Ordinal pattern dependence between hydrological time series

Ordinal patterns provide a method to measure dependencies between time series. In contrast to classical correlation measures like the Pearson correlation coefficient they are able to measure not only linear correlation but also non-linear correlation even in the presence of non-stationarity. Hence, they are a noteworthy alternative to the classical approaches when considering discharge series. Discharge series naturally show a high variation as well as single extraordinary extreme events and, caused by anthropogenic and climatic impacts, non-stationary behaviour. Here, the method of ordinal patterns is used to compare pairwise discharge series derived from macro- and mesoscale catchments in Germany. Differences of coincident groups were detected for winter and summer annual maxima. Hydrological series, which are mainly driven by annual climatic conditions (yearly discharges and low water discharges) showed other and in some cases surprising interdependencies between macroscale catchments. Anthropogenic impacts as the construction of a reservoir or different flood conditions caused by urbanization could be detected.