Uncertainty estimation of end‐member mixing using generalized likelihood uncertainty estimation (GLUE), applied in a lowland catchment

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End‐member mixing models have been widely used to separate the different components of a hydrograph, but their effectiveness suffers from uncertainty in both the identification of end‐members and spatiotemporal variation in end‐member concentrations. In this paper, we outline a procedure, based on the generalized likelihood uncertainty estimation (GLUE) framework, to more inclusively evaluate uncertainty in mixing models than existing approaches. We apply this procedure, referred to as G‐EMMA, to a yearlong chemical data set from the heavily impacted agricultural Lissertocht catchment, Netherlands, and compare its results to the “traditional” end‐member mixing analysis (EMMA). While the traditional approach appears unable to adequately deal with the large spatial variation in one of the end‐members, the G‐EMMA procedure successfully identified, with varying uncertainty, contributions of five different end‐members to the stream. Our results suggest that the concentration distribution of “effective” end‐members, that is, the flux‐weighted input of an end‐member to the stream, can differ markedly from that inferred from sampling of water stored in the catchment. Results also show that the uncertainty arising from identifying the correct end‐members may alter calculated end‐member contributions by up to 30%, stressing the importance of including the identification of end‐members in the uncertainty assessment.