Thomas Zieher from the section of Remote Sensing and Geomatics at IGF and his colleagues from the Austrian Research and Training Centre for Forests, University of Natural Resources and Life Sciences Vienna, and the University of Innsbruck published their new findings about physically-based modelling of shallow landslides in the journal Natural Hazards and Earth System Sciences (NHESS). The journal is an interactive open access journal, where the article is currently under discussion.

nat hazards

Zieher, T., Rutzinger, M., Schneider-Muntau, B., Perzl, F., Leidinger, D., Formayer, H., and Geitner, C.: Sensitivity analysis and calibration of a dynamic physically-based slope stability model, Nat. Hazards Earth Syst. Sci. Discuss., doi:10.5194/nhess-2017-73, in review, 2017.


Physically-based modelling of slope stability at catchment scale is still a challenging task. Applying a physically-based model at such 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) if parameters 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 multi-temporal 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, effective angle of internal friction, effective cohesion) 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 73.5 % 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 runoff 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.