Abstract
Abstract. Streamflow recession has been investigated by a variety of methods, often involving the fit of a model to empirical recession plots to parameterize a non-linear storage–outflow relationship based on the dQ/dt−Q method. Such recession analysis methods (RAMs) are used to estimate hydraulic conductivity, storage capacity, or aquifer thickness and to model streamflow recession curves for regionalization and prediction at the catchment scale. Numerous RAMs have been published, but little is known about how comparably the resulting recession models distinguish characteristic catchment behavior. In this study we combined three established recession extraction methods with three different parameter-fitting methods to the power-law storage–outflow model to compare the range of recession characteristics that result from the application of these different RAMs. Resulting recession characteristics including recession time and corresponding storage depletion were evaluated for 20 meso-scale catchments in Germany. We found plausible ranges for model parameterization; however, calculated recession characteristics varied over two orders of magnitude. While recession characteristics of the 20 catchments derived with the different methods correlate strongly, particularly for the RAMs that use the same extraction method, not all rank the catchments consistently, and the differences among some of the methods are larger than among the catchments. To elucidate this variability we discuss the ambiguous roles of recession extraction procedures and the parameterization of the storage–outflow model and the limitations of the presented recession plots. The results suggest strong limitations to the comparability of recession characteristics derived with different methods, not only in the model parameters but also in the relative characterization of different catchments. A multiple-methods approach to investigating streamflow recession characteristics should be considered for applications whenever possible.
Highlights
Recession analysis methods (RAMs) are widely used to investigate the storage–outflow relationship of catchments
Low flow at the catchment scale is examined with baseflow separation techniques, low-flow frequency analysis, low-flow indices and recession analysis methods, which have been comprehensively reviewed by Hall (1968), Tallaksen (1995), Smakhtin (2001) and Dewandel et al (2003)
We tested the effect of different recession analysis methods to distinguish recession characteristics in a regional set of streamflow records caused by particular catchment characteristics
Summary
Recession analysis methods (RAMs) are widely used to investigate the storage–outflow relationship of catchments. As in rainless periods streamflow originates solely from stored water in a catchment (aquifers, soils, lakes, etc.), the shapes of these recession curves should be characteristic for a specific catchment. If this is the case, they could be used for low-flow prediction and estimation of total dynamic storage. Anderson and Burt (1980) have shown that graphical plotting techniques can lead to biased recession characteristics and even semi-logarithmic plotting is more appropriate to describe single recession events than a general storage–outflow behavior. To analyze streamflow recessions individually instead of collectively ignores the variability of storage depletion, which is represented by numerous recession events and not by one single event
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