Conservative solute transport processes and associated transient storage mechanisms: Comparing streams with contrasting channel morphologies, land use and land cover

Abstract

Land use within a watershed impacts stream channel morphology and hydrology and, therefore, in‐stream solute transport processes and associated transient storage mechanisms. This study evaluated transport processes in two contrasting stream sites where channel morphology was influenced by the surrounding land use, land cover, climate and geologic controls: Como Creek, CO, a relatively undisturbed, high gradient, forested stream with a gravel bed and complex channel morphology, and Clear Creek, IA, an incised, low‐gradient stream with low‐permeability substrate draining an agricultural landscape. We performed conservative stream tracer injections at these sites to address the following questions: (1) How does solute transport vary between streams with differing morphologies? and (2) How does solute transport at each stream site change as a function of discharge? We analysed in‐stream tracer time series data and compared results quantifying solute attenuation in surface and subsurface transient storage zones. Significant trends were observed in these metrics with varying discharge conditions at the forested site but not at the agricultural site. There was a broad range of transport mechanisms and evidence of substantial exchange with both surface and hyporheic transient storage in the relatively undisturbed, forested stream. Changing discharge conditions activated or deactivated different solute transport mechanisms in the forested site and greatly impacted advective travel time. Conversely in the simplified agricultural stream, there was a narrow range of solute transport behaviour across flows and predominantly surface transient storage at all measured discharge conditions. These results demonstrate how channel simplification inhibits available solute transport mechanisms across varying discharge conditions.

Physical and stoichiometric controls on stream respiration in a headwater stream

“…In our study site, 𝜏 𝑡𝑠 decreased one order of magnitude from round 1 to round 2, and were comparable to the range of values observed in other studies involving forested mountain streams(Valett et al 1996;Hall et al 2002). Due to the geomorphology of the stream, which is characterized by steep longitudinal and valley slopes, pool and riffle sequences, and shallow bedrock, transient storage was expected to occur mainly in the main channel(Fields and Dethier 2019;Barnhart et al 2021;Emanuelson et al 2022). As flow receded from round 1 to round 2, we observed the disconnection of in-stream pools contributing to transient storage, which explains the direct correlation between discharge and transient storage timescales.…”

“…Our results indicate that the transformation of Raz (𝜆 𝑅𝑎𝑧 ) was directly and moderately correlated with the transient storage timescale (𝜏 𝑡𝑠 ), as other studies on reactive transport have shown (Valett et al 1996 (Zarnetske et al 2007;Schmid et al 2010), 𝜏 𝑡𝑠 in our study site increased with 𝑄 because the geomorphology of the channel and the valley favored in-stream transient storage in pools (Jackson et al 2012(Jackson et al , 2013(Jackson et al , 2015, and the metabolically active biofilms available there may have prompted the transformation of Raz (Haggerty et al 2014;Peralta-Maraver et al 2018). Consistently, when stream flows recede in these types of streams, the subsequent disconnection of parts of the channel causes a decline in transient storage and metabolism (Covino et al 2010a;Emanuelson et al 2022). Hall et al (2002) found similar results in a study of thirteen streams, where changes in stream width and depth primarily drove variations in transient storage timescales.…”

“…Como Creek is a tributary to Boulder Creek, with land cover consisting of approximately 20% alpine meadow-tundra and 80% conifer forest. The study reach drains a 5.4 km 2 catchment, with elevations ranging from 2895-3557 m and a mean average precipitation of 883 mm/y (Ries III et al 2017;Emanuelson et al 2022). Como Creek has a snowmelt-driven hydrograph with stream discharges ranging from 1-98 L/s and features short-lived increases in discharge during the monsoon season between July and August (Figure 1).…”

“…The extent of water exchange controls the residence time of solutes (Drummond et al, 2012;Gomez et al, 2012;Patil et al, 2013), their chemical signatures (Covino and McGlynn 2007), as well as the microbial composition and their metabolic functioning (Blume et al 2002;Navel et al 2011;Li et al 2020). Exchange patterns are influenced by geomorphologic conditions (Kasahara and Wondzell 2003;Cardenas et al 2004;Gooseff et al 2005;Emanuelson et al 2022), hydrologic conditions (i.e., discharge and surrounding water table configuration) (Gooseff et al 2005;Wondzell 2006;Ward et al 2013;Ward and Packman 2019), and even biofilm growth (Battin et al 2003;Wen and Li 2018). The spatiotemporal variability in exchange processes and resource availability (e.g., seasonal variations in nutrient loads) create heterogeneous hydrologic and biogeochemical gradients across space and time, within which ecosystem metabolism occurs (Mulholland et al, 1985;Mulholland & Hill, 1997).…”

Breaking the Window of Detection: Using Multi‐Scale Solute Tracer Studies to Assess Mass Recovery at the Detection Limit

“…Thus, in addition to assessment of marginal mass recovery, we strongly recommend additional care is given to calculation of discharge. This could be realized by making discharge measurements independently of the tracer (e.g., via velocity gauging or co‐location a station with an established stream gauge), building a stage‐discharge relationship from several replication dilution gauging experiments, using multiple in‐stream sensors to validate complete mixing (e.g., Clow & Fleming, 2008), or otherwise assessing uncertainty in discharge and propagating that through calculations (Emmanuelson et al., 2022; Ward, Wondzell, et al., 2019). Any error in study of an individual segment may propagate through the analysis, and small errors could be amplified in the convolution of reaches to represent study segments.…”