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Physically-based landslide susceptibility models have been widely used to predict rainfall-triggered landslides. Calibration of these models is mainly conducted by tuning various inputs, but spatially varying soil properties are not considered. In this study, an efficient probabilistic model calibration method is proposed, which integrates discrete cosine transform (DCT) representation of spatial variability with Bayesian parameter estimation to characterise the spatial variation of soil properties in a physically-based landslide susceptibility model. The efficacy of DCT inversion in representing fields with binary information about landslide occurrence and non-occurrence is investigated. To illustrate the capability and feasibility of the proposed method, a hypothetical example and a case study of a 2014 regional rainfall-triggered landslide event in Qinglian Town, Chongqing, China are presented. The results demonstrate that the location, size and shape of landslide have little influence on the DCT inversion. Considering spatial variation in model calibration can significantly improve model prediction performance for regional rainfall-induced landslides. The topography of the landslide initiation area may affect the influence of the model calibration if the field observations do not agree with the fundamental physics underlying the stability equation used in the model.

Probabilistic model calibration of spatial variability for a physically-based landslide susceptibility model