Long-term persistence in discharge time series of mountainous catchments in the Elbe River basin

Statistical detection of trends in hydrometeorological time series is a crucial task when revealing how river systems react to environmental and human-induced changes. It was shown that the autocorrelation structure of a series influences the power of parametric and nonparametric trend tests. While the order of short-memory processes can be sufficiently captured by AR(I)MA models, the determination of the Hurst exponent, which describes the long memory, is still challenging, considering that the available methods partially give different results. In the Elbe River basin, Europe, several studies focusing on the detection (or description) of long-term persistence were performed. However, different lengths of series and different methods were used. The aim of the present work is to gather the results gained in various parts of the Elbe basin in Central Europe and to compare them with our estimation of the Hurst exponent using six discharge series observed in selected subbasins. Instead of the dependence of the exponent on the catchment area suggested by the theory of aggregated short-memory processes, we rather found a relationship between this parameter and the series length. As the theory is not supported by our findings, we suppose that the Hurst phenomenon is caused by a complex interplay of low-frequency climate variability and catchment processes. Experiments based on distributed water balance models should be the further research objective, ideally under the umbrella of mutual international projects.