Comment on hess-2022-149_3

Abstract

The transport of solutes in river networks is controlled by the interplay of processes such as in-stream solute transport and the exchange of water between the stream channel and dead zones, in-stream sediments, and the hyporheic zone. Transient storage models (TSMs) are a powerful tool for testing hypotheses related to solute transport in streams. However, TSM parameters are often non-identifiable leading to an unclear understanding of the processes controlling solute transport in streams. In this study, we increased parameter identifiability in a set of tracer breakthrough experiments by combining global identifiability analysis and dynamic identifiability analysis. We compared our results to inverse modelling approaches (OTIS-P) and the commonly used random sampling approach for TSMs (OTIS-MCAT). Compared to OTIS-P, our results informed about sensitivity and identifiability of TSM parameters on the entire feasible parameter space. Our results clearly improved parameter identifiability compared to OTIS-MCAT that often indicated non-identifiability of TSM parameters. Non-identifiable results led to wrong solute retention times in the storage zone and the exchange flow with the storage zone, with a difference respectively up to four and two orders of magnitude compared to results with identifiable TSM parameters. The severe differences in the transport metrics between results obtained from our proposed approach and results from OTIS-MCAT model also resulted in contrasting interpretation of the hydrologic processes controlling solute transport at the study site. Thus, our outcomes point to the risks of interpreting TSM results when even one of the TSM parameters is non-identifiable. Our results showed that there is clear potential for increasing parameter identifiability in random sampling approaches for TSMs and for advancing our understanding of hydrological processes controlling in-stream solute transport.