'Investigating the impact of higher spatial resolution parameter information on stream solute transport predictions'
Noah Schmadel (firstname.lastname@example.org), Utah State University Justin Heavilin (email@example.com), Utah State University; Bethany Neilson (firstname.lastname@example.org), Utah State University; Anders Wörman (email@example.com), The Royal Institute of Technology, Stockholm
Transient storage processes in streams are well recognized as being spatially variable; however, this variability is commonly simplified by assuming reach averaged parameter values when predicting solute transport. Two main challenges arise when attempting to incorporate spatial variability into solute predictions: 1) observations may not be suited well for estimation of all relevant transport processes which makes characterizing the individual influences of storage parameters difficult, and 2) the resolution of spatial information necessary to appropriately capture the influence of variability is not clear. To address these challenges, we start by deriving analytical solutions to a two-zone solute transient storage model that accounts for both surface and hyporheic transient storage. Next, we evaluate temporal moments from these solutions to gain insight regarding the sensitivity of different storage parameters. Lastly, we use more spatially explicit parameter information acquired from high-resolution infrared imagery to better inform at what spatial resolution observations are necessary to capture bulk variability in transient storage processes. This was completed by convolving solutions of spatially distinct sub-reaches. We found that while reach averaged parameter values reasonably predict downstream solute concentrations, parameter sensitivity is highly dependent on appropriately incorporating spatial information. Further, a spatial resolution threshold was estimated through the convolution of solutions where no significant information was gained by increasing resolution beyond this threshold. This aids in determining the appropriate data collection resolution necessary to capture bulk variability.