TY - JOUR

T1 - Uncertainty estimation of end-member mixing using generalized likelihood uncertainty estimation (GLUE), applied in a lowland catchment

AU - Delsman, Joost R.

AU - Essink, Gualbert H. P. Oude

AU - Beven, Keith J.

AU - Stuyfzand, Pieter J.

N1 - ©2013. American Geophysical Union. All Rights Reserved.

PY - 2013/8

Y1 - 2013/8

N2 - 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.

AB - 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.

KW - end-member mixing

KW - lowland hydrology

KW - hydrograph separation

KW - GLUE

KW - MODELING STREAMWATER CHEMISTRY

KW - HYDROGRAPH SEPARATIONS

KW - GROUNDWATER-FLOW

KW - HYDROLOGICAL PATHWAYS

KW - MOUNTAINOUS CATCHMENT

KW - HEADWATER CATCHMENT

KW - WATER CHEMISTRY

KW - STORM RUNOFF

KW - TRACER

KW - IDENTIFICATION

U2 - 10.1002/wrcr.20341

DO - 10.1002/wrcr.20341

M3 - Journal article

VL - 49

SP - 4792

EP - 4806

JO - Water Resources Research

JF - Water Resources Research

SN - 0043-1397

IS - 8

ER -