Effect of topography and soil parameterisation representing soil thicknesses on shallow landslide modelling

Kim, Min Seok; Onda, Yuichi; Kim, Jin Kwan; Kim, Suk Woo

doi:10.1016/j.quaint.2015.03.057

Cited by 32 publications

(33 citation statements)

References 68 publications

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“…The bedrock surface connects sparsely saturated regions [28], and thus determines, along with regolith thickness, the water pressure within the soil mantle's pores. Indeed, depth to bedrock is a key variable that controls subsurface flow [29], and triggers landslides during rainfall events [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

“…Resulting topographic maps often do not do justice to complex field measurements of the bedrock depth, which often demonstrate significant spatial variability [3,33] with a geometry that is difficult to characterize adequately with some closed-form mathematical expression, while hydraulic and strength parameters can vary abruptly at the soil-bedrock interface. Whereas some authors have used high-resolution bedrock depth maps to assess hillslope stability [28,32,34,35], existing studies in the literature do not properly recognize the effect of bedrock depth uncertainty on slope stability assessments.…”

Section: Introductionmentioning

confidence: 99%

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The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

Gomes

Vrugt

Vargas

et al. 2017

Computers and Geotechnics

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a b s t r a c tWe investigate the uncertainty in bedrock depth and soil hydraulic parameters on the stability of a variably-saturated slope in Rio de Janeiro, Brazil. We couple Monte Carlo simulation of a threedimensional flow model with numerical limit analysis to calculate confidence intervals of the safety factor using a 22-day rainfall record. We evaluate the marginal and joint impact of bedrock depth and soil hydraulic uncertainty. The mean safety factor and its 95% confidence interval evolve rapidly in response to the storm events. Explicit recognition of uncertainty in the hydraulic properties and depth to bedrock increases significantly the probability of failure.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

Gomes

Vrugt

Vargas

et al. 2017

Computers and Geotechnics

View full text Add to dashboard Cite

show abstract

“…Deterministic interpolation techniques create a bedrock depth map from measured DTB observations, based on either the extent of similarity between nearby regolith depth observations or the degree of smoothing. Examples include the use of triangulated irregular networks [ Kim et al ., ], inverse distance weighting [ Stewart , ], and radial basis functions, and these approaches work well in the absence of spatial correlation between the measured regolith depth data [ Freer et al ., ; Wiegand et al ., ]. Geostatistical interpolation techniques capitalize on the spatial structure and semivariance of the measured bedrock depth data [ Goovaerts , ].…”

Section: Introductionmentioning

confidence: 99%

Toward improved prediction of the bedrock depth underneath hillslopes: Bayesian inference of the bottom‐up control hypothesis using high‐resolution topographic data

Gomes

Vrugt

Vargas

2016

Water Resources Research

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Toward improved prediction of the bedrock depth underneath hillslopes: Bayesian inference of the bottom-up control hypothesis using high-resolution topographic data Abstract The depth to bedrock controls a myriad of processes by influencing subsurface flow paths, erosion rates, soil moisture, and water uptake by plant roots. As hillslope interiors are very difficult and costly to illuminate and access, the topography of the bedrock surface is largely unknown. This essay is concerned with the prediction of spatial patterns in the depth to bedrock (DTB) using high-resolution topographic data, numerical modeling, and Bayesian analysis. Our DTB model builds on the bottom-up control on freshbedrock topography hypothesis of Rempe and Dietrich (2014) and includes a mass movement and bedrock-valley morphology term to extent the usefulness and general applicability of the model. We reconcile the DTB model with field observations using Bayesian analysis with the DREAM algorithm. We investigate explicitly the benefits of using spatially distributed parameter values to account implicitly, and in a relatively simple way, for rock mass heterogeneities that are very difficult, if not impossible, to characterize adequately in the field. We illustrate our method using an artificial data set of bedrock depth observations and then evaluate our DTB model with real-world data collected at the Papagaio river basin in Rio de Janeiro, Brazil. Our results demonstrate that the DTB model predicts accurately the observed bedrock depth data. The posterior mean DTB simulation is shown to be in good agreement with the measured data. The posterior prediction uncertainty of the DTB model can be propagated forward through hydromechanical models to derive probabilistic estimates of factors of safety.

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“…Past studies have focused on simulating various situations by modifying variables in spatial models [24][25][26]. The measures should also consider general adaptation plans to deal with climate change.…”

Section: The Effect Of Adaptationmentioning

confidence: 99%

Assessing the Cost of Damage and Effect of Adaptation to Landslides Considering Climate Change

2018

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Abstract:The amount of damage caused by landslides has increased due to climate change. Adaptation plans are required to help cope with landslides in order to reduce the extent of the damage. Landslide hazard analysis can help create adaptation plans. Analyzing the cost of damage of and the effect of adaptation to landslides is an effective way to support decision makers. The cost of damage is calculated using the costs of damage incurred in residential and transportation areas in the past, based on data from annual reports. Spatial distribution models are used to analyze landslide hazard areas in the present and the future. Future or potential landslide hazards are estimated by using climate change scenarios through representative concentration pathways. The effects of adaptation measures are assessed using modified variables and a cost-benefit analysis. The uncertainty of the cost of damage is considered using average, minimum, and maximum values. As a result, the methods used to estimate future costs of damage are developed, and the effects of adaptation are analyzed. The future cost of damage is calculated for every climate change scenario. The effect of adaptation are analyzed and areas with a reduced risk of landslides are identified, reducing the cost of damage and adaptation costs, as well as the costs and benefits of adaptation measures. Improving soil drainage is the most effective measure among the four measures analyzed. This study can help estimate future costs of damage and analyze the effect of adaptation in creating effective adaptation plans.

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Effect of topography and soil parameterisation representing soil thicknesses on shallow landslide modelling

Cited by 32 publications

References 68 publications

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

The role of uncertainty in bedrock depth and hydraulic properties on the stability of a variably-saturated slope

Toward improved prediction of the bedrock depth underneath hillslopes: Bayesian inference of the bottom‐up control hypothesis using high‐resolution topographic data

Assessing the Cost of Damage and Effect of Adaptation to Landslides Considering Climate Change

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