Brief communication: Visualizing uncertainties in landslide susceptibility modeling using bivariate mapping

Abstract. Effectively communicating uncertainties inherent to statistical models is a challenging yet crucial aspect of the modeling process. This is particularly important in applied research, where output is used and interpreted by scientists and decision makers alike. In disaster risk reduction, susceptibility maps for natural hazards are vital for spatial planning and risk assessment. We present a novel type of landslide susceptibility map that jointly visualizes the estimated susceptibility and the corresponding prediction uncertainty, using an example from a mountainous region in Carinthia, Austria. We also provide implementation guidelines to create such maps using popular free and open-source software packages.