J. Trau, L. Hurni

ETH Zurich, Institute of Cartography, Zurich, Switzerland



This contribution discusses the possibilities of incorporating and visualising uncertainties in natural hazard maps. Including uncertainties associated with prediction maps of future natural hazards such as mass movements, floods, and avalanches might increase the usefulness of natural hazard prediction map to decision makers in land-use planning.

Today hazard prediction maps are usually created by applying a bivariate hazard matrix (magnitude-frequency diagram) to transfer hazard analyses results into a number of classes (e.g. 10 in Switzerland). Each class represents a certain magnitude and likelihood of occurrence of a hazardous process. The hazard classes are then applied to the study area which will then be divided into discrete zones. To a certain degree this approach already accounts for the uncertainties inherent in the analysis of natural hazards as it presents its outputs in ranges and not in absolute values. However, a user of this map product might want to know how valid and reliable boundaries between the displayed hazard classes are. In the conventional way of representation these suggest a sharp and sudden change in magnitude-frequency although the transition might be rather continuous and furthermore they are subject to uncertainties. Also, areas inside a hazard class are not homogeneous as one might assume from a cursory consultation of such a hazard prediction map. Therefore the uncertainties of prediction maps should be assessed and there are different types of them intrinsic to a hazard analyses. These uncertainties can be introduced into the analysis during all its stages - data acquisition, data transformation, and visualization (Pang et al. 1997). This paper aims at indicating and applying various techniques of uncertainty visualization to natural hazard prediction maps. This can be achieved by two approaches. First, by using the maps compared concept in which the uncertainty is treated as another variable and displayed as another "layer" i.e. by using uncertainty glyphs or transparency maps. And second, the maps combined approach in which the uncertainty is dealt with as an integral part of the data and displayed combined with the data i.e. by employing broken contours (Pang 2001, Slocum et al. 2005). Of the seven categories of data quality (Guptil and Morrison 1995) the two which are thought to be the most important ones in hazard mapping are positional and attribute accuracy. These will be focussed on in this paper.