Exploring tracer information in a small stream to improve parameter identifiability and enhance the process interpretation in transient storage models

Abstract. The transport of solutes in river networks is controlled by the interplay processes such as in-stream solute and exchange water between stream channel dead zones, sediments, adjacent groundwater bodies. Transient storage models (TSMs) are a powerful tool for testing hypotheses related to streams. However, model parameters often do not show univocal increase performances certain parameter range (i.e. they non-identifiable), leading an unclear understanding controlling In this study, we increased identifiability set tracer breakthrough experiments combining global analysis dynamic iterative approach. We compared our results inverse modelling approaches (OTIS-P) commonly used random sampling approach TSMs (OTIS-MCAT). Compared OTIS-P, informed about entire feasible range. Our clearly improved standard OTIS-MCAT application, due progressive reduction investigated with iteration. Non-identifiable led retention times zone flow differences up 4 2 orders magnitude identifiable respectively. clear metrics obtained from proposed classic also resulted contrasting interpretations hydrologic headwater western Luxembourg. Thus, outcomes point risks interpreting TSM when even one non-identifiable. showed that coupling TSMs. results, was effective at obtaining higher accuracy evaluated metrics, which advancing hydrological control transport.