Sensitivity analysis and calibration of a dynamic physically based slope stability model

Abstract

Abstract. Physically based modelling of slope stability on a catchment scale is still a challenging task. When applying a physically based model on such a scale (1 : 10 000 to 1 : 50 000), parameters with a high impact on the model result should be calibrated to account for (i) the spatial variability of parameter values, (ii) shortcomings of the selected model, (iii) uncertainties of laboratory tests and field measurements or (iv) parameters that cannot be derived experimentally or measured in the field (e.g. calibration constants). While systematic parameter calibration is a common task in hydrological modelling, this is rarely done using physically based slope stability models. In the present study a dynamic, physically based, coupled hydrological-geomechanical slope stability model is calibrated based on a limited number of laboratory tests and a detailed multitemporal shallow landslide inventory covering two landslide-triggering rainfall events in the Laternser valley, Vorarlberg (Austria). Sensitive parameters are identified based on a local one-at-a-time sensitivity analysis. These parameters (hydraulic conductivity, specific storage, angle of internal friction for effective stress, cohesion for effective stress) are systematically sampled and calibrated for a landslide-triggering rainfall event in August 2005. The identified model ensemble, including 25 "behavioural model runs" with the highest portion of correctly predicted landslides and non-landslides, is then validated with another landslide-triggering rainfall event in May 1999. The identified model ensemble correctly predicts the location and the supposed triggering timing of 73.0 % of the observed landslides triggered in August 2005 and 91.5 % of the observed landslides triggered in May 1999. Results of the model ensemble driven with raised precipitation input reveal a slight increase in areas potentially affected by slope failure. At the same time, the peak run-off increases more markedly, suggesting that precipitation intensities during the investigated landslide-triggering rainfall events were already close to or above the soil's infiltration capacity.

Viewing Earth’s surface as a soft-matter landscape

“…In principle, the solid-state failure model and the sub-yield creeping 'flow' model are physically incompatible. In practice, these models require site-specific calibrations and parameterizations that limit their predictive power [121]. Rigidity transitions of this type are central concepts in softmatter physics [122], and it is possible to draw connections between the frictionless jamming transition [38] and rough frictional geophysical flows.…”

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

“…Thiebes, 2014, Tofani et al, 2017, Zieher et al, 2017 or using values from existing 5 databases, lookup tables or other published/unpublished sources (e.g. Schmidt et al, 2008, Kuriakose et al, 2009, Mergili et al, 2014b.…”

“…The most commonly applied modeling approach relies on the infinite-slope stability model which reduces the landslide geometry to a slope-parallel layer of infinite length and width. Modeling approaches that try to introduce more 5 complex landslide geometries in a GIS environment are generally outperformed by the infinite-slope stability model (Zieher et al, 2017). Consequently, parameters representing the landslide geometry assumed by the model (i.e.…”

“…Consequently, parameters representing the landslide geometry assumed by the model (i.e. slope angle and depth) are highly sensitive (Zieher et al, 2017). This means that the underlying model itself already performs some sort of averaging too since the precise landslide geometry cannot be adequately resolved in the infinite-slope stability model.…”

“…Neves Seefelder et al (2016) and Zieher et al (2017) identified slope angle as one of the most sensitive 15 modeling parameter in TRIGRS, which is not surprising since slope failures are in general associated with higher slope angles (Liao et al, 2011). Therefore, no matter what the geotechnical or hydraulic input parameters are, it will be always the same slope segments that will result the highest slope failure probability.…”