Predicting Effects of Dead Zones on Stream Mixing

Thackston, Edward L.; Schnelle, Karl B.

doi:10.1061/jsedai.0001078

Cited by 129 publications

(10 citation statements)

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“…The term "immobile zone" is a general concept as it can refer to stagnant zones (distinguished by lowvelocity regions) but also RZs (characterized by enclosed rotating flow regions). The classic MIM theory is also widely applied in modeling transport in unsaturated soils with different water contents and/or adsorption (Bond & Wierenga, 1990;Bosma et al, 1993;Padilla et al, 1999;van Genuchten & Wierenga, 1976) and streams or rivers with solute storage areas (called the transient storage model ;Bencala, 1983;Kim et al, 1992;Thackston & Schnelle, 1970). The traditional MIM model conceptualizes the transport domain as having mobile and immobile regions that are uniformly distributed along the main flow direction, with their proportion determined by the volume ratio (θ).…”

Section: Distributed Mim Model With Physically Based Local Parametersmentioning

confidence: 99%

Mass Transfer Between Recirculation and Main Flow Zones: Is Physically Based Parameterization Possible?

Zhou

Wang

Chen

et al. 2019

Water Resources Research

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Recirculation zones (RZs) are common in many geophysical flows. These zones form near irregular solid boundaries and are separate from the main flow. Since mass can be trapped and later released locally from RZs, bulk transport can exhibit long tails—an anomalous behavior that is challenging to predict. The underlying RZ mass transfer and retention in this situation is poorly understood despite common parameterization by effective exchange coefficients in mobile‐immobile (MIM) domain models. We analyzed the mass transfer process using computationally resolved flow and transport fields inside two‐dimensional rough fractures. RZs were delineated by a novel technique followed by quantification of mass transfer across the interface with the main flow zone. The results showed that the first‐order mass transfer coefficient is a function of Reynolds number and velocity difference between the RZ and bulk flow. A distributed mobile‐immobile model with the directly estimated parameters accurately reproduced bulk anomalous transport. While the distributed mobile‐immobile model is not yet predictive, its development showed that mass transfer coefficients for flows involving RZs are physically meaningful, potentially predictable, and useful for elucidating local mass transfer processes within the general framework of mobile‐immobile transport modeling.

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Section: Distributed Mim Model With Physically Based Local Parametersmentioning

confidence: 99%

Mass Transfer Between Recirculation and Main Flow Zones: Is Physically Based Parameterization Possible?

Zhou

Wang

Chen

et al. 2019

Water Resources Research

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“…The average time spent by a molecule in the transient storage zone (T sto , T) is evaluated as (Thackston and Schnelle, 1970)…”

Section: Metrics and Hydrologic Interpretation Of Tsm Resultsmentioning

confidence: 99%

“…A robust assessment of transient storage parameters would not only improve the model fit of tracer transport and increase parameter identifiability, but it might also lead to a more robust interpretation of the physical processes controlling solute transport in streams. Model parameters are often used to calculate metrics on the solute exchange between the stream channel and the transient storage zone and the residence time of solutes in the coupled system (Thackston and Schnelle, 1970;Morrice et al, 1997;Hart et al, 1999;Runkel, 2002). These metrics are pivotal for addressing the potential for nutrient cycling, microbial activity, and the development of hotspots in river ecosystems (Triska et al, 1989;Mulholland et al, 1997;Smith, 2005;Krause et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Exploring tracer information in a small stream to improve parameter identifiability and enhance the process interpretation in transient storage models

Bonanno

Blöschl²,

Klaus³

2022

Hydrol. Earth Syst. Sci.

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Abstract. The transport of solutes in river networks is controlled by the interplay of processes such as in-stream solute transport and the exchange of water between the stream channel and dead zones, in-stream sediments, and adjacent groundwater bodies. Transient storage models (TSMs) are a powerful tool for testing hypotheses related to solute transport in streams. However, model parameters often do not show a univocal increase in model performances in a certain parameter range (i.e. they are non-identifiable), leading to an unclear understanding of the processes controlling solute transport in streams. In this study, we increased parameter identifiability in a set of tracer breakthrough experiments by combining global identifiability analysis and dynamic identifiability analysis in an iterative approach. We compared our results to inverse modelling approaches (OTIS-P) and the commonly used random sampling approach for TSMs (OTIS-MCAT). Compared to OTIS-P, our results informed about the identifiability of model parameters in the entire feasible parameter range. Our approach clearly improved parameter identifiability compared to the standard OTIS-MCAT application, due to the progressive reduction of the investigated parameter range with model iteration. Non-identifiable results led to solute retention times in the storage zone and the exchange flow with the storage zone with differences of up to 4 and 2 orders of magnitude compared to results with identifiable model parameters respectively. The clear differences in the transport metrics between results obtained from our proposed approach and results from the classic random sampling approach also resulted in contrasting interpretations of the hydrologic processes controlling solute transport in a headwater stream in western Luxembourg. Thus, our outcomes point to the risks of interpreting TSM results when even one of the model parameters is non-identifiable. Our results showed that coupling global identifiability analysis with dynamic identifiability analysis in an iterative approach clearly increased parameter identifiability in random sampling approaches for TSMs. Compared to the commonly used random sampling approach and inverse modelling results, our analysis was effective at obtaining higher accuracy of the evaluated solute transport metrics, which is advancing our understanding of hydrological processes that control in-stream solute transport.

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“…From the measured water depth in the stream channel and other streambed characteristics we evaluated the Darcy‐Weisbach friction factor ( f [−]). This quantity has been related to streambed complexity and in‐stream storage zones and its formulation reads (Bencala & Walters, 1983; Hart et al., 1999; Thackston & Schnelle, 1970):

f=\frac{8g\cdot d\cdot S}{{v}^{2}}

where g [L/T 2 ] is the gravitational constant, S [L/L] is the slope of the energy grade line estimated from the stream channel slope (Zarnetske et al., 2007), d [L] is the average water depth measured in the stream channel.…”

Section: Methodsmentioning

confidence: 99%

“…We evaluated the average residence time of a tracer molecule in the transient storage zone ( RT s [T]) and the average time a tracer molecule remains in the stream channel before passing into the storage zone ( RT Q [T]) (Runkel, 2002; Thackston & Schnelle, 1970):

{\mathrm{R}\mathrm{T}}_{Q}=\frac{1}{\alpha }

{\mathrm{R}\mathrm{T}}_{S}=\frac{{A}_{\mathrm{T}\mathrm{S}}}{\alpha \cdot A}

…”

Section: Methodsmentioning

confidence: 99%

Discharge, Groundwater Gradients, and Streambed Micro‐Topography Control the Temporal Dynamics of Transient Storage in a Headwater Reach

Bonanno

Blöschl

Klaus

2023

Water Resources Research

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Contradictory interpretations of transient storage modeling (TSM) results of past studies hamper the understanding of how hydrologic conditions control solute transport in streams. To address this issue, we conduct 30 instantaneous tracer experiments in the Weierbach stream, Luxembourg. Using an iterative modeling approach, we calibrate TSM parameters and assess their identifiability across various hydrologic conditions. Near‐stream groundwater monitoring wells and LIDAR scans of the streambed are used to evaluate the area of the hyporheic zone and of the submerged sediments for each experiment. Our findings show that increasing discharge enhances parameters interaction requiring more samples of TSM parameters to obtain identifiable results. Our results also indicate that transient storage at the study site is influenced by in‐stream and hyporheic exchange processes during low discharge, likely due to the hyporheic zone's large extent and the relatively low water level compared to the size of slate fragments on the streambed. However, as discharge increases, in‐stream storage zones become part of the advective channel and the lower localized stream water losses to the adjacent groundwater suggests a decrease of the hyporheic exchange on transient storage. The results obtained were utilized to generate a hydrograph for the study site illustrating the dynamic evolution of in‐stream and hyporheic storage with varying discharge, providing insights into the expected influence of different transient storage processes prior to tracer experiments. Overall, our study enhances the understanding of the role of the hyporheic area and in‐stream storage zones in transient storage and helps estimate TSM parameters more accurately.

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Predicting Effects of Dead Zones on Stream Mixing

Cited by 129 publications

References 0 publications

Mass Transfer Between Recirculation and Main Flow Zones: Is Physically Based Parameterization Possible?

Mass Transfer Between Recirculation and Main Flow Zones: Is Physically Based Parameterization Possible?

Exploring tracer information in a small stream to improve parameter identifiability and enhance the process interpretation in transient storage models

Discharge, Groundwater Gradients, and Streambed Micro‐Topography Control the Temporal Dynamics of Transient Storage in a Headwater Reach

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