantification of reactivated landslide behaviour using acoustic emission monitoring

et al. 2014

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Section: Ae Rate-and Velocity-time Deformation Event Profilesmentioning

confidence: 99%

Quantification of reactivated landslide behaviour using acoustic emission monitoring

et al. 2014

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“…During this process, the pressure of the water that fills the void spaces between soil particles and rock fissures rises when the amount of water infiltrating into the ground increases. A rise in pore-water pressure causes a drop in effective stress, affecting the stability of a slope, and thus is a major cause of landslides and other sediment-related disasters (Matsuura et al, 2008). Intense rainfall is believed to be a cause of shallow landslides (Caine, 1980).…”

Section: Explanatory Factorsmentioning

confidence: 99%

Landslide susceptibility mapping on a global scale using the method of logistic regression

Lin

Wang

2017

Nat. Hazards Earth Syst. Sci.

121

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Abstract. This paper proposes a statistical model for mapping global landslide susceptibility based on logistic regression. After investigating explanatory factors for landslides in the existing literature, five factors were selected for model landslide susceptibility: relative relief, extreme precipitation, lithology, ground motion and soil moisture. When building the model, 70 % of landslide and nonlandslide points were randomly selected for logistic regression, and the others were used for model validation. To evaluate the accuracy of predictive models, this paper adopts several criteria including a receiver operating characteristic (ROC) curve method. Logistic regression experiments found all five factors to be significant in explaining landslide occurrence on a global scale. During the modeling process, percentage correct in confusion matrix of landslide classification was approximately 80 % and the area under the curve (AUC) was nearly 0.87. During the validation process, the above statistics were about 81 % and 0.88, respectively. Such a result indicates that the model has strong robustness and stable performance. This model found that at a global scale, soil moisture can be dominant in the occurrence of landslides and topographic factor may be secondary.

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“…Almost every landslide has a basic water table or minimum water table (here starts at ∼ 29 kPa). It means the "drainage position" is higher than the "bedrock" (for other cases see Matsuura et al, 2008;Schulz et al, 2009;Yin et al, 2010). The relation between PWP i+1 and PWP i without rainfall infiltration is shown in Fig.…”

Section: Determining Change In Pore Water Pressure In the Modified Tamentioning

confidence: 99%

“…This is equivalent to the infiltration calculations of some authors (Suzuki and Kobashi, 1981;Matsuura et al, 2003Matsuura et al, , 2008) who define equivalent infiltration as…”

Section: Monitoring Datamentioning

confidence: 99%

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A modified tank model including snowmelt and infiltration time lags for deep-seated landslides in alpine environments (Aggenalm, Germany)

Nie¹,

Krautblatter²,

Leith³

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. Deep-seated landslides are an important and widespread natural hazard within alpine regions and can have significant impacts on infrastructure. Pore water pressure plays an important role in determining the stability of hydrologically triggered deep-seated landslides. Based on a simple tank model structure, we improve groundwater level prediction by introducing time lags associated with groundwater supply caused by snow accumulation, snowmelt and infiltration in deep-seated landslides. In this study, we demonstrate an equivalent infiltration calculation to improve the estimation of time lags using a modified tank model to calculate regional groundwater levels. Applied to the deep-seated Aggenalm landslide in the German Alps at 1000-1200 m a.s.l., our results predict daily changes in pore water pressure ranging from −1 to 1.6 kPa, depending on daily rainfall and snowmelt, which are compared to piezometric measurements in boreholes. The inclusion of time lags improves the results of standard tank models by ∼ 36 % (linear correlation with measurement) after heavy rainfall and by ∼ 82 % following snowmelt in a 1-2-day period. For the modified tank model, we introduced a representation of snow accumulation and snowmelt based on a temperature index and an equivalent infiltration method, i.e. the melted snow-water equivalent. The modified tank model compares well to borehole-derived water pressures. Changes of pore water pressure can be modelled with 0-8 % relative error in rainfall season (standard tank model: 2-16 % relative error) and with 0-7 % relative error in snowmelt season (standard tank model: 2-45 % relative error). Here we demonstrate a modified tank model for deep-seated landslides which includes snow accumulation, snowmelt and infiltration effects and can effectively predict changes in pore water pressure in alpine environments.

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Relationship between rain and/or meltwater, pore-water pressure and displacement of a reactivated landslide

Cited by 115 publications

References 21 publications

Quantification of reactivated landslide behaviour using acoustic emission monitoring

Quantification of reactivated landslide behaviour using acoustic emission monitoring

Landslide susceptibility mapping on a global scale using the method of logistic regression

A modified tank model including snowmelt and infiltration time lags for deep-seated landslides in alpine environments (Aggenalm, Germany)

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