Catchments as non-linear filters: understanding catchment similarities for the regionalisation of rainfall-runoff transformation processes using wavelets

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Discharge prediction in catchments with no or only a few discharge measurements - so-called ungauged catchments - is emerging as a major scientific and socioeconomic challenge. Currently available hydrological models are difficult to transfer from gauged to ungauged catchments due to an insufficient understanding of the hydrological processes underlying the discharge formation at the catchment scale. Such a spatial transfer of hydrological models is possible under the assumption that similar landscape units produce similar hydrological processes. The objective of the present research is to investigate this assumption and to propose a new approach for the quantification of hydrological landscape similarity. This similarity measure will be based on the assumption that different landscapes have different non-linear filtering characteristics during the rainfall-runoff transformation process and that these characteristics can be quantified. The filtering effects will be identified through signal processing techniques, namely wavelet analysis. Wavelets enable a quantitative analysis of how certain frequency features of a system input time series are filtered in the system output time series. In the present research, this main property will be exploited for the investigation of the filtering characteristics of different landscape units. A similarity measure based on such dynamic characteristics has the main advantage of connecting directly the hydrological response of a catchment or landscape unit to the processes that condition this response. It contributes therefore not only to the modelling of the hydrological response but to its understanding. The efficiency of the new approach and the gained insights will be validated through a discharge regionalisation study with data sets from the German part of the Rhine catchment.