News & Events

Grad student talk - Stream tracer breakthrough curve decomposition into mass fractions: A simple...

Thursday, October 20, 2016, 12:30PM - 1:30PM

Speaker

Adam Wlostowski

Location:

SEEC room S225

Full title

Stream tracer breakthrough curve decomposition into mass fractions: A simple framework to analyze and compare conservative solute transport processes

Abstract

Ecological functions are coupled to the physical transport of water and solutes in streams. Transport of conservative tracers in lotic systems is subject to the processes of advection, dispersion, transient storage, and mass loss to groundwater. Stream tracer experiments and the simulation of observed tracer breakthrough curve (BTC) data with 1-D numerical transient storage models (TSMs) are commonly used to quantify these processes. Results from TSMs can be useful, but issues related to model appropriateness and parameter identifiability suggest a need for conceptually simpler approaches to BTC analyses. We present a new approach to analyze BTCs to quantify the amount of stream water transported by each dominant process. BTCs are analyzed to parse the total quantity of injected tracer mass into three dominant process domains: advection and dispersion, transient storage, and gross loss of tracer. This method can be used to quantify the relative influence of transport processes within, and among streams, and estimate the proportion of stream water associated with each mode of transport. As proof of concept, we apply this approach to conservative tracer injections on two streams of contrasting morphology. Application of this method indicates that transport of injected solute mass in an alluvial stream is dominated by advection and dispersion, relative to a beaded, peat-bottomed stream, where more tracer mass transport was found to be associated with longer timescale transient storage processes. This approach provides a simple, inexpensive, and useful quantification of dominant transport processes and provides an additional tool for analyzing experimental BTC data.