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

Abstract

Effectively communicating uncertainties inherent to statistical models is a challenging yet crucial aspect of the modelling 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.