Parameterizing a physically based shallow landslide model in a data poor region

Shallow landslides and consequent debris flows are an increasing concern in the Western Ghats of Kerala, India. Their increased frequency has been associated with deforestation and unfavourable land‐use practices in cultivated areas. In order to evaluate the influence of vegetation on shallow slope failures a physically based, dynamic and distributed hydrological model (STARWARS) coupled with a probabilistic slope stability model (PROBSTAB) was applied to the upper Tikovil River basin (55·6 km²). It was tuned with the limited evidence of groundwater conditions during the monsoon season of 2005 and validated against observed landslide activity in the hydrological year 2001–2002. Given the data poor conditions in the region some modifications to the original model were in order, including the estimation of parameters on the basis of generalized information from secondary sources, pedo‐transfer functions, empirical equations and satellite remote sensing data. Despite the poor input, the model captured the general temporal and spatial pattern of instability in the area. Sensitivity analysis proved root cohesion, soil depth and angle of internal friction as the most dominant parameters influencing slope stability. The results indicate the importance of root cohesion in maintaining stability and the critical role of the management of rubber plantations in this. Interception and evapotranspiration showed little influence on the development of failure conditions. The study also highlights the importance of high resolution digital terrain models for the accurate mechanistic prediction of shallow landslide initiation. Copyright © 2009 John Wiley & Sons, Ltd.     

Strategies to improve the explanatory power of a dynamic slope stability model by enhancing land cover parameterisation and model complexity

Despite the importance of land cover on landscape hydrology and slope stability, the representation of land cover dynamics in physically based models and their associated ecohydrological effects on slope stability is rather scarce. In this study, we assess the impact of different levels of complexity in land cover parameterisation on the explanatory power of a dynamic and process-based spatial slope stability model. Firstly, we present available and collected data sets and account for the stepwise parameterisation of the model. Secondly, we present approaches to simulate land cover: 1) a grassland landscape without forest coverage; 2) spatially static forest conditions, in which we assume limited knowledge about forest composition; 3) more detailed information of forested areas based on the computation of leaf area development and the implementation of vegetation-related processes; 4) similar to the third approach but with the additional consideration of the spatial expansion and vertical growth of vegetation. Lastly, the model is calibrated based on meteorological data sets and groundwater measurements. The model results are quantitatively validated for two landslide-triggering events that occurred in Western Austria. Predictive performances are estimated using the Area Under the receiver operating characteristic Curve (AUC). Our findings indicate that the performance of the slope stability model was strongly determined by model complexity and land cover parameterisation. The implementation of leaf area development and land cover dynamics further yield an acceptable predictive performance (AUC ~0.71-0.75) and a better conservativeness of the predicted unstable areas (FoC ~0.71). The consideration of dynamic land cover expansion provided better performances than the solely consideration of leaf area development. The results of this study highlight that an increase of effort in the land cover parameterisation of a dynamic slope stability model can increase the explanatory power of the model. © 2018 The Authors. Earth Surface Processes and Landforms published by John Wiley & Sons Ltd.