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Climate change impact on rainfall-induced landslide susceptibility of a certain region is often implied based on expected changes in rainfall patterns and rarely explicitly quantified. This study aims to address this gap by implementing coupled landslide and climate modelling chains to explicitly assess the effects of changing rainfall patterns on rainfall-induced landslide susceptibility. The effects of climate change are integrated into the landslide modelling chain via Intensity-Duration-Frequency (IDF) curves for the present and future climate conditions for a landslide-prone study area located in central Norway. The effects of climate change on landslide susceptibility are examined by using a physical-based landslide prediction model with rainfall events of varying duration and intensity that are simulated based on the climate-dependent IDF curves. The novelty of this study is the proposition of a novel probabilistic framework to assess the climate change impact on landslide susceptibility for rainfall events with a given duration. The proposed framework accounts for both the uncertainties of rainfall events through probabilistic interpretation of IDF curves and the uncertainties in the landslide model with the Monte Carlo method. Compared to results based on only intense rainfall events, the proposed framework leads to a lower increase in the probability of landslide initiation and landslide-susceptible extents due to climate change. [ABSTRACT FROM AUTHOR]

• Modeling the parameters of spatially-distributed landslide prediction models as spatially variable with random field models. • Development of a novel Bayesian framework for calibrating spatially variable parameters of physical-based landslide prediction models. • Development of approximate likelihood formulations for an efficient implementation of the Bayesian calibration framework. This study presents a novel Bayesian framework for statistical calibration of spatially distributed physical-based landslide prediction models. The calibration process is formulated in a statistical setting with the model parameters simulated as spatially variable with random fields and the model calibration defined within the Bayesian framework. The implementation of such calibration process is challenging due to large numbers of calibration parameters and high-dimensional likelihood functions, which are central in establishing a relation between observations and the corresponding model predictions. The former challenge was resolved by reformulating the Bayesian updating problem as an equivalent reliability problem and solving it with efficient reliability methods. The latter challenge was resolved by developing novel lower-dimensional approximate likelihood formulations, suitable for the interpretation of landslide initiation zones, based on the Approximate Bayesian Computation method. The novelties of the proposed approach stem from describing landslide model parameters as spatially variable, development of a statistical framework to calibrate landslide prediction models, and introduction of approximate likelihood formulations. [ABSTRACT FROM AUTHOR]