A Perspective on the Future of Transient Storage Modeling: Let's Stop Chasing Our Tails. Issue 3 (23rd March 2020)

• Record Type: Journal Article
• Authors: Knapp, Julia L.A.; Kelleher, Christa
• Is Part Of: Water resources research. Volume 56:Issue 3(2020)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2020-03-23
• Subjects: transient storage model -- tracer test -- parameter uncertainty -- exchange process -- storage zone -- model framework; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2019WR026257
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Due to its simplicity, adaptability, and accessibility, the transient storage model has become one of the most widely used tools to investigate transport and exchange processes in streams. In spite of its ubiquitous use, a number of challenges remain, in particular with respect to model conceptualization and interpretation. Are parameters good approximations of physical processes and properties? What is the relevance of parameter interactions in increasingly complex conceptualizations of the transient storage model? How does novel data inform, improve, or falsify our models? In this commentary, we illuminate several open questions and provide an outlook on how we can use the transient storage model to improve our understanding of solute transport. We propose the incorporation of novel and different data, the comparison of multiple models as working hypotheses for stream reach exchange, and call for a synthesis of the data collected and analyzed over the past years. Plain Language Summary: To learn about how water moves through streams, many scientists and practitioners use a simple mathematical model that describes how water—and chemicals in this water—is transported downstream and may interact with areas in and near the stream in which water and dissolved chemicals are stored for some time. This model has been used for more than 35 years, but we still have many open questions regarding how we can use this model to understand in‐ and near‐stream processes and what this model teaches us about how these processes may vary in different places. In this article, we provide some history on the application of this model and how it has been used, and some recommendations as to what type of research is needed in the future to advance our understanding of in‐ and near‐stream processes. Key Points: Integrating novel and different data can help constrain estimated parameters and inferred processes Assessing parameter uncertainties and comparing multiple models as working hypotheses can aid the prediction of transport processes Moving from case studies to syntheses of collected data can help to identify tuning parameters of river systems