Does parameterization influence the performance of slope stability model results? A case study in Rio de Janeiro, Brazil

Seefelder, Carolina de Lima Neves; Koide, Sérgio; Mergili, Martin

doi:10.1007/s10346-016-0783-6

Cited by 32 publications

(29 citation statements)

References 55 publications

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“…3 and 5) underlines the important impact of slope angle, and thus DTM quality, on model outputs. Neves Seefelder et al (2016) and Zieher et al (2017) identified slope angle as one of the most sensitive modeling parameters in TRIGRS, which is not surprising since slope failures are in general associated with higher slope angles (Liao et al, 2011). Even under greatly varying geotechnical or hydraulic input parameter settings, the same slope segments experience the highest likelihood of slope failure.…”

Section: Discussionmentioning

confidence: 91%

“…(Seyfried and Wilcox, 1995;Fan et al, 2016). Neves Seefelder et al (2016) suggest applying rather broad ranges of parameters for physically based approaches to be on the "safe side" as they yield results comparable in quality to those derived with best-fit narrow ranges. By acknowledging the fact that geotechnical and hydrological parameterswhen working on larger areas -are highly variable, uncertain, and often poorly known, narrow parameter ranges or even singular combinations of parameters come with the risk of being off target (Neves Seefelder et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

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Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

Canli

Mergili

Thiebes

et al. 2018

Nat. Hazards Earth Syst. Sci.

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Abstract. Landslide forecasting and early warning has a long tradition in landslide research and is primarily carried out based on empirical and statistical approaches, e.g., landslidetriggering rainfall thresholds. In the last decade, flood forecasting started the operational mode of so-called ensemble prediction systems following the success of the use of ensembles for weather forecasting. These probabilistic approaches acknowledge the presence of unavoidable variability and uncertainty when larger areas are considered and explicitly introduce them into the model results. Now that highly detailed numerical weather predictions and high-performance computing are becoming more common, physically based landslide forecasting for larger areas is becoming feasible, and the landslide research community could benefit from the experiences that have been reported from flood forecasting using ensemble predictions. This paper reviews and summarizes concepts of ensemble prediction in hydrology and discusses how these could facilitate improved landslide forecasting. In addition, a prototype landslide forecasting system utilizing the physically based TRIGRS (Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability) model is presented to highlight how such forecasting systems could be implemented. The paper concludes with a discussion of challenges related to parameter variability and uncertainty, calibration and validation, and computational concerns.

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Section: Discussionmentioning

confidence: 91%

Section: Discussionmentioning

confidence: 99%

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

Canli

Mergili

Thiebes

et al. 2018

Nat. Hazards Earth Syst. Sci.

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“…Sliding surfaces most likely coincide spatially with geotechnically susceptible areas, layers or interfaces, spaced in a more or less irregular way. Even though much effort was put in sampling and testing, it appears hardly feasible to parameterize such patterns of localized patches of low soil strength, increased water input or increased hydraulic conductivity, or the effects of the vegetation (de Lima Neves Seefelder et al 2016). This means that either even more detailed studies including comprehensive material sampling and testing as well as regolith depth measurements, or appropriate techniques for a stochastic representation of the variability of the key parameters, are necessary for studies of single landslides or slopes.…”

Section: Discussionmentioning

confidence: 99%

Are real-world shallow landslides reproducible by physically-based models? Four test cases in the Laternser valley, Vorarlberg (Austria)

2017

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In contrast to the complex nature of slope failures, physically-based slope stability models rely on simplified representations of landslide geometry. Depending on the modelling approach, landslide geometry is reduced to a slope-parallel layer of infinite length and width (e.g., the infinite slope stability model), a concatenation of rigid bodies (e.g., Janbu's model), or a 3D representation of the slope failure (e.g., Hovland's model). In this paper, the applicability of four slope stability models is tested at four shallow landslide sites where information on soil material and landslide geometry is available. Soil samples were collected in the field for conducting respective laboratory tests. Landslide geometry was extracted from pre-and post-event digital terrain models derived from airborne laser scanning. Results for fully saturated conditions suggest that a more complex representation of landslide geometry leads to increasingly stable conditions as predicted by the respective models. Using the maximum landslide depth and the median slope angle of the sliding surfaces, the infinite slope stability model correctly predicts slope failures for all test sites. Applying a 2D model for the slope failures, only two test sites are predicted to fail while the two other remain stable. Based on 3D models, none of the slope failures are predicted correctly. The differing results may be explained by the stabilizing effects of cohesion in shallower parts of the landslides. These parts are better represented in models which include a more detailed landslide geometry. Hence, comparing the results of the applied models, the infinite slope stability model generally yields a lower factor of safety due to the overestimation of landslide depth and volume. This simple approach is considered feasible for computing a regional overview of slope stability. For the local scale, more detailed studies including comprehensive material sampling and testing as well as regolith depth measurements are necessary.

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“…Many deterministic approaches use the FoS to state whether a hillslope becomes unstable according to distinct physical parameters. Mergili et al (2014a) and De Lima Neves Seefelder et al (2016) emphasised that FoS-derived with a fixed set of geotechnical parameters-might fail to capture the details of a landscape. The wide range of root cohesion forces associated to different root systems would rather promote misinterpretations of the FoS due to the uncertainties associated with the dissimilarities of root distribution.…”

Section: Separated Vs Mixed Standsmentioning

confidence: 99%

Integration of root systems into a GIS-based slip surface model: computational experiments in a generic hillslope environment

Schmaltz

Mergili

2018

Landslides

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Integration of root systems into a GIS-based slip surface model: computational experiments in a generic hillslope environment Abstract Root systems of trees reinforce the underlying soil in hillslope environments and therefore potentially increase slope stability. So far, the influence of root systems is disregarded in Geographic Information System (GIS) models that calculate slope stability along distinct failure plane. In this study, we analyse the impact of different root system compositions and densities on slope stability conditions computed by a GIS-based slip surface model. We apply the 2.5D slip surface model r.slope.stability to 23 root system scenarios imposed on pyramidoid-shaped elements of a generic landscape. Shallow, taproot and mixed root systems are approximated by paraboloids and different stand and patch densities are considered. The slope failure probability (P f ) is derived for each raster cell of the generic landscape, considering the reinforcement through root cohesion. Average and standard deviation of P f are analysed for each scenario. As expected, the r.slope.stability yields the highest values of P f for the scenario without roots. In contrast, homogeneous stands with taproot or mixed root systems yield the lowest values of P f . P f generally decreases with increasing stand density, whereby stand density appears to exert a more pronounced influence on P f than patch density. For patchy stands, P f increases with a decreasing size of the tested slip surfaces. The patterns yielded by the computational experiments are largely in line with the results of previous studies. This approach provides an innovative and simple strategy to approximate the additional cohesion supplied by root systems and thereby considers various compositions of forest stands in 2.5D slip surface models. Our findings will be useful for developing strategies towards appropriately parameterising root reinforcement in real-world slope stability modelling campaigns.

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Does parameterization influence the performance of slope stability model results? A case study in Rio de Janeiro, Brazil

Cited by 32 publications

References 55 publications

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

Probabilistic landslide ensemble prediction systems: lessons to be learned from hydrology

Are real-world shallow landslides reproducible by physically-based models? Four test cases in the Laternser valley, Vorarlberg (Austria)

Integration of root systems into a GIS-based slip surface model: computational experiments in a generic hillslope environment

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