Stream solute tracers have been a popular tool to study transport and transformation of solutes through streams and their connected river corridors for decades. From an initial focus on travel times (“time of passage” studies) and advection-dispersion [e.g., Fischer et al., 1979], the toolkit for interpretation of these data has grown substantially. Current, popular techniques include advection-dispersion modeling [ibid], interpretation of holdback [Danckwerts, 1953], temporal moments [Harvey and Gorelick, 1995; Gupta and Cvetkovic, 2000], channel water balance [Payn et al., 2009], separation of mass involved in transient storage [Wlostowski et al., 2017], and StorAge Selection frameworks [Harman, 2015; Harman et al., 2016; Ward et al., 2019a]. Moreover, several models exist to interpret findings using an inverse modeling approach, including the popular Transient Storage Model [Bencala and Walters, 1983; Runkel, 1998], STAMMT-L [Haggerty and Reeves, 2002], and several continuous-time random-walk models [e.g., Boano et al., 2007]. My lab group currently has toolboxes built in Matlab to execute these analyses, has a track record of sharing our codebase for community use [Ward et al., 2017], and has produced an open-access educational module on HydroLearn to teach best practices in the design, execution, and interpretation of stream solute tracer studies.

Although the execution of a stream solute tracer study is conceptually straightforward, the interpretation of these data is considerably more nuanced. The techniques summarized above each require different mathematical techniques and may provide conflicting information if analyzed individually rather than holistically [Ward et al., 2019; Ward and Packman, 2019]. Moreover, the assumptions made by different researchers can significantly alter the interpretation of the exact same data set) [Drummond et al., 2012]. Thus, we propose to standardize the interpretation of stream solute tracer studies and provide context from other studies to aid in interpretation of data and analyses. By doing so, we will (1) provide data interpretation to users contributing their solute tracer results to the database; (2) aggregate a database of uniformly interpreted tracer studies that may aid informatics approaches to prediction in the future; (3) position HydroShare as the “go-to” data repository for stream solute tracer experiments; and (4) implement open-access training via the HydroLearn platform.