Spatial and Temporal Dynamics of Dissolved Oxygen Concentrations and Bioactivity in the Hyporheic Zone

Reeder, W. J.; Quick, A. M.; Farrell, T. B.; Benner, S. G.; Feris, Kevin P.; Tonina, Daniele

doi:10.1002/2017wr021388

Cited by 45 publications

(52 citation statements)

References 70 publications

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“…The overall CFD modeling approach was validated against the pressure measurements provided by Fehlman (). Excellent agreement between the simulated and measured bed‐pressures, over a broad range of discharges and energy head, gave us a high degree of confidence that our modeling approach was robust, accurate, and not overly sensitive to the applied boundary conditions (Reeder, Quick, Farrell, Benner, Feris, & Tonina, ).…”

Section: Methodsmentioning

confidence: 72%

“…In Reeder, Quick, Farrell, Benner, Feris, and Tonina (), we showed that, for dune‐like bedforms, K DO values are lognormally distributed and that flowline K DO values can be calculated from the mean K DO ,

{\overset{true¯}{K}}_{DO}

, and the standard deviation, σ KDO . They can be estimated by using the log transform variable, Y = ln( K DO ), the log transform mean, μ Y , and the standard deviation, σ Y , from the inverse normal distribution:

K_{DO} = exp ((), F^{- 1} (|(), X^{*}, μ_{Y}, σ_{Y})),

where

F (|(), Y, μ_{Y}, σ_{Y}) = \frac{1}{σ_{Y} \sqrt{2 π}} \int_{o}^{Y} e^{\{\frac{- {(ln ξ - μ_{Y})}^{2}}{2 σ_{Y}^{2}}\}} normald ξ,

where ξ is a dummy variable of integration and X* is the dimensionless, horizontal distance along the upstream downwelling face of the bedform, defined as

X^{*} = x / λ^{'},

with λ ′ as the horizontal length of the downwelling face of the bedform.…”

Section: Methodsmentioning

confidence: 82%

“…Measurements were recorded every 5 min for approximately 3 days using Model CR1000 data loggers (Campbell Scientific, Logan, UT, USA). Residence times were calculated from breakthrough curves at 9 locations in a 70 cm dune and 20 locations in a 100 cm dune (Reeder, Quick, Farrell, Benner, Feris, & Tonina, ).…”

Section: Methodsmentioning

confidence: 99%

“…DO and other chemical constituents can be treated in a similar manner. We used the same procedure, with τ versus [DO] data streams, to calculate the rate constant of metabolic DO consumption ( K DO ) for all flowlines (Reeder, Quick, Farrell, Benner, Feris, & Tonina, ).…”

Section: Methodsmentioning

confidence: 99%

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Hyporheic Source and Sink of Nitrous Oxide

Reeder

Quick

Farrell

et al. 2018

Water Resources Research

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Nitrous oxide (N2O) is a potent greenhouse gas with an estimated 10% of anthropogenic N2O coming from the hyporheic zone of streams and rivers. However, difficulty in making accurate fine‐scale field measurements has prevented detailed understanding of the processes of N2O production and emission at the bedform and flowline scales. Using large‐scale, replicated flume experiments that employed high‐density chemical concentration measurements, we have been able to refine the current conceptualization of N2O production, consumption, and emission from the hyporheic zone. We present a predictive model based on a Damköhler‐type transformation (τ̃) in which the hyporheic residence times (τ) along the flowlines are multiplied by the dissolved oxygen consumption rate constants for those flowlines. This model can identify which bedforms have the potential to produce and emit N2O, as well as the portion and location from which those emissions may occur. Our results indicate that flowlines with τ̃up (τ̃ as the flowline returns to the surface flow) values between 0.54 and 4.4 are likely to produce and emit N2O. Flowlines with τ̃up values of less than 0.54 will have the same N2O as the surface water and those with values greater than 4.4 will likely sink N2O (reference conditions: 17C, surface dissolved oxygen 8.5 mg/L). N2O production peaks approximately at τ̃ = 1.8. A cumulative density function of τ̃up values for all flowlines in a bedform (or multiple bedforms) can be used to estimate the portion of flowlines, and in turn the portion of the streambed, with the potential to emit N2O.

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Section: Methodsmentioning

confidence: 72%

{\overset{true¯}{K}}_{DO}

K_{DO} = exp ((), F^{- 1} (|(), X^{*}, μ_{Y}, σ_{Y})),

where

F (|(), Y, μ_{Y}, σ_{Y}) = \frac{1}{σ_{Y} \sqrt{2 π}} \int_{o}^{Y} e^{\{\frac{- {(ln ξ - μ_{Y})}^{2}}{2 σ_{Y}^{2}}\}} normald ξ,

where ξ is a dummy variable of integration and X* is the dimensionless, horizontal distance along the upstream downwelling face of the bedform, defined as

X^{*} = x / λ^{'},

with λ ′ as the horizontal length of the downwelling face of the bedform.…”

Section: Methodsmentioning

confidence: 82%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Hyporheic Source and Sink of Nitrous Oxide

Reeder

Quick

Farrell

et al. 2018

Water Resources Research

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“…This inference is supported by Peclét and Damköhler calculations >1 for each location and time point, suggesting that the DO transport‐induced oxygen consumption reaction controls porewater concentrations of DO (Saup et al, ; equations S1 and S2). Previous work on the lateral distribution of microbial respiration rates also found that DO and nutrient concentrations influence spatial and temporal “hot spots” of biogeochemical activity within the hyporheic zone (King et al, ; Reeder et al, ). In contrast to the vertical exchange measured here at the East River site, the introduction and rapid depletion of DO along lateral hyporheic flow paths through the floodplain have also been modeled at East River, indicating that complete oxygen consumption occurs over ~1‐m distances into the floodplain (Dwivedi et al, ).…”

Section: Resultsmentioning

confidence: 93%

Hyporheic Zone Microbiome Assembly Is Linked to Dynamic Water Mixing Patterns in Snowmelt‐Dominated Headwater Catchments

Saup

Bryant

et al. 2019

JGR Biogeosciences

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Terrestrial and aquatic elemental cycles are tightly linked in upland fluvial networks. Biotic and abiotic mineral weathering, microbially mediated degradation of organic matter, and anthropogenic influences all result in the movement of solutes (e.g., carbon, metals, and nutrients) through these catchments, with implications for downstream water quality. Within the river channel, the region of hyporheic mixing represents a hot spot of microbial activity, exerting significant control over solute cycling. To investigate how snowmelt-driven seasonal changes in river discharge affect microbial community assembly and carbon biogeochemistry, depth-resolved pore water samples were recovered from multiple locations around a representative meander on the East River near Crested Butte, CO, USA. Vertical temperature sensor arrays were also installed in the streambed to enable seepage flux estimates. Snowmelt-driven high river discharge led to an expanding zone of vertical hyporheic mixing and introduced dissolved oxygen into the streambed that stimulated aerobic microbial respiration. These physicochemical processes contributed to microbial communities undergoing homogenizing selection, in contrast to other ecosystems where lower permeability may limit the extent of mixing. Conversely, lower river discharge conditions led to a greater influence of upwelling groundwater within the streambed and a decrease in microbial respiration rates. Associated with these processes, microbial communities throughout the streambed exhibited increasing dissimilarity between each other, suggesting that the earlier onset of snowmelt and longer periods of base flow may lead to changes in the composition (and associated function) of streambed microbiomes, with consequent implications for the processing and export of solutes from upland catchments. Plain Language SummarySeasonal changes in river discharge in high-altitude watersheds affect patterns of surface and groundwater mixing in the hyporheic zone (the region in the riverbed where these two water sources interact) that impacts how carbon compounds and dissolved metals are transported.One key constraint with respect to hyporheic mixing concerns the rate of water flow, a process driving the change in oxygen concentration in the riverbed. Oxygen concentration controls rates of carbon degradation and metal inputs from surrounding rocks, which, in turn, affect downstream water quality. In this study, we examined a stream in an alpine watershed, the East River, in the Upper Colorado River Basin, sampling a representative meander at discrete depths throughout the year for microbiological and geochemical analyses. We also installed data loggers to continuously collect temperature information to understand the extent of hyporheic mixing. We found that the movement of dissolved materials was strongly correlated with seasonal changes in flow. Under maximum flow, increased oxygen concentration stimulates microbial degradation of carbon compounds, and conversely, during minimum flow, decreased oxygen c...

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