| Abstract: | The stream tracer technique and transient storage models (TSMs) have become common tools in stream solute and hyporheic exchange studies. The expense and logistics associated with water sample collection and analysis often results in limited temporal resolution of stream tracer breakthrough curves (BTCs). Samples are often collected without a priori or real‐time knowledge of BTC information, which can result in poor sample coverage of the critical shoulder (initial rise) and tail (post‐steady state fall) of the BTC. We illustrate the use of specific conductance (SC) measurements as a surrogate for conservative dissolved tracer (Br?) samples. The advantages of collecting SC data for use in the TSM are (1) cost, (2) ease of data collection, and (3) well‐defined breakthrough curves, which strengthen TSM parameter optimization. This method is based on developing an ion concentration (IC)–SC relationship from limited discrete tracer solute samples. SC data can be collected on a more frequent basis at no additional analysis cost. TSM simulations can then be run for the conservative tracer data derived from SC breakthrough curves and the IC–SC relationship. This technique was tested in a 120 m reach of stream (2–60 m subreaches) in the Maimai M15 catchment, New Zealand during baseflow recession. Dissolved LiBr was injected for 12·92 h, with Br? as the conservative ion of interest. Four TSM simulations using the OTIS model are optimized using UCODE to fit (1) Br? data derived from the Br?–SC relationship (n = 1307 observations at each of two stream sampling sites), (2) all stream Br? data collected (n = 58 in upper reach, n = 60 in lower reach), (3) half of the stream Br? data collected, and (4) 20 stream Br? samples from each site. No two simulations resulted in the same optimal parameter values. Results suggest that the greater the frequency of observations, the greater the confidence in estimated parameter values. Br?–SC simulations resulted in the best overall model fits to the data, with the lowest calculated error variance of 6·37, narrowest 95% parameter estimate confidence intervals, and the highest correlation coefficient of 0·99 942, among the four simulations. This is largely due to the improved representation of the shoulder and tail of the BTC with this method. The IC–SC correlation method is robust in situations in which (1) changes in background SC data can be accounted for, and (2) the data used to define the IC–SC relationship are representative of the range of data collected. This method provides more efficient sample analysis, improved data resolution, and improved model results compared to the alternative stream tracer data gathering methods presented. Additionally, we describe a new parameterization of the cross‐sectional area of the stream during flow recession, as a function of discharge, based on a stream hydraulic geometry relationship. This variant of the OTIS model provides a more realistic representation of stream dynamics during unsteady discharge. Copyright © 2005 John Wiley & Sons, Ltd. |
