| Digital spatial data always imply some kind of uncertainty. The source of
this uncertainty can be found in their compilation as well as the conceptual
design that causes a more or less exact abstraction of the real world,
depending on the scale under consideration. Within the framework of
hydrological modelling, in which numerous data sets from diverse sources of
uneven quality are combined, the various uncertainties are accumulated.
In this study, the GROWA model is taken as an example to examine the effects
of different types of uncertainties on the calculated groundwater recharge.
Distributed input errors are determined for the parameters' slope and aspect
using a Monte Carlo approach. Landcover classification uncertainties are
analysed by using the conditional probabilities of a remote sensing
classification procedure. The uncertainties of data ensembles at different
scales and study areas are discussed.
The present uncertainty analysis showed that the Gaussian error propagation
method is a useful technique for analysing the influence of input data on
the simulated groundwater recharge. The uncertainties involved in the land
use classification procedure and the digital elevation model can be
significant in some parts of the study area. However, for the specific model
used in this study it was shown that the precipitation uncertainties have
the greatest impact on the total groundwater recharge error. |