Transport of Conservative and “Smart” Tracers in a First-Order Creek: Role of Transient Storage Type

Yakirevich, A.; Shelton, Daniel R.; Hill, Robert L.; Kiefer, L.A.; Stocker, Matthew; Blaustein, Ryan A.; Kuznetsov, M.; McCarty, Greg; Pachepsky, Yakov

doi:10.3390/w9070485

Cited by 8 publications

(10 citation statements)

References 36 publications

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“…Furthermore, dotty plots (Figure S7) between k and objective functions (nRMSE Rru , LRMSE Rru ) show that k is indeed sensitive; it is just less sensitive than other model parameters. While only a few studies exist that employ formulations of MITS and MATS alongside smart tracer observations, some have concluded, similar to our study, that k may be highly uncertain (Yakirevich et al, ). Others have found low uncertainty for k through joint fitting of multiple tracers (Lemke et al, ).…”

Section: Discussionsupporting

confidence: 80%

“…As we only consider one objective function in this analysis, and we do not combine and propagate these parameter estimates back into the observation space, we can only speculate on how these findings may lead to improved calibration strategies. We do note that our findings challenge a common approach where some model parameters are constrained first using a conservative tracer and then fixed and others constrained in a second step using a reactive tracer (e.g., Claessens et al, ; Keefe et al, ; Yakirevich et al, ). Lemke et al () also found differences in optimized parameters for transport when a conservative tracer was fitted alone or jointly with Raz.…”

Section: Discussionmentioning

confidence: 57%

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

Kelleher

Ward

Knapp

et al. 2019

Water Resources Research

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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.

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Section: Discussionsupporting

confidence: 80%

Section: Discussionmentioning

confidence: 57%

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

Kelleher

Ward

Knapp

et al. 2019

Water Resources Research

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“…For instance, Byappanahalli et al (2003) reported a relatively low correlation between concentrations of E. coli in stream water and sediment (r = 0.49; p = 0.064), as did Crabill et al (1999) for concentrations along a creek in Arizona (r = 0.28). Wu et al (2009) reported a significant correlation of r = 0.29 for the Blackstone River, MA. One reason for low correlations might be sediment heterogeneity.…”

Section: Discussionmentioning

confidence: 97%

“…The experiment was performed in the Beaver Dam Creek Tributary, which is described in detail by Angier et al (2005), Cho et al (2010), Stocker et al (2016), and Yakirevich et al (2017). In brief, the creek runs through a riparian corridor with a width of about 65 m at its narrowest to more than 100 m at its widest point.…”

Section: Methodsmentioning

confidence: 99%

Spatial Patterns of Escherichia coli Concentrations in Sediment before and after High‐Flow Events in a First‐Order Creek

2018

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Understanding spatial patterns of Escherichia coli in freshwater sediments is necessary to characterize sediments as microbial reservoirs and to evaluate the impact of sediment resuspension on microbial water quality in watersheds. Sediment particle size distributions and streambed E. coli concentrations were measured along a 500‐m‐long reach of a first‐order creek 1 d before and on Days 1, 3, 6, and 10 after each of two artificial high‐flow events, with natural high‐flow events also occurring within the sampling periods. Spatial variability of E. coli was greater in sediments than in water within any given sampling; however, variation between sampling days was greater for water than for sediment. The mean relative difference analysis revealed temporally stable patterns of E. coli concentrations in sediments. Escherichia coli–rich locations along the reach corresponded to areas with higher organic matter and fine particle contents. Although low (k < 0.5 d−1) or negative survival rates were observed at most locations along the reach during times where no precipitation was recorded, a small number of locations showed such large concentration increase that on average the survival rate remained positive at the reach scale. The studied creek appears to have hot spots of concentration increase, where conditions for E. coli populations to increase are much more favorable than in most other locations across the reach. The effect of this increase can be seen at the reach scale but is difficult to discern without individual sampling that is dense in space and time. Core Ideas Temporally stable patterns of E. coli concentrations in sediments were observed. Reach‐average E. coli concentrations increased in sediments between high‐flow events. E. coli survival rates were generally low except in certain locations along the reach.

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“…We based our analysis on temporal moments rather than parameters estimated from a transient storage‐type model. Even though a model‐based analysis is very common for the assessment of reaction parameters in the context of the reactive‐tracer system (e.g., Argerich et al, ; Haggerty et al, ; Knapp & Cirpka, ; Lemke et al, ; Liao et al, ; Yakirevich et al, ), the estimated parameters are often associated with high uncertainties. Furthermore, choosing the most suitable model is difficult, as opinions differ on the question of an adequate representation and dismissal of different processes, with nearly as many models available as modeling studies published on the resazurin‐resorufin tracer system.…”

Section: Discussionmentioning

confidence: 99%

A Critical Assessment of Relating Resazurin–Resorufin Experiments to Reach‐Scale Metabolism in Lowland Streams

Knapp

Cirpka

2018

JGR Biogeosciences

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The reactive tracer resazurin is frequently used to investigate metabolic processes and hyporheic exchange in streams, because its transformation to resorufin is considered a function of oxygen turnover in the presence of living cells and thus of ecosystem respiration. This has been investigated and confirmed in a number of laboratory and batch studies but so far not unanimously been verified in the field. In this study we conducted 13 tracer tests with resazurin in different small, low-gradient streams typical of lowland regions. We determined tracer processing rates and ratios based on metrics commonly used to assess reactive-tracer tests with resazurin and compared them to flow characteristics and respiration rates determined from diel-oxygen recordings following a two-station approach. We found no statistical relationship between tracer processing and parameters of transport, indicating that resazurin-to-resorufin transformation cannot be explained by hydrological parameters alone. The statistical relationship between resazurin processing and respiration rates based on diel-oxygen profiles was strongest if resazurin-to-resorufin transformation ratios were used, and uptake lengths were also found to be a good proxy. However, the relationships were not linear, and weak for individual streams, contrary to what was expected based on previous laboratory and batch studies. We attribute this finding to a lack of process understanding of resazurin reactions under field conditions, and generally insignificant hyporheic exchange fluxes in lowland streams. Our findings highlight important limitations of the tracer technique under field conditions. Plain Language SummaryResazurin is a compound that can be used to track the flow of water and metabolic processes in rivers. This has been tested in laboratory experiments before, but until now the method has not been fully verified in the field. In this study we apply resazurin to 13 different small rivers and measure its transformation. We compare our results to other, independent methods that are often used to determine metabolic processes. We find good relationships between the two different methods for all rivers together, but not for individual rivers. Also, the relationships are not linear, which makes predicting metabolic rates from the transformation of resazurin difficult.

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Transport of Conservative and “Smart” Tracers in a First-Order Creek: Role of Transient Storage Type

Cited by 8 publications

References 36 publications

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

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

Spatial Patterns of Escherichia coli Concentrations in Sediment before and after High‐Flow Events in a First‐Order Creek

A Critical Assessment of Relating Resazurin–Resorufin Experiments to Reach‐Scale Metabolism in Lowland Streams

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