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Automated calibration of a stream solute transport model: implications for interpretation of biogeochemical parameters

Durelle T. Scott, Michael N. Gooseff, Kenneth E. Bencala and Robert L. Runkel
Journal of the North American Benthological Society
Vol. 22, No. 4 (December 2003), pp. 492-510
DOI: 10.2307/1468348
Stable URL: http://www.jstor.org/stable/1468348
Page Count: 19
Subjects: Aquatic Sciences Ecology & Evolutionary Biology Biological Sciences
Find more content in these subjects: Aquatic Sciences Ecology & Evolutionary Biology Biological Sciences
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Abstract

AbstractThe hydrologic processes of advection, dispersion, and transient storage are the primary physical mechanisms affecting solute transport in streams. The estimation of parameters for a conservative solute transport model is an essential step to characterize transient storage and other physical features that cannot be directly measured, and often is a preliminary step in the study of reactive solutes. Our study used inverse modeling to estimate parameters of the transient storage model OTIS (One dimensional Transport with Inflow and Storage). Observations from a tracer injection experiment performed on Uvas Creek, California, USA, are used to illustrate the application of automated solute transport model calibration to conservative and nonconservative stream solute transport. A computer code for universal inverse modeling (UCODE) is used for the calibrations. Results of this procedure are compared with a previous study that used a trial-and-error parameter estimation approach. The results demonstrated 1) importance of the proper estimation of discharge and lateral inflow within the stream system; 2) that although the fit of the observations is not much better when transient storage is invoked, a more randomly distributed set of residuals resulted (suggesting nonsystematic error), indicating that transient storage is occurring; 3) that inclusion of transient storage for a reactive solute (Sr2+) provided a better fit to the observations, highlighting the importance of robust model parameterization; and 4) that applying an automated calibration inverse modeling estimation approach resulted in a comprehensive understanding of the model results and the limitation of input data.

Notes and References

This item contains 44 references.

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