  The reliability of surface-based electrical resistivity tomography (ERT) for
quantifying resistivities for shallow subsurface water processes is analysed. A
method comprising numerical simulations of water movement in soil and
forward-inverse modeling of ERT surveys for two synthetic data sets is
presented. Resistivity contrast, e.g. by changing water content, is shown to
have large influence on the resistivity quantification.
  An ensemble and clustering approach is introduced in which ensembles of 50
different inversion models for one data set are created by randomly varying the
parameters for a regularisation based inversion routine. The ensemble members
are sorted into five clusters of similar models and the mean model for each
cluster is computed. Distinguishing persisting features in the mean models from
singular artifacts in individual tomograms can improve the interpretation of
inversion results.
  Especially in the presence of large resistivity contrasts in high sensitivity
areas, the quantification of resistivities can be unreliable. The ensemble
approach shows that this is an inherent problem present for all models inverted
with the regularisation based routine. The results also suggest that the
combination of hydrological and electrical modeling might lead to better
results.
