Journal
JOURNAL OF HYDROLOGIC ENGINEERING
Volume 9, Issue 4, Pages 319-324Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)1084-0699(2004)9:4(319)
Keywords
neural networks; streams; tracers; contaminants; hydrologic data
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Tracer experiments are widely used tools in field studies on solute transport in streams. In addition, numerous injections are. carried out each year as part of the so-called salt dilution technique of stream gauging, producing a large body of data (in principle) available for analysis, and most of these latter experiments are instantaneous slug releases. Interpretation of the observed breakthrough curves is often done with the aid of either the transient storage (TS) or the advection-dispersion (AD) model, the latter being a subset or simplification of the former. The work reported here aimed at the development of a criterion to determine when the use of the TS model is required to explain a given breakthrough curve and in what circumstances the AD model can be expected to produce comparable results. The transient storage (TS) or dead zone model was solved for 216 parameter combinations, believed to cover a large portion of the situations possibly encountered in practice. The advection-dispersion (AD) model was then calibrated to reproduce the TS breakthrough curves as closely as possible. The outcome was judged to determine whether a particular breakthrough curve was unique to the TS model or could have been produced by the AD model as well. Criteria in terms of Damkohler index, DaI, and skewness coefficient, G(t), have been derived, and an application to a real world case is given. Use of an artificial neural network to improve the diagnostics is demonstrated.
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