Quantifying uncertainty in stable isotope mixing models

Davis, Paul; Syme, James; Heikoop, Jeffrey; Fessenden-Rahn, Julianna; Perkins, George; Newman, Brent; Chrystal, Abbey E.; Hagerty, Shannon B.

doi:10.1002/2014JG002839

Title: Quantifying uncertainty in stable isotope mixing models

Journal Article · Tue May 19 00:00:00 EDT 2015 · Journal of Geophysical Research. Biogeosciences

DOI:https://doi.org/10.1002/2014JG002839· OSTI ID:1221608

Davis, Paul ^[1]; Syme, James ^[1]; Heikoop, Jeffrey ^[2]; Fessenden-Rahn, Julianna ^[2]; Perkins, George ^[2]; Newman, Brent ^[2]; Chrystal, Abbey E. ^[2]; Hagerty, Shannon B. ^[3]

EnviroLogic Inc., Durango, CO (United States)
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Univ. of California, Santa Barbara, CA (United States)

Mixing models are powerful tools for identifying biogeochemical sources and determining mixing fractions in a sample. However, identification of actual source contributors is often not simple, and source compositions typically vary or even overlap, significantly increasing model uncertainty in calculated mixing fractions. This study compares three probabilistic methods, SIAR [Parnell et al., 2010] a pure Monte Carlo technique (PMC), and Stable Isotope Reference Source (SIRS) mixing model, a new technique that estimates mixing in systems with more than three sources and/or uncertain source compositions. In this paper, we use nitrate stable isotope examples (δ¹⁵N and δ¹⁸O) but all methods tested are applicable to other tracers. In Phase I of a three-phase blind test, we compared methods for a set of six-source nitrate problems. PMC was unable to find solutions for two of the target water samples. The Bayesian method, SIAR, experienced anchoring problems, and SIRS calculated mixing fractions that most closely approximated the known mixing fractions. For that reason, SIRS was the only approach used in the next phase of testing. In Phase II, the problem was broadened where any subset of the six sources could be a possible solution to the mixing problem. Results showed a high rate of Type I errors where solutions included sources that were not contributing to the sample. In Phase III some sources were eliminated based on assumed site knowledge and assumed nitrate concentrations, substantially reduced mixing fraction uncertainties and lowered the Type I error rate. These results demonstrate that valuable insights into stable isotope mixing problems result from probabilistic mixing model approaches like SIRS. The results also emphasize the importance of identifying a minimal set of potential sources and quantifying uncertainties in source isotopic composition as well as demonstrating the value of additional information in reducing the uncertainty in calculated mixing fractions.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: AC52-06NA25396

OSTI ID:: 1221608

Report Number(s):: LA-UR-15-23883

Journal Information:: Journal of Geophysical Research. Biogeosciences, Vol. 120, Issue 5; ISSN 2169-8953

Publisher:: American Geophysical UnionCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 42 works

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