Exploring Tracer Information and Model Framework Trade‐Offs to Improve Estimation of Stream Transient Storage Processes

Abstract

Novel observation techniques (e.g., smart tracers) for characterizing coupled hydrological and biogeochemical processes are improving understanding of stream network transport and transformation dynamics. In turn, these observations are thought to enable increasingly sophisticated representations within transient storage models (TSMs). However, TSM parameter estimation is prone to issues with insensitivity and equifinality, which grow as parameters are added to model formulations. Currently, it is unclear whether (or not) observations from different tracers may lead to greater process inference and reduced parameter uncertainty in the context of TSM. Herein, we aim to unravel the role of in‐stream processes alongside metabolically active (MATS) and inactive storage zones (MITS) using variable TSM formulations. Models with one (1SZ) and two storage zones (2SZ) and with and without reactivity were applied to simulate conservative and smart tracer observations obtained experimentally for two reaches with differing morphologies. As we show, smart tracers are unsurprisingly superior to conservative tracers when it comes to partitioning MITS and MATS. However, when transient storage is lumped within a 1SZ formulation, little improvement in parameter uncertainty is gained by using a smart tracer, suggesting the addition of observations should scale with model complexity. Importantly, our work identifies several inconsistencies and open questions related to reconciling time scales of tracer observation with conceptual processes (parameters) estimated within TSM. Approaching TSM with multiple models and tracer observations may be key to gaining improved insight into transient storage simulation as well as advancing feedback loops between models and observations within hydrologic science.

Solute Transport and Transformation in an Intermittent, Headwater Mountain Stream with Diurnal Discharge Fluctuations

“…The Raz-Rru system has broadly been used to identify the portion of transient storage that is metabolically active, and has been used to assess reactivity in biofilms [74], the benthic zone [75,76], vegetation beds [77], stream-and lakebed sediments [78][79][80][81], and whole stream reaches [82][83][84][85]. Interpretation of Raz-Rru information commonly relies upon inverse modeling that enforces a conceptual model with binary divisions of the system into zones such as channel or storage (based on the importance of advection and relative timescales of storage) or metabolically active or inactive storage (based on transformation rates) [83,86].…”

“…One challenge with modeling solute tracer transport and transformation is parameter uncertainty and equifinality [18,[86][87][88][89][90]. Further, in their standard form, these models have not been formulated to address time-variable transport or transformation, and do not account for the possibility that the system is more complex than the binary differentiation of stream and storage zone.…”

Fine particle transport dynamics in response to wood additions in a small agricultural stream

“…The time solutes and particles spend in the immobile zone is controlled by the following parameters: (a) the power-law residence time distribution of solute within the immobile zone, set by the power law slope, β S , (b) the rate of fine particle immobilization with the immobile zone, Λ IP , and (c) the powerlaw residence time distribution of particles in the immobile zone, set by the power-law slope, β IP .Following the fitting procedure outlined inDrummond, Schmadel, Kelleher, Packman, and Ward (2019), we performed several computational experiments with simulations and parameter sets constrained to match the conservative solute and fine particle breakthrough curves. We sampled the parameter space using a Latin Hypercube approach (N = 27,000; e.g.,Kelleher et al, 2019). TheBalanced mean square error (θ ; Bottacin-Busolin, Marion, Musner, Tregnaghi, & Zaramella, 2011) objective function was calculated for each simulation as:θ = 1 n…”

Improving Predictions of Fine Particle Immobilization in Streams

“…Despite the extensive literature exploring the implications of combined tracer injection experiments and transient storage model analysis (e.g., Gooseff et al, 2005;Kelleher et al, 2013;Kelleher et al, 2019;Wagener et al, 2002;Ward et al, 2017), there is a lack of consensus on methods for assessing fine particle dynamics. Despite the extensive literature exploring the implications of combined tracer injection experiments and transient storage model analysis (e.g., Gooseff et al, 2005;Kelleher et al, 2013;Kelleher et al, 2019;Wagener et al, 2002;Ward et al, 2017), there is a lack of consensus on methods for assessing fine particle dynamics.…”

“…Model simulations are useful to extend tracer observations and to quantify the key physical transport processes for both solutes and fine particles. Despite the extensive literature exploring the implications of combined tracer injection experiments and transient storage model analysis (e.g., Gooseff et al, 2005;Kelleher et al, 2013;Kelleher et al, 2019;Wagener et al, 2002;Ward et al, 2017), there is a lack of consensus on methods for assessing fine particle dynamics. Methods to relate fine particle transport characteristics to solute tracer data are particularly lacking.…”