POSSIBILITIES OF INCORPORATING AND VISUALISING UNCERTAINTY IN
NATURAL HAZARD PREDICTION MAPS
J. Trau, L. Hurni
ETH Zurich, Institute of Cartography, Zurich, Switzerland
trau@karto.baug.ethz.ch
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.