A new method for testing time series of environmental data for internal inconsistencies is presented. The method divides the dataset into several disjunct blocks. By means of a comparison of the blocks' estimated probability density distributions, each block is compared with the others. In order to judge the differences, four different measures are used and compared: Kullback-Leibler Divergence, Jensen-Shannon Divergence, Earth Mover's Distance and the Root Mean Square. By looking at the resulting patterns, conclusions on possible inconsistencies in the data can be drawn. This paper shows some sensitivitiy tests and gives an example for an application to real data. Furthermore, it is shown, in which cases of errors (shift in mean, shift in variance and rounding), which measure performs best.