We retrieve descriptors of the terrain morphology from a digital elevation model of a 105 km² study area and blend them through decision tree models to map flood susceptibility and expected water depth. We investigate this approach with particular attention to (a) the comparison with a selected single-descriptor approach, (b) the goodness of decision trees, and (c) the performance of these models when applied to data-scarce regions. We find promising pathways for future research.