Uncertainty estimation of end-member mixing using generalized likelihood uncertainty estimation (GLUE), applied in a lowland catchment
2013 (English)In: Water resources research, ISSN 0043-1397, E-ISSN 1944-7973, Vol. 49, no 8, 4792-4806 p.Article in journal (Refereed) Published
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.
Place, publisher, year, edition, pages
2013. Vol. 49, no 8, 4792-4806 p.
end-member mixing, lowland hydrology, hydrograph separation, GLUE
IdentifiersURN: urn:nbn:se:uu:diva-210265DOI: 10.1002/wrcr.20341ISI: 000324838300021OAI: oai:DiVA.org:uu-210265DiVA: diva2:661837