Hyporheic hot moments: Dissolved oxygen ... - AGU Publications

PUBLICATIONS Water Resources Research RESEARCH ARTICLE 10.1002/2016WR020296 Key Points: Experiments and models show that changes in channel water velocity lead to significant dynamic response in hyporheic zone dissolved oxygen The hyporheic zone response time scale is much longer than the open channel perturbation time scale Hyporheic response time scales and dissolved oxygen plume morphology exhibit hysteresis relative to channel velocity change Supporting Information: Supporting Information S1 Figure S1 Movie S1

Correspondence to: M. H. Kaufman, [email protected]

Citation: Kaufman, M. H., M. B. Cardenas, J. Buttles, A. J. Kessler, and P. L. M. Cook (2017), Hyporheic hot moments: Dissolved oxygen dynamics in the hyporheic zone in response to surface flow perturbations, Water Resour. Res., 53, 6642–6662, doi:10.1002/2016WR020296. Received 20 DEC 2016 Accepted 8 JUL 2017 Accepted article online 13 JUL 2017 Published online 7 AUG 2017

Hyporheic hot moments: Dissolved oxygen dynamics in the hyporheic zone in response to surface flow perturbations Matthew H. Kaufman1 Perran L. M. Cook2

, M. Bayani Cardenas1

, Jim Buttles1, Adam J. Kessler2

, and

1

Geological Sciences, University of Texas at Austin, Austin, Texas, USA, 2Water Studies Centre, Monash University, Clayton, Australia

Abstract Dissolved oxygen (DO) is a key environmental variable that drives and feeds back with numerous processes. In the aquatic sediment that makes up the hyporheic zone, DO may exhibit pronounced spatial gradients and complex patterns which control the distribution of a series of redox processes. Yet, little is known regarding the dynamics of hyporheic zone DO, especially under transitional flow regimes. Considering the natural tendency of rivers to be highly responsive to external forcing, these temporal dynamics are potentially just as important and pronounced as the spatial gradients. Here we use laboratory flume experiments and multiphysics flow and reactive transport modeling to investigate surface flow controls on the depth of oxygen penetration in the bed as well as the area of oxygenated sediment. We show that the hyporheic zone DO conditions respond over time scales of hours-to-days when subjected to practically instantaneous surface flow perturbations. Additionally, the flume experiments demonstrate that hyporheic zone DO conditions respond faster to surface flow acceleration than to deceleration. Finally, we found that the morphology of the dissolved oxygen plume front depends on surface flow acceleration or deceleration. This study thus shows that the highly dynamic nature of typical streams and rivers drives equally dynamic redox conditions in the hyporheic zone. Because the redox conditions and their distribution within the hyporheic zone are important from biological, ecological, and contaminant perspectives, this hyporheic redox dynamism has the potential to impact system scale aquatic chemical cycles.

Plain Language Summary The amount of dissolved oxygen (DO) in water is important both in rivers and their underlying sediment. River discharge is constantly changing, and since it is the river’s flow which drives flow in the bed, the pronounced chemical and biological gradients within the riverbed may also be constantly changing. We used laboratory experiments and computational models to explore how fast the DO in the bed changes in response to changes in the river velocity. We found that small changes in river velocity created large changes in riverbed DO conditions. We also found that the riverbed DO conditions changed much slower than the river velocity, and that the riverbed response was faster when the river velocity increased and slower when the river decelerated. The shape of the high DO area in the riverbed depended on whether the river water was speeding up, slowing down, or steady. We observed short-lived areas of low DO in what were mostly high DO zones, and vice versa, particularly when the river water was decelerating. This study thus showed that the riverbed is highly dynamic, subject to large changes in DO that can last much longer than the variations in the river flows that cause them.

1. Introduction

C 2017. American Geophysical Union. V

All Rights Reserved.

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The hyporheic zone, the area under and around a river where pore water flow lines both start and end in the river channel, is a critical natural biogeochemical reactor [Goosef, 2010]. Transport and metabolism of solutes within the hyporheic zone are key processes required by many organisms and impact water quality over larger scales [Harvey and Fuller, 1998; Packman and Brooks, 2001; Triska et al., 1989]. In particular, hyporheic dissolved oxygen (DO) exerts a strong control on the ecology and biogeochemistry of aquatic systems. DO in the streambed is a critical factor for the survival of benthic invertebrates as well as the eggs and young of many fish [Boulton et al., 1998; Brunke and Gonser, 1997; Chapman, 1988; Tonina et al., 2015]. In addition, advances in research on the terrestrial freshwater component of global carbon and nutrient cycling have shown that many of the chemical transformations that make up these large-scale cycles are

DISSOLVED OXYGEN DYNAMICS IN THE HYPORHEIC ZONE

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primarily microbial in nature and take place within the hyporheic zone [Battin et al., 2008; Fischer et al., 2005; McClain et al., 2003; Stegen et al., 2016]. Nitrate uptake is a critical ecosystem service provided by the hyporheic zone, and one that is completely controlled by the redox conditions present. In addition to nutrient processing, the mobility of many metals is controlled by their redox state, making hyporheic redox conditions important to the fate and transport of many metallic contaminants. These redox conditions in the streambed are typically present in a stacked assemblage, with zones of different redox pathways and terminal electron acceptors proceeding from oxygen at the top, down the redox ladder depending on the other terminal electron acceptors available in the system [Fischer et al., 2005; Hendricks, 1993; Triska et al., 1993; Zheng et al., 2016]. In order to sustain biogeochemically active systems, the hyporheic zone must be constantly supplied with oxygen, carbon, and other nutrients at rates at least equal to their respective consumption [Drummond et al., 2014]. These are provided as solutes and colloids through the process of hyporheic exchange. As water flows through a channel, the bed forms present on the bottom of the channel impinge on the flowing water, creating a varying pressure field along the sediment-water interface. This in turn drives interstitial flow from areas of relatively high bed pressure to areas of relatively low bed pressure. This is frequently referred to as bed form-driven hyporheic exchange or pumping [Elliott and Brooks, 1997a, 1997b; Packman and Brooks, 2001]. Recent investigations into large-scale hyporheic exchange dynamics indicate that bed form-driven hyporheic exchange may be far more significant biogeochemically than lateral hyporheic exchange [Gomez-Velez et al., 2015]. Recognition of the hyporheic zone as an important hot spot for nutrient dynamics has driven increasing study of the intersection between the physics of bed form-driven hyporheic exchange and the biogeochemistry of reactive transport within the hyporheic zone. The flow field which develops within the hyporheic zone beneath a dune-shaped bed form river bottom consists of alternating patterns of downwelling and upwelling zones, with the horizontal component of hyporheic flow alternating between downstream and upstream [Thibodeaux and Boyle, 1987]. Because of this flow field, combined with the supply of DO from the surface water, the hyporheic zone contains a series of discrete oxygen plumes beneath each bed form at relatively low and steady velocities. These plumes have been both mapped and modeled recently [Kessler et al., 2012], and bear strong resemblance to similar (though oscillatory) processes occurring in shallow marine sediments [Precht et al., 2004]. Since bed form-driven hyporheic exchange is created by pressure gradients along the bed surface, which are dependent on the velocity of the surface channel flow, hyporheic zone fluxes, and residence times are intimately connected with surface flow dynamics. As the microbially mediated processes of interest within the hyporheic zone are not instantaneous in nature, the residence time of water within the hyporheic zone is a critical control over the biogeochemical processes taking place [Battin et al., 2008; Zarnetske et al., 2011b]. While residence time has been recognized as an important control on hyporheic processes, variability in hyporheic residence times has been a topic of increasing interest [Briggs et al., 2014, 2015; Brunke and Gonser, 1997; Cardenas, 2015; Gu et al., 2007]. Nonetheless, to date, most studies of hyporheic biogeochemical processes have focused on either long-term average conditions, instant-in-time snapshots, or constant hydraulics with time-variant chemical loads [Gandy et al., 2007; Gomez-Velez et al., 2015; Tonina et al., 2015]. The reality, however, is that rivers are inherently dynamic and the hyporheic zone should thus be treated as a similarly dynamic region. Both natural and human-impacted rivers show wide variability in streamflow, temperature, and chemical conditions. Natural rivers are subject to flow perturbations at wide ranging time scales, from long-term climate changes [Feikema et al., 2013] to seasonal effects [Loheide and Lundquist, 2009], to diel evapotranspiration [Larsen et al., 2014], and even sudden storms acting over minute to hour scales [Sawyer et al., 2014]. Sawyer et al. [2014] conducted one of the few studies that examined linked hydrologic and biogeochemical dynamics at high temporal resolution over short time scales under dynamic conditions. That study took place in a floodplain aquifer during hurricane Sandy, and concluded that additional high-resolution dynamic studies are necessary to better understand biogeochemical dynamics in riparian zones. Heavily human-impacted rivers are subject to these natural perturbations, and also to dam control, which can take the form of increased dynamism through pulsing for irrigation water delivery, hydroelectricity, and mandated environmental flows [Sawyer et al., 2009], or decreased dynamism through

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the damping out of natural flow variation. Thus, there is a potentially significant disconnection between the widespread approach of studying the hyporheic zone under assumed or imposed steady flow conditions versus the dynamic reality of most fluvial systems. In addition to viewing the hyporheic zone as a static system, much research into the biogeochemistry of the hyporheic zone typically focuses on large-scale bulk chemical processing, particularly with regard to nutrients [Wexler et al., 2011; Zarnetske et al., 2011a]. This framework, despite providing broad-scale temporal and spatial information, provides only partial understanding. Just as the hyporheic zone is a hot spot for biogeochemical processes, it may also exhibit biogeochemical hot moments due to dynamic river conditions. While small-scale, high-resolution hyporheic research [Quick et al., 2016], as well as the importance of variable residence times and fluxes [Boano et al., 2010] have both gained ground recently, there is currently a need for integrating high-resolution approaches in studies of dynamic conditions. Thus, in this study we seek to advance the process-level understanding of hyporheic biogeochemistry under dynamic channel velocity conditions and to explore the zone’s potential for hot moments. Our work addresses three major research questions: (1) How does surface channel flow velocity control the pattern of dissolved oxygen in the hyporheic zone? (2) How fast do the hyporheic oxygen conditions respond to surface channel flow perturbations? and (3) How does the spatial pattern of hyporheic dissolved oxygen change under dynamic conditions? We hypothesize that surface flow velocity will drive the size and morphology of the DO plume in the hyporheic zone as it has been shown to do in marine systems [Cook et al., 2007]. Additionally, we hypothesize that hyporheic oxygen conditions will change much more slowly than the surface channel velocity can. Finally, we hypothesize that when the hyporheic zone is subject to dynamic conditions, the morphology of the plumes of dissolved oxygen in the hyporheic zone will vary in nonlinear ways, rather than simply linearly scaling up or down with surface channel velocity. In order to test these hypotheses and gain broad insight, we conducted experiments in a flume outfitted with what is, to our knowledge, the largest planar optode for DO imaging; this unique tool is capable of capturing hyporheic DO dynamics at high frequency and spatial resolution. We supplemented these laboratory experiments with multiphysics numerical models of turbulent flow and reactive hyporheic transport. The combined laboratory and modeling approach allows us to explore, with high fidelity, the dynamic nature of the coupled physical and biogeochemical processes at work within the hyporheic zone.

2. Methods A series of flume experiments were conducted under various channel flow velocity conditions. The key component of the experimental setup was planar optode imaging of the DO distribution. Numerical flow and transport models designed to replicate the flume experiments were constructed in order to explain the observations. Additional numerical simulations were conducted to analyze the sensitivity of DO dynamics to key hyporheic zone parameters, including aerobic respiration rate and sediment permeability. 2.1. Flume Experiments The water-recirculating flume used to conduct these experiments is 8 m long by 0.3 m wide by 1.3 m tall (Figure 1), with a 5 m long by 0.3 m wide by 0.7 m deep sediment zone. The flume is inside a temperaturecontrolled building subject to a maximum of 58C daily temperature change. The walls and bottom of the flume were covered in rigid foam insulation to both limit temperature swings and block light which could promote inappropriate photosynthesis. The sediment used in the flume was washed and screened quartz sand (fairly homogenous sand with D50 5 1.5 mm and only trace fines) mixed with 0.05% by weight of finely crushed walnut shells to provide a source of dissolved organic carbon, mostly in the form of tannic acid [Cao, 2014], manually formed into seven dune bed forms. The dunes were 40–45 cm long and 4 cm tall, with the lee face approximately 1/3 the length of the stoss face. The sand size was chosen to provide a balance of surface area for microbial colonization, resistance to mobilization, and enough hydraulic conductivity for reasonable hyporheic exchange to occur over the duration of the experiment. Hyporheic sediment with a D50 of 1–10 mm is quite common in the center of the continental United States, as well as the southern part of the east coast, making this choice of grain size broadly appropriate in that context as well [Kaufmann et al., 1999; Kiel and Cardenas, 2014; Stoddard et al., 2005]. The flume does not recirculate sediment, so flow velocities were constrained below the critical velocity for entrainment. The water used in the flume was collected from the Lower Colorado River near Hornsby Bend in Austin, Texas, USA. This was done to

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Figure 1. Experimental flume photos and diagram. (a) Photo of the flume in operation, with rigid foam insulation covering the sand bed and black plastic sheeting covering the imaging area. The flume controller is the gray box on the right, while the imaging and sensing systems are controlled by the two computers on the desk. (b) Schematic diagram of the flume, showing the direction of flow, position of the planar optode, and position of the sand bed with dune bed forms. (c) The operating section of the flume with the insulation and plastic sheeting removed, revealing the imaging and lighting systems and the planar optode.

ensure that the microbial communities developed in the flume sediment had access to similar nutrients and trace elements as they would in a natural setting. The chemistry of this river water is provided in Table 1. Prior to conducting any measurements, the flume underwent an incubation period of approximately 5 months to give sufficient time for microbial communities to establish and thrive. The flume is designed to provide rapid and accurate computerized volumetric flow control. In order to ensure that constant volumetric flow resulted in constant water velocity, the water level in the flume was controlled with an electronic auto top-off system that prevents large changes in water depth due to leakage or evaporation. Each experimental trial consisted of an extended period of time (typically 120 h) at a low Table 1. Typical Flume Water Chemistrya

Surface water Pore water (10 cm depth) Pore water (70 cm depth) a

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pH

TDS (ppm)

Sulfate (ppm)

Nitrate (ppm)

Chloride (ppm)

TIC (ppm)

TOC (ppm)

7.9 7.8 7.6

593.1 596.2 583.8

97.8 103.8 62.6

0.6 0.6 BDL

3.2 4.7 3.3

55.5 57.3 60.0

12.5 12.2 10.9

pH, Total dissolved solids (TDS), sulfate, nitrate, chloride, total inorganic carbon (TIC), and total organic carbon (TOC).


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Figure 2. Example experimental trial, planar optode images, and oxygen plume metrics. (a) True-color photograph of the imaged bed form. (b) Planar optode dissolved oxygen image overlain with the two major metrics used throughout this study: oxygenated area (gray area) and depth of plume center of mass (white dot). (c) Example of a planar optode dissolved oxygen image at low surface channel velocity, and (d) Example of a planar optode dissolved oxygen image at high surface channel velocity. (e) Example of a typical experimental trial. The surface channel velocity is shown in black, with a surface channel velocity acceleration at 120 h and deceleration at 240 h. The oxygenated plume area is shown in blue, with steady state conditions achieved before and after each surface channel velocity change.

flow velocity followed by a near-instantaneous (typically under 5 min) velocity acceleration. The higher velocity was maintained long enough for new steady state conditions to be achieved (typically 120 h), followed by a near-instantaneous velocity deceleration back to the original low flow velocity, and further maintenance of the lower velocity until steady state conditions were achieved (again typically 120 h) (Figure 2 and supporting information Video 1). Steady state conditions near the end of the 120 h static discharge periods were used to answer our first research question above, while the periods of dynamic hyporheic response following the near-instantaneous surface channel flow perturbations were used to answer our second and third research questions. The key experimental conditions of volumetric discharge, characteristic velocity, and bed form Reynolds Number are summarized in Table 2. The bed form Reynolds Number is calculated following: Re 5

Uave H v

(1)

where H is the bed form height, v is the kinematic viscosity of water, and Uave is the characteristic velocity. Characteristic velocity is defined as the cross section averaged velocity of the surface water at the bed form crest [Cardenas and Wilson, 2007a, 2007b]. Water depth at the bed form crest was maintained at 4.5 cm. By capturing both steady state periods and the transitions between these states, we were able to examine both steady state and transient hyporheic redox zone regimes without the need for extra experimental trials. Surface water temperature and volumetric flow rate were recorded every minute using a thermocouple and flowmeter built into the flume recirculation system.

Table 2. Experimental Flow Conditions Flume Discharge (L/min) 50 60 75 90 100 125 150

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Reynolds Number (–)

Average Damk€ ohler Number (–)

2778 3333 4167 5000 5556 6944 8333

315 236 165 122 102 71.7 52.9

2.2. Planar Optode Dissolved Oxygen Sensor Foil Dissolved oxygen concentrations in the open channel and bed were recorded hourly throughout each experiment using a planar optode dissolved oxygen sensing system [Larsen et al., 2011]. The planar optode consists of a clear plastic sheet coated with a dye mixture, and then coated again with a mixture of silicone and black carbon. The silicone and black carbon layer serves to reduce reflections from angular sand grains. The silicone


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layer is highly gas-permeable, so it does not disrupt the interaction between the DO in the water and the dye layer. The dye layer is composed of two dye components suspended in a polystyrene matrix. The two dye elements are an oxygen-sensitive luminophore, platinum(II)octaethylporphyrin (PtOEP), and a coumarin antenna dye (MacrolexV fluorescence yellow 10GN (MY)). The typical formulation for the optode is 1%/1% (wt/wt) PtOEP/MY in 4% polystyrene. This mixture is dissolved in an appropriate solvent (chloroform or toluene) and spray-coated onto a 125 lm thick polyester sheet. After the dye layer is dry, 1% (wt/wt) black carbon is suspended in a 1:4 (wt/wt) mixture of silicone and hexane, then spray-coated over the dye layer. Construction is complete once this layer is dry. R

The optode is a fluorescent sensor dependent on the quenching of PtOEP fluorescence in the presence of oxygen. The PtOEP and MY are excited by blue light in the 445 nm range. Excitation light was provided by a 60 cm linear array containing 30 individual 450 nm LEDs. The MY serves as both a light harvester, transporting energy to the PtOEP, and also as an internal reference. The PtOEP fluorescence peaks at 650 nm and is quenched in the presence of oxygen. In addition to providing energy to the PtOEP, the MY fluoresces at a peak of 480 nm and is not quenched by oxygen. In order to account for heterogeneity in the spray coating process, instead of using the PtOEP fluorescence magnitude alone (which is in part a function of the deposited dye thickness), the ratio (R) of PtOEP (red) fluorescence to MY (green) fluorescence is used and is calculated following: R5

red2green green

(2)

R varies nonlinearly with oxygen concentration, expressed as % oxygen saturation, with much greater sensitivity at the low-oxygen-saturation end. R decreases by more than 75% while oxygen saturation rises from 0% to 50%. Nonetheless, enough sensitivity is retained near 100% oxygen saturation to use this method across the full 0–100% saturation spectrum. 2.3. Planar Optode Imaging and Calibration The patterns of fluorescence of the planar optode are dimly visible to the naked eye, but of course that is insufficient to collect reliable data. Fortunately, the emission spectra of the two dyes lend themselves to imaging with a very lightly modified digital SLR camera. We used a Canon EOS 1000D (Canon Inc., Tokyo, Japan) with the built-in near-infrared blocking filter removed. The lens used was a Sigma EX DG Macro lens (50 mm f2.8) (Sigma Corporation of America, Ronkonkoma, NY, USA) equipped with a filter holder and a 530 nm long-pass filter placed in front of the lens. The camera and lighting system were physically controlled by a computer using a combination of off the shelf and custom power supply components. Light and camera triggering, as well as image capture and preliminary image processing, were carried out using custom software (Look@RGB). Images were taken at an exposure time of 1.6 s and aperture of f/3.5. The camera’s CCD sensor is made up of a grid of smaller light sensors, each of which is actually four light sensors: one red, one blue, and two green. Because the PtOEP fluoresces primarily in the red range and MY fluoresces primarily in the green range, the PtOEP (red) image and MY (green) image can easily be extracted from the true color image using software. In this study, the extraction process was performed using Look@RGB. The optode requires calibration. The planar optode response is described by a modified Stern-Volmer equation [Klimant et al., 1995]: R 1 5 a1ð12aÞ (3) R0 Ksv C which can be simplified to the easier-to-use form [Larsen et al., 2011]: C5

R0 2R Ksv ðR2R0 aÞ

(4)

where R0 is the value of R at 0% oxygen saturation, a is the unquenchable fraction of the luminance signal (i.e., the fraction of dye that oxygen cannot reach to quench, even at 100% oxygen saturation), C is the concentration of oxygen (in this study presented in % saturation form), and Ksv is the Stern-Volmer quenching constant. Ksv accounts for both diffusion-limited quenching and the fact that only a fraction of the

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interactions between the dye and oxygen result in quenching. Calibrating the planar optode consists of experimentally determining a and Ksv for a specific system with a specific planar optode, under expected temperature and ambient light conditions. Temperature consistency is particularly important, as the fluorescence of both PtOEP and MY varies with temperature. Our calibration system consisted of a custom built 107 cm wide by 81 cm tall by 20.5 cm deep clear acrylic calibration tank. The tank was constructed to be large enough to contain the optode and allow it to be attached to the inside surface of the tank, while also containing as little water volume as possible. The larger the volume of water in the tank, the slower the system will change its oxygen concentrations and the longer calibration takes to complete. The planar optode is mounted inside the tank, which is then filled with water and allowed to equilibrate with the temperature of the room which houses the flume. In addition to the planar optode, we placed a Firesting dissolved oxygen sensor (Pyro Science GmbH, Aachen, Germany) inside the calibration tank. Prior to use, the Firesting was two-point calibrated using a 0% oxygen solution created using sodium thiosulfate to scavenge all available oxygen from a small vial of water, and a 100% oxygen solution created using a wet paper towel wrapped around the sensor. The Firesting was used as the calibration reference. In order to keep ambient lighting as low and consistent as possible, the calibration setup (tank, camera, and lighting array) was covered with a heavy black plastic sheet. Calibration consisted of at least five images taken over as much of the 0–100% oxygen saturation range as possible, and their accompanying Firesting dissolved oxygen readings. Near-100% oxygen saturation was reached in the tank by stirring the water vigorously with a small pump. After the 100% image was taken, dissolved oxygen in the tank was reduced by covering the tank with a loose-fitting lid and sparging the water with nitrogen using an aquarium air stone. Over the course of a few hours, dissolved oxygen saturation decreased from near-100% to below 10%. Once at least five pairs of Firesting and optode measurements were recorded, R was plotted versus % oxygen saturation, and R0 , a, and Ksv were varied to achieve a fit. The calibration was spot checked after each trial by assuming that the flowing open channel was at 100% saturation and the deepest parts of the bed were at or near 0%. These conditions were indicated by plotting a vertical transect of R values across the planar optode and looking for flat zones at the top and bottom, indicating that the saturation had reached 100% or 0%. This check never indicated drift in the sensor calibration or bleaching of the foil. In high-flow conditions where the lower part of the image was not at or near 0%, this spot check was omitted. Combining the field of view with the camera’s 10.1 megapixel resolution, the spatial resolution of the images is approximately 0.05 mm2 per pixel. The planar optodes used in this study typically furnish an even and smooth DO sensing capability across their field. The resolution of the optode and imaging system is discussed in much more detail by Larsen et al. [2011]. A video of one experimental trial is included as supporting information Video 1. Four individual frames from supporting information Video 1 are included as a supporting information figure in case the reader cannot view the video. 2.4. Imaging and Analysis of Dissolved Oxygen Fields Once calibrated, the large planar optode (80 cm tall by 60 cm wide) was installed in the flume, oriented with the long axis perpendicular to the open channel. The optode extended approximately 65 cm into the bed below the sediment-water interface (SWI). Images were taken in triplicate. Preliminary data processing was carried out with the software Look@RGB and consisted of averaging the triplicate images to reduce noise, then separating each averaged image into the red, green, and blue color bands. DO data analysis was undertaken using ImageJ [Schindelin et al., 2015] and Matlab (The Mathworks Inc., Natick, MA, USA). In order to generate useful metrics for the oxygen plume size and shape within the bed, the image sequences corresponding to each trial were loaded into Matlab, where they were filtered for noise and masked to remove the open channel from the image field. A pixel-to-area conversion factor was generated by measuring the number of pixels contained within length gradations placed within the image field. Pixels with measured oxygen saturation greater than 10% were counted and converted to an oxygenated area (Figure 2). Spatial moment analysis was then undertaken, wherein the first moment normalized by the zeroth moment corresponds to the position of the center of mass of the oxygen plume (Figure 2). Finally, in select cases, the ‘‘transition zone’’ (the area of the image between 25% and 75% oxygen saturation) was extracted from the image. In addition to steady state metrics, oxygen metrics were plotted over time. This allowed analyses of hyporheic zone dynamics, the most useful of which was the time taken for a given metric to rise or fall by a factor of 1=e, referred to here as e-folding time:

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Ae;r 5ðAf 2Ai Þ e21 1Ai

(5)

Ae;f 5ðAi 2ððAi 2Af Þ e21 Þ

(6)

where Ae;r is the 1/e-fold change in the oxygenated area in the acceleration case and Ae;f is the 1/e-fold change in the oxygenated area in the deceleration case. In both cases, Ai is the quasi steady state oxygenated area prior to the flow velocity change, and Af is the quasi steady state oxygenated area after the flow velocity change has taken place. e-folding time was determined by observing the plot of oxygenated area over time, and extracting the elapsed time from the initiation of the flow velocity change to the time when the oxygenated area equaled Ae;r or Ae;f , respectively. e-folding time was chosen as a metric because it is generally applicable for exponential processes, which is reasonable to assume given the first-order kinetics of microbially mediated consumption of oxygen as well as the diffusive transport of oxygen (analytical solutions to simple models of both processes are exponential functions) [Buss et al., 2005]. While it is perhaps a less appropriate metric for an advective transport dominated system, it was used for both the acceleration and deceleration cases in order to facilitate direct comparisons between the two. 2.5. Multiphysics Numerical Modeling of Flow and Transport Processes Throughout the Flume Two-dimensional (2-D) numerical modeling of flow and reactive transport was executed in order to explore the processes at work in the bed under both steady state and dynamic conditions. The models represent a vertical 2-D cross section similar to the geometry of the flume; the flume was narrow, deep, and imaged along one side (perpendicular to the direction of flow), effectively showing a similar vertical section. The model consisted of two zones (Figure 3) and followed the approach of Cardenas and Wilson [2007a, 2007b]. The turbulent flow field in the overlying channel zone was solved first. The pressure calculated along the bed surface by this model was used as a boundary condition for a separate groundwater flow and DO reactive transport zone in the underlying sediment. 2.5.1. Turbulent Flow Zone Modeling The channel geometry replicated the geometry from the flume experiments. The turbulent flow in the channel is governed by the Reynolds-averaged Navier-Stokes equations with k2x turbulence closure scheme [Wilcox, 1991]. The governing equations are: @Ui 50 @xi

(7)

Figure 3. Model schematic diagram. (a) The turbulent flow zone, representing the surface channel. (b) Output from the turbulent flow zone: a bed pressure profile. This bed pressure profile forms the top boundary condition for (c), the biogeochemical reactive transport zone, representing the sand bed. Flow lines within the lower zone (c) are shown in white. Examples of the meshes for the two zones are shown in the inset images.

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@Ui @P @ 52 1 2lSi j 2qu0 j u0 i @xj @xi @xj

(8)

where Ui ði51; 2Þ and ui ði51; 2Þ [L/t] are the time-averaged and instantaneous velocity components in the xi ði51; 2Þ [L] directions, P[M/Lt2] is the time-averaged pressure, q[M/L3] is the fluid density, andl [M/Lt] is the dynamic viscosity. Sij [t21] is the strain rate tensor, which is defined as: 1 @Ui @Uj (9) Sij 5 1 2 @xj @xi The turbulent flow domain was bounded by a mass flux inlet on the left: qun 5

_ m A

(10)

_ where un [L/t] is the fluid velocity normal to the inlet, m[M/t] is the mass flow rate specified to match the flow conditions from the experiments, and A[M2] is the cross-sectional area of the inlet face. The domain was bounded by a zero-pressure outlet on the right, a zero-shear/slip boundary for the water surface, and no-slip condition for the bed. Such boundary conditions have been shown to accurately replicate coupled open channel flow and hyporheic flow [Janssen et al., 2012]. Modeled water properties included a density q of 998.2 kg/m3 and viscosity l of 1.003 3 1023 kg/m s. The sediment-water interface was assigned a roughness height of 1 mm. Meshing consisted of quadrilateral elements with a maximum aspect ratio of 2.1 and a mean aspect ratio of 1.03. The mesh element size was refined near the sediment-water interface. The overall mesh element count started arbitrarily and was increased and rerun on a sample model until solutions converged to the same answer. A final element edge length of 0.5 cm for most of the domain and 0.25 cm along the sediment-water interface was selected, with 20,500 total elements. The model was implemented via the finite-volume approach in the commercial computational fluid dynamics software, Ansys FLUENT (ANSYS Inc., Canonsburg, PA, USA). Each overall model run contained two separate turbulent zone model runs: one at the lower surface channel flow velocity and one at the higher surface channel flow velocity. This provided two output bed pressure profiles representing high and low flow conditions. The surface flow conditions were modeled this way, as a succession of separately modeled steady state conditions, in order to closely replicate the rapid flow velocity changes imposed during the flume experiments and to match the instantly switched steady state subsurface flow model described in the following section. 2.5.2. Biogeochemical Advection-Dispersion-Reaction Modeling Zone The bed pressure field output from the turbulent flow zone served as the upper boundary condition for the lower flow and reactive transport zone, representing the bed of the flume. The reactive transport zone was modeled to represent the geometry of the flume sediment. In this zone, the groundwater flow equations were solved to calculate the hyporheic flow field: @qi 50 @xi qi 52

kp @P l @xi

(11) (12)

where qi [L/t] is the Darcy flux or specific discharge, / [–] is the porosity, t [t] is time, and kp [L2] is the intrinsic permeability. Instead of solving a truly dynamic flow field, the subsurface domain is simply switched between the two bed pressure profiles provided by surface domain model. In this way, the flow field in the reactive transport zone is switched from the steady state flow field generated from the lower flow velocity turbulent zone output to the flow field generated by the higher flow velocity turbulent zone output, or vice versa. This mimics a system with incompressible fluid and incompressible matrix, where a change in pressure field propagates through the bed instantaneously [Boano et al., 2007]. Research shows that pressure perturbations propagate extremely rapidly in porous media, so this assumption is considered valid [Cardenas and Jiang, 2011]. The left, right, and bottom boundaries were no-flow boundaries, q50, representing the flume walls. The top boundary was treated as an open boundary with an external concentration of oxygen of 8.86 mg/L representing 100% oxygen-saturated surface water. The pressure field from the turbulent flow model was linearly interpolated from the resolution of the surface domain model to the resolution of the subsurface domain model mesh and imposed along the bed surface. Porosity / was assumed to be 0.3.

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Because permeability kp and pressure gradient drive subsurface flow velocity, the model is sensitive to the choice of permeability. In order to closely mimic the flume experiments, it was important that we select a permeability similar to that of the flume sand. Column experiments were carried out, however unusually high permeabilities were recorded using this method. We believe that compaction and lack of biofilm development caused these anomalously high permeability readings in the column experiments. In order to more closely approximate the flume sand permeability, we carried out a series of dye tracer tests in the flume to determine an approximation of the subsurface flow velocity field. We then modified the permeability value used for the model until we achieved similar subsurface flow velocity fields. The permeability value that gave us the best approximation of the experimental subsurface flow velocity field was 4 3 1028 m2, which is reasonable for the coarse sand matrix used. The simulated groundwater flow field was then used as input to the advection-dispersion-reaction equation to model the transport and consumption of oxygen in the bed: @c @ 2 c @ui ðx; tÞc 1R 5Dðx; tÞ 2 2 @t @xi @xi

(13)

where c [M/L3] is the solute concentration (in this case oxygen), t [t] is time, D[L2/t] is the dispersion tensor, u[L/t] is the average linear velocity of the subsurface water, and R[M/L3t] is the reaction rate. D is calculated following: hDij 5aT jqjdij 1ðaL 2aT Þ

qi qj 1/ sDm dij jqj

(14)

where i, j 5 1, 2, aL and aT [L] are transverse and longitudinal dispersivities, s [–] is the tortuosity factor, dij is the Kronecker delta function, and Dm [L2/t] is the molecular diffusion coefficient. aL is assumed as 3 cm (several sand grain diameters) and aT is considered to be aL/10. s is /1=3 . A simple first-order reaction term, R5ðn K cÞ, where K[1/t] is the reaction coefficient was used. Based on trial and error, a value of K5 21 h21 led to results that qualitatively matched the experimental observations most closely. The initial conditions for the system were cx;t50 50mg/L DO and qx;t50 50. The initial pressure field was prescribed as above, and the model was allowed to run for 24 h to achieve steady state oxygen concentration conditions before any surface channel velocity perturbation was applied. The mesh used for the subsurface domain was a free triangular mesh with a maximum element size of 0.8 cm, resulting in a total of 172,750 elements. A backward-differentiation formula time-stepping scheme was used. The model was solved via the generic finite-element solver COMSOL Multiphysics (COMSOL Inc., Burlington, MA, USA).

3. Results and Discussion The results of this study comprise three primary foci, corresponding to the three major research questions: steady state experiments controlled by surface channel velocity and corresponding modeling, dynamic experiments involving response time to surface channel flow perturbations and corresponding modeling, and the morphology of the transition zone under dynamic conditions. 3.1. Hyporheic Redox Zone Sensitivity to Surface Flow Velocity—Steady State Our first research question asked: how does surface channel flow velocity control the pattern of dissolved oxygen in the hyporheic zone? Our results show that while the extent of oxygen penetration into the hyporheic zone beneath dune bed forms is a function of longitudinal position beneath the bed form, as has been observed in prior studies [Kessler et al., 2012; Thibodeaux and Boyle, 1987], it is also strongly controlled by surface channel velocity, both in terms of total oxygenated area and penetration depth (Figure 4). In general, higher surface channel flow velocities lead to larger oxygen plumes in the hyporheic zone. These plumes expand laterally as well as vertically, with the vertical position of the plume center of mass deepening with increased surface channel flow velocity. While this general pattern has been shown in smaller scale cylindrical tank experiments [Booij et al., 1991; Huettel and Gust, 1992] and later in smaller scale flume experiments [Kessler et al., 2012], our work represents a novel view of the process within the context of larger scale bed forms in coarser riverbed sediment with deeper oxygen penetration compared to prior studies.

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Figure 4. (a) Oxygenated area and (b) oxygen plume center of mass depth versus Reynolds Number from flume experiments. Both metrics rise with increasing surface channel velocity and thus Reynolds Number.

In addition to simply scaling linearly with increased surface channel velocity, both oxygenated area and penetration depth exhibit a nonlinear relationship, with asymptotic behavior at higher surface channel flow velocities. This is at least partly due to a change in the morphology of the oxygen plume itself. At lower surface channel flow velocities, the oxygenated areas in the hyporheic zone form a series of distinct plumes contained beneath single bed forms. As the surface channel velocity rises, these single distinct plumes spill over into adjacent bed forms, forming a more contiguous larger scale oxygen plume (Figure 5 and supporting information Video 1). Both the scaling relationship (Figure 4) and the change in plume morphology (Figure 5) are also exhibited by our numerical models, though the size of the plume is not perfectly reproduced. In addition to agreeing with the scaling and morphology changes shown in our experimental data, the model provides some insight into the mechanisms at work. The size and depth of the plume are functions of the rate of advection of oxygen through hyporheic zone and the rate of consumption of oxygen in the hyporheic zone by the microbes present in the sediment. The ratio of these two factors is the oxygen Dam€hler number [Zarnetske et al., 2012]. Varying any parameter that changes this oxygen Damko €hler number ko impacts the area and depth of the oxygen plume [Marzadri et al., 2012]. For example, increasing permeabil€hler numity, increasing surface channel velocity, and decreasing reaction rate all lower the oxygen Damko € ber (Table 2), resulting in increased oxygen plume area and depth. Damkohler numbers were calculated using the method outlined by Azizian et al. [2015] and [Elliott and Brooks, 1997b] following Da5

sT sR

(15)

€hler number, sR [t] is the characteristic reaction time scale calculated by taking where Da [–] is the Damko the reciprocal of the reaction rate coefficient (1/h), and sT [t] is the transport time scale through a bed form. Instead of utilizing a characteristic time scale generalized across the entire bed, we instead chose to model the age of the water within the bed using direct simulation of groundwater age [Cardenas et al., 2016; Glee€hler field for the modeling son et al. 2016; Goode, 1996]. The results of this modeling produce a Damko €hler field over the domain to generate a characteristic domain (Figure 6). We take the average of the Damko €hler number for each surface channel velocity scenario. Boano et al. [2015] state a range of flow time Damko scales for dune and ripple bed forms of roughly 1023 to 5 3 1021 h, while Azizian et al. [2015] utilize reac€hler numbers tion time scales from 1022 to 3.8 3 1021 h. Comparing these values gives a range of Damko €hler numbers are higher than these, but they are not directly comparable, from 2.6 3 1023 to 50. Our Damko €hler field is made up of local points within the domain, while more traditional formulations of as our Damko €hler number represent only the total transport time experienced by some representative individthe Damko ual packet of water from the time it enters the hyporheic zone to the time it is released back into the sur€hler field produced by our numerical model provides a much more nuanced face channel. The full Damko

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Figure 5. Comparison of experimental and modeled low surface channel velocity and high surface channel velocity dissolved oxygen images. (a) Low surface channel velocity experimental scenario, showing an oxygen plume that is fully contained within a single bed form. (b) Experimental scenario with 3 times higher surface channel velocity, showing an oxygen plume that is beginning to extend beyond the boundaries of a single bed form. Similar to the experimental results, (c) low surface channel velocity model result and (d) high surface channel velocity model result.

view of the competition between reaction time scale and transport time scale within the hyporheic zone. Our reactive transport model also helps us gain insight into the change in morphology of the oxygen plume as the surface channel flow velocity rises. The flow lines across our model domain highlight nested flow cells (Figure 3c). As the surface channel velocity increases, the oxygen plume is driven down from the smaller isolated bed form-scale flow cells into progressively larger reach-scale cells. While the surface channel velocity in a stream has been known to control the residence time distribution of hyporheic water [Buffington and Tonina, 2009; Cardenas et al., 2008; Cardenas and Jiang, 2010], highresolution bed form-scale analysis of the redox conditions within the hyporheic zone are lacking, with only a few studies utilizing the planar optode technique in hyporheic-analog systems [Kessler et al., 2012]. Largescale nitrogen processing rates have been estimated [Gomez-Velez et al., 2015], and both field studies and numerical models including aerobic respiration have been carried out at the meter scale [Zarnetske et al., 2011b]. In the context of these larger scale studies, high-resolution understanding of the processes occurring at the bed form scale in the hyporheic zone remains a topic of interest. Quick et al. [2016] recently investigated the bed form-scale distribution of DO and nitrate, but were limited to approximately 40 discrete sample locations per dune. Our steady state studies combine the focus on biologically mediated DO consumption and the resultant spatial DO patterns that have been studied as far back as Poole and Stewart [1976], with the fine-resolution bed form-scale laboratory work and numerical modeling of Rutherford et al.

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Figure 6. Modeled Damkohler number fields for three representative flow velocities. The yellow line represents Da 5 1.

[1993, 1995] and more recently Quick et al. [2016]. While Poole and Stewart [1976], Valett et al. [1990], and others characterize spatial patterns of dissolved oxygen in the hyporheic zone, they do so at coarse meterscale depth or lateral transects, and at single times in field sites. Our observation and numerical modeling scale is uniquely positioned between these extremes of scale. In addition, through the use of large planar optodes, we achieve coverage of a full dune at a very high resolution without impacting the naturally occurring flow field. Due to the limitations of the planar optode system, we necessarily collect DO data along the wall of the flume. This requires that we make two important assumptions. The first is that the longitudinal plane is representative of what is happening throughout the width of the bed form. The second is that preferential flows or any other wall effects are negligible. We believe both of these assumptions to be valid within the context of our experimental system. Due to the nature of the quartz sand substrate, it is possible to observe dye from a dye tracer test that is several grain diameters away from the flume wall. During tracer tests, the dye a few grain diameters back from the wall was not observed to lead or lag the dye very close to the wall significantly, lending support to the validity of our assumptions. 3.2. Hyporheic Redox Zone Sensitivity to Surface Flow Velocity Dynamics—Perturbation Response Time Having explored the strong control exhibited by surface channel flow velocity on hyporheic oxygen under steady state conditions, here we explore our second research question: how fast do the hyporheic oxygen conditions respond to surface channel flow perturbations? From our steady state experiments, we know that over long enough periods of time, the oxygen conditions in the hyporheic zone of our flume will eventually change from one configuration to another following a change in surface channel flow velocity. The focus of our dynamic experiments is to understand how fast the hyporheic configuration changeover can take place. Therefore, we subjected the flume to near-instantaneous surface channel flow velocity perturbations, both accelerations and decelerations. These took the form of step changes, where the transition from steady low surface channel flow velocity to steady high surface channel flow velocity, or vice versa, took less than 5 min. We imaged the hyporheic zone before, during, and after the flow change, and primarily used the metric of oxygenated area e-folding time to examine hyporheic DO condition response times. We found that response times were generally longer in the surface channel velocity deceleration scenarios than they were in the surface channel velocity acceleration scenarios (Figure 7 and supporting information Video 1). In addition, we observed a possible slight sensitivity to the magnitude of the velocity change to the response time, with response time decreasing slightly with increasing velocity perturbation magnitude in the acceleration scenarios and increasing slightly with velocity perturbation magnitude in the deceleration scenarios (Figure 7). Due to the small number of replicates of these experiments, these possible trends are not statistically significant. Overall, response times in the hyporheic DO conditions were on the order of 10s-of-hours, more than two orders of magnitude slower than the surface channel velocity perturbations. We were able to observe similar patterns in our dynamic numerical modeling, though the overall response

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Figure 7. Experimental response time comparison. Response times were generally longer in the (b) surface channel velocity deceleration scenarios than they were in the (a) surface channel velocity acceleration scenarios. Slight trends linking the magnitude of the velocity change to the response time are observed, with response time decreasing slightly with increasing velocity perturbation magnitude in the acceleration scenarios and increasing slightly with velocity perturbation magnitude in the deceleration scenarios.

times were a good deal faster, on the order of 2–8 h (Figure 8). Even at 2–8 h, the modeled hyporheic oxygen conditions responded much slower than the surface channel velocity perturbations. Reasons for the disconnection between simulation and experimental response times are open to speculation. We tested the numerical simulations with a Monod kinetic model instead of the simple first-order model we decided to use for this study, and saw no significant difference in response time, so the simplicity of our kinetics is unlikely to be the reason. The biggest factor that differentiates the simulations from the experiments is the nature of the microbes themselves. Because the chemistry we observe is almost entirely biologically mediated, it is likely that the microbial community structure within the bed undergoes its own dynamism, a process which is unaccounted for in the simulations and may exert strong influence on the observed response times. In addition to e-folding time, we observed the rate at which the oxygen front proceeded along a single flow path, and compared that to the average linear velocity along that flow path. The results agree with the e-folding times: the fronts advance and retreat faster with larger changes in surface channel velocity (Figure 9). Many prior studies have explored the way in which dynamic flow events impact hyporheic exchange. Gariglio et al. [2013], Wondzell and Swanson [1996], Larsen et al. [2014], and others explored natural flow variation from the seasonal to diel to single-storm scales, while Nowinski et al. [2012] focused on flood responses, and Gerecht et al. [2011] and Sawyer et al. [2009] honed in on the impact of human controlled river discharge fluctuations on hyporheic exchange. This prior exploration all shows that river discharge is highly variable over time scales from minutes to years. Connecting the highly variable nature of river discharge with our observations regarding the connection between surface channel flow velocity and hyporheic oxygen conditions implies Figure 8. Model response time comparison. While modeled response times that in order to understand the hyporheic were faster than experimental response times across the board, the overall patterns of longer response times in the surface channel velocity deceleration scezone, we must understand the way the narios (blue) than in the surface channel velocity acceleration scenarios (red) chemical conditions respond to changing were observed in both the experimental and model results. Similarly, slight flow conditions, and the time scales of trends linking the magnitude of the velocity change to the response time, with response time decreasing slightly with increasing velocity perturbation magnithese responses. These prior studies, tude in the acceleration scenarios and increasing slightly with velocity perturalong with Cardenas and Jiang [2011], bation magnitude in the deceleration scenarios, were also observed in both indicate that the flow field in the the experimental and model results.

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hyporheic zone responds nearly instantaneously to changes in the surface channel flow, a situation we mimic in our numerical model. However, the chemical conditions within the hyporheic zone are governed not only by these flow conditions, but also by the microbial reactions taking place within the hyporheic zone. As a result, the oxygen conditions in the hyporheic zone respond much more slowly to external forcing than do the flow conditions. We expect that our experiments fall on the slow side of Figure 9. (a) Average linear velocity along a selected flow path and the (b) oxygen front what might be observed in velocity along the same flow path for various surface flow acceleration and deceleration nature, as our water represents scenarios. a fairly clean river with low nutrient content and slow flow velocities. We expect that rivers impacted by agricultural runoff or other sources of elevated nutrients, as well as higher river velocities, may respond more rapidly than our experiments do. However, even with this in mind, the response times shown in our experiments and numerical models fall within a particularly interesting range of values. They are fast enough that a single storm, flood pulse, or dam release lasting no more than a few hours can cause large changes in the oxygen conditions in the hyporheic zone, and these changes can last for hours to days after the perturbation has passed. In addition, the response times are slow enough that it may be possible for cyclic surface channel velocity variations, like those caused by diel cycles of evapotranspiration, to be either damped out or to set up resonance in the hyporheic zone, leading to a highly dynamic system with oxygen conditions undergoing large, rapid changes. Moving redox zones also tie in to recent research which has shown that anoxic metabolism byproducts can often be found in primarily oxic sediment zones. This is commonly explained through the concept of redox microzones or microsites [Briggs et al., 2015; Kessler et al., 2014; Zarnetske et al., 2012], but may also be—at least in part—a function of moving redox zone boundaries due to small perturbations to the surface channel flow velocity, forming a temporal redox hot moment rather than micro hotspots. 3.3. Transition Zone Morphology Dynamics The concept of redox microzones and hot moments comes up again when, in addition to examining changes in the size and position of the oxygen plume within the hyporheic zone, we explore our third research question: how does the spatial pattern of hyporheic dissolved oxygen change under dynamic conditions? To answer this question, we zoom in on the morphology of the oxygenated zone edge, an area we refer to as the transition zone. The transition zone is defined here by an oxygen saturation level between 25% and 75%. These boundaries were not chosen particularly due to biochemical relevance, but simply because they clearly demonstrate the differences between the various flow regimes tested. Under steady state conditions, the transition zone shows relatively little fluctuation, though it is not quite the perfectly uniform arc predicted by our numerical model. This is likely due to small inhomogeneities in the flow field, in the distribution of organic carbon, and in the microbes within the sediment. More interestingly, under dynamic conditions, the transition zone has highly variable morphology. The transition zone is sharp and regular in the accelerated surface channel flow velocity scenarios, while in the decelerated surface channel flow velocity scenarios it is both much wider and much more ragged and patchy, showing potential temporary redox microzones, where oxygenated sediments intermingle with oxygen poor sediments (Figure 10 and supporting information Video 1). For the accelerated case, the oxic front will advance at a rate controlled by the advective velocity, damped by microbial oxygen consumption. For the decelerated case, the oxic front will retreat in a manner controlled primarily by the rate of microbial respiration, as well as

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Figure 10. Transition zone comparison. Examples of two distinct transition zone morphologies are shown. Black areas have oxygen saturation between 25% and 75%. (a) Generated from a planar optode dissolved oxygen image taken shortly after a surface channel velocity acceleration, while the oxygen plume is growing. (b) Generated from a planar optode dissolved oxygen image taken shortly after a surface channel velocity deceleration, while the oxygen plume is shrinking. (a) Has a sharp and well-defined transition zone, while (b) has a wide and ragged transition zone.

potentially some diffusion/dispersion (Figure 9). In addition, the difference between the two scenarios becomes more pronounced with increased velocity perturbation magnitudes (Figure 11). The sediment and flow field within our flume is relatively homogeneous, so the ragged edges of the transition zone, particularly in the deceleration scenarios, indicate that on a millimeters-to-centimeters scale, the rate of oxygen consumption in a dynamic hyporheic zone may be highly variable. It is possible that this feathered zone is initiated by uneven distribution of walnut shell in the flume bed, however organic carbon sources in actual riverbeds are highly unevenly distributed, so this phenomenon is not likely confined to these experiments. While the exact sizes and morphologies we produce here are likely to be highly specific to our experimental setup, the overarching trend of sharper, cleaner transition zones in acceleration cases as compared to broader, patchier transition zones in deceleration cases may be more broadly applicable.

Figure 11. (a) Transition zone area for surface channel velocity acceleration and (b) deceleration scenarios. The transition zone is larger in the surface channel velocity deceleration scenarios than in the surface channel velocity acceleration scenarios. In addition, the difference between the two scenarios becomes more pronounced with larger velocity changes. For these plots the central red line denotes the median, the top and bottom of the box denote the 75th and 25th percentile, and the whiskers denote the maximum and minimum values observed. The number of observations included in each box are denoted by ‘‘n5’’ on the plots.

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4. Implications Our study shows that the hyporheic zone is a dynamic place with response time scales that can be much longer than those in the open river channel. This simple fact has myriad potentially profound impacts on hyporheic zone processes of broad interest. The mobility of many metals is controlled by their redox state. A resulting example of the importance of hyporheic dynamism is that the large and long-lived changes in the redox conditions of the hyporheic zone that we see in our experiments may contribute to unexplained variability in metals transport seen in real-world examples. Harvey and Fuller [1998] attempt to model the mobilization of manganese in a stream contaminated by mine drainage, a common environmental issue both within the United States and globally. Their results indicate that relatively narrow manganese oxidizing areas within the hyporheic zone make significant contributions to the manganese mass balance at the drainage basin scale. In addition, they note that less manganese was removed, and that field analysis of solute concentrations showed more variability than was predicted. It is possible that both of these anomalies were affected by unaccounted-for hyporheic dynamism. Harvey and Fuller [1998] state that it is possible that storm events may have caused variations in tributary input to their stream of interest. While that influx changes the solute concentrations in their stream, it also would have driven changes in the size of the redox zones where the manganese-oxidizing bacteria of interest would be expected, reinforcing variations in the manganese processing capacity of the whole riparian system. As a result, if we are to attempt to quantify riparian systems with regard to such metals-processing capacities, we need to consider not only the more complex residence time distributions that have largely overtaken the transient storage zone models of the past, but also the dynamics present both in the residence time distributions, and also in the size and location of the redox zones present in the hyporheic zone. In addition to sequestering and releasing metals, the hyporheic zone is currently a key focus of research on nutrient processing, with particular emphasis on nitrate uptake. As dependence on fertilizers grows, being able to accurately predict the impact that nitrate-laden runoff will have on streams and rivers as well as their eventual lake and ocean destinations becomes increasingly necessary. This has led to a large volume of research pointed at quantifying the various nitrogen transformations taking place in riparian systems, and their responses to outside stimuli. As an example, Mulholland et al. [2008] attempt to correlate realworld denitrification rates with adjacent land use and stream nitrate concentration. While their results show significant effect in both cases, they also have a very large range in the rates, which is very common among similar studies. One possibility is that typical field measurements are carried out over relatively short periods of time, and at what are often described as near steady state flows. While the flows may have been at or near steady state, our work shows that the hyporheic zone may well not be, leading to variation in the assumed steady state redox conditions in the bed. In order to avoid such issues in similar field experiments it would be necessary to, at minimum, measure the dissolved oxygen profile in the bed over time in order to ascertain that the redox zonation present is in a relatively stable configuration prior to sampling. Beyond that, a characterization of the stream’s velocity variation and subsequent hyporheic redox condition variation would be necessary to truly choose a representative sampling period. In more practical terms, our work shows that flow velocity perturbations ranging from 1.25 to 3 times the initial flow velocity create significant responses in the hyporheic zone that last on the order of 1 to 2 days. As a result, any field sampling campaign subject to hyporheic zone chemistry should be considered in light of this dynamism. It is important to bear in mind that our experiments and models maintain consistent water depth, only varying velocity. In addition, we treat the system as effectively two dimensional. As such, we do not capture any hyporheic zone disturbance than would come from hydraulic gradients driven by the rising stage associated with flood events, particularly in the lateral direction. Despite our focus on capturing and understanding hyporheic zone dynamics, we know that much field sampling will continue to be carried out with the idea of using a small number of samples to characterize a long period of assumed steady state river function. In this light, we recommend that prior to sampling from a river or hyporheic zone, the hydrograph for the river should be observed, and attempts should be made not to sample within a few days of small perturbations, and longer for large disturbances, particularly as with large disturbance the bed forms can be rapidly and significantly mobilized or changed to other configurations, which our study does not consider. While the microbially mediated transport and processing of solutes constitutes a major ecosystem service provided by hyporheic zones, and one that is receiving more and more scientific scrutiny over time, the

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hyporheic zone is also the habitat for larger organisms. The ability for any individual area within the hyporheic zone to harbor macroinvertebrate life is largely a function of temperature and dissolved oxygen availability [Cardenas et al., 2016; Greig et al., 2005]. Increasing study has been given to long-term trends in macroinvertebrate population structures [Pace et al., 2013]. One of the major findings of this work is that the increasing variability in river conditions over time leads to decreasing macroinvertebrate diversity. Our examination of the long time scale of hyporheic disturbances after relatively small surface channel perturbations may help to explain why an increased incidence of small perturbations could have a significant impact on the habitability of the hyporheic zone for specific species. In particular, less-mobile organisms could easily find themselves in an oxygen-deprived location for an extended period of time after even a short dry spell corresponding with a decrease in stream velocity, similar to what Marzadri et al. [2013a] observed with regard to water temperature. In general, the high sensitivity of the bed oxygen conditions to surface channel velocity, and the extended response times observed in the hyporheic zone can lead to large and longlived habitat changes.

5. Summary, Conclusions, and Recommendations The use of a large flume, high temporal and spatial resolution dissolved oxygen observation, and tightly controlled dynamic conditions combined with two-dimensional numerical modeling allowed us to demonstrate and examine four key processes related to hyporheic zone dissolved oxygen dynamics. This study explored the direct control that stream channel velocity exerts on oxygenated hyporheic zone area, including the transition from individual bed form-scale oxygen plumes to larger continuous multi-bed form-scale plumes. We conducted two-dimensional numerical modeling to explore the parameters which €hler exert control on the size and depth of the oxygen plume, finding that parameters affecting the Damko number, through either the advective time scale or reactive time scale, exerted strong control. Because advective time scales are a function of flow path length and fluid velocity, heterogeneity of the hydraulic properties of the bed material should be explored in the future. Heterogeneities, particularly large sources of organic carbon, may lead to ‘‘shadows,’’ wherein the slow movement of water combines with an excess of dissolved organic carbon to create a microzone which reaches anoxic conditions in a portion of the bed where oxic conditions dominate [Bourke et al., 2014]. Bourke et al. [2014] show that the extents of these microzones are partly a function of flow velocity, which opens the door for dynamic responses similar to those we show in dune bed forms. We examined dynamic hyporheic behavior and the widely differing time scales of hyporheic and stream channel response, finding longer response times in cases of deceleration of the surface channel velocity compared to acceleration. We also found that the response time may vary slightly with the magnitude of acceleration or deceleration of the surface channel flow velocity, more experimental repetitions should be conducted to determine if these possible trends are real. Both of these observations are replicated by the numerical model, though the response times predicted by the model are shorter in all cases than the experimental observations. These long and variable hyporheic response times open the door to complex cycles, particularly in rivers with highly variable discharge. We speculate that dynamism within the microbial communities of the hyporheic zone driven by changes to the chemical environment they occupy may be a major factor in determining response times in the hyporheic zone, and thus represents a prime area for further study. We observed changes in the transition zone along the edge of the oxygen plume, showing a well-defined and smooth transition zone in accelerated velocity conditions and a broad and somewhat patchy transition zone in decelerated velocity conditions. Again, these variations in morphology with surface channel dynamics imply the possibility for highly dynamic real-world conditions, particularly in rivers with highly variable discharge. They also indicate the potential for variations in rates of microbial metabolism over small spatial scales, a topic that deserves much more scrutiny than it has received to date. Because so much of the oxygen plume size and depth is controlled by stream velocity, it is also worth exploring the impact of more complicated surface channel flow variations in the stream than those we recreated, particularly sinusoidal diel cycling [Marzadri et al., 2013b; Tonina et al., 2015]. Finally, we looked only at longitudinal variations in pressure and flow fields in our flume and models. Future work concentrating on lateral pressure fields or integrated three-dimensional monitoring and modeling may be of use, helping to complete

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our conceptual model of parameters controlling the oxygen conditions in the hyporheic zones of streams and rivers. Acknowledgments The National Science Foundation (EAR0955750 and EAR-1344547) and the Geology Foundation at the University of Texas at Austin supported this study. MHK was partly supported by a Geological Society of America student grant. We thank Lizhi Zheng for her assistance with the modeling, and the entire Cardenas research group for their help moving several tons of sand and water around. All experimental work was conducted at the UT Morphodynamics Laboratory in the Center for Water in the Environment. Access to river water was provided by Kevin Anderson of the Austin Water Center for Environmental Research. The planar optode imaging was made possible with guidance from Morten Larsen and Ronnie Glud of the University of Southern Denmark. All dissolved oxygen and model results are freely available from the authors upon request.

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