Exploring the Effects of Tracer Mass and Detection Limits in Stream Experiments With Instantaneous Tracer Injections on Solute Transport

ABSTRACT

Slug tracer experiments have greatly advanced our understanding of transport processes in streams. However, the resulting breakthrough curves (BTCs) derived from these experiments are biassed towards faster flow paths, partly due to truncation of BTCs caused by sensor detection limits. Although this issue is acknowledged, little is known about how BTC truncation from detection limits depends on applied tracer mass or stream characteristics. To address this gap, we conduct a virtual experiment with 120 000 virtual streams, simulating BTCs using the transient storage model with two virtual tracers differing in detection limits and sensor type. We test four approaches that differ in the applied tracer mass and evaluate how truncation affects stream transport metrics (normalised transient storage index, coefficient of variation, skewness, and holdback). Our results show that higher tracer mass consistently yields higher values across all transport metrics, underlining the interplay between tracer mass, BTC truncation, and measurement outcome. In particular, experiments applying higher tracer masses in streams with larger transient storage and higher dispersion yield higher transport metrics, which suggests that maximising the applied tracer mass is critical for describing transport processes in such streams. Overall, our findings reveal that the applied tracer mass rather than sensor detection limits alone, plays a key role in the truncation of BTCs and the resulting bias towards faster flow paths. To reduce the bias of slug tracer experiments towards faster flow and improve the accuracy of transport metrics in streams with larger transient storage and higher dispersion we recommend: (I) maximising the injected tracer mass, (II) use sensors with a lower detection limits, and (III) extend experiment duration.