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• W2920229929 abstract "Abstract Streamflow recession analysis characterizes the storage‐outflow relationship in catchments. This relationship, which typically follows a power law, summarizes all catchment‐scale subsurface hydrological processes and has long been known to be a key descriptor of the hydrologic response. In this paper, we tested a range of common recession analysis methods (RAMs) and propose the use of an analytic streamflow distribution model as an alternative method for recession parameter estimation and to objectively compare different RAMs. The used analytical model assumes power law recessions, in combination with a stochastic process for streamflow triggering rainfall events. This streamflow distribution model is used in the present framework to establish reference values for the recession parameters via maximum likelihood estimation. The model‐based method has two main advantages: (a) joint estimation of both power law recession parameters (coefficient and exponent), which are known to be strongly correlated, and (b) parameter estimation based on all available streamflow data (no recession selection). The approach is applied to five rainfall‐dominated catchments in Switzerland with 40 years of continuous streamflow observations. The results show that the estimated recession parameters are highly dependent on methodological choices and that some RAMs lead to biased estimates. The recession selection method is shown to be of prime importance for a reliable description of catchment‐scale recession behaviour, in particular in presence of short streamflow records. The newly proposed model‐based RAM yields robust results, which supports the further development of this method for comparative hydrology and opens new perspectives for understanding the recession behaviour of catchments." @default.
• W2920229929 created "2019-03-11" @default.
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• W2920229929 date "2019-04-24" @default.
• W2920229929 modified "2023-10-18" @default.
• W2920229929 title "Estimation of streamflow recession parameters: New insights from an analytic streamflow distribution model" @default.
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• W2920229929 doi "https://doi.org/10.1002/hyp.13425" @default.
• W2920229929 hasPublicationYear "2019" @default.
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