Benthic Uptake Rate due to Hyporheic Exchange: The Effects of Streambed Morphology for Constant and Sinusoidally Varying Nutrient Loads

Tonina, Daniele; Marzadri, Alessandra; Bellin, Alberto

doi:10.3390/w7020398

Cited by 17 publications

(15 citation statements)

References 61 publications

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“…As a water parcel exits the HZT and returns to the stream, its final nitrate concentration (denoted here by the function

C_{HZT - normalN {normalO}_{3}^{-}} true(τ; " chemistry " true)

, units mol m −3 ) will depend on the water parcel's travel time through the hyporheic zone (

τ

, units s) conditioned on subsurface biogeochemical reactions that consume and produce nitrate (denoted here by the shorthand “chemistry”). Provided there is no mixing of mass across or within HZTs (i.e., mass transport occurs only by advection through the HZT, discussed further in section 2.2), mass balance over a single submerged and periodic bed form yields the PASS model for nitrate uptake velocity [ Rutherford et al ., ; Grant et al ., ; Azizian et al ., ; Tonina et al ., ]:

v_{f} = q_{H} true[{\overset{C}{true}}_{HZT - normalN {normalO}_{3}^{-}} - 1 true]

{\overset{C}{true}}_{HZT - normalN {normalO}_{3}^{-}} = \frac{1}{C_{normalS - normalN {normalO}_{3}^{-}}} \int_{0}^{\infty} C_{HZT - N O_{3}^{-}} (τ; “ chemistry ”) \times E (τ) d τ

…”

Section: Pumping and Streamline Segregation (Pass) Model For Nitrate mentioning

confidence: 99%

“…Indeed, based on 15 NH 1 4 stream seeding studies, Peterson et al [2001] concluded that ammonium is removed primarily by assimilation and sorption to sediments, and ''secondarily by nitrification.'' These model limitations can be Water Resources Research 10.1002/2016WR020048 addressed by increasing model complexity, for example by adding: (1) an additional term to the mass balance equation for nitrate (equation (4b)) to account for the kinetics of nitrate assimilation [e.g., see Birgand et al, 2007]; (2) a rate equation to the biokinetic model for the evolution of dissolved and/or particulate organic carbon concentration with travel time [Zarnetske et al, 2012]; and (3) additional terms to the mass balance equation for ammonium (equation (4c)) to account for adsorption and assimilation [Thibodeaux and Mackay, 2011].…”

Section: 1002/2016wr020048mentioning

confidence: 99%

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Ambient groundwater flow diminishes nitrate processing in the hyporheic zone of streams

Azizian

Boano

Cook

et al. 2017

Water Resources Research

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Modeling and experimental studies demonstrate that ambient groundwater reduces hyporheic exchange, but the implications of this observation for stream N‐cycling is not yet clear. Here we utilize a simple process‐based model (the Pumping and Streamline Segregation or PASS model) to evaluate N‐cycling over two scales of hyporheic exchange (fluvial ripples and riffle‐pool sequences), ten ambient groundwater and stream flow scenarios (five gaining and losing conditions and two stream discharges), and three biogeochemical settings (identified based on a principal component analysis of previously published measurements in streams throughout the United States). Model‐data comparisons indicate that our model provides realistic estimates for direct denitrification of stream nitrate, but overpredicts nitrification and coupled nitrification‐denitrification. Riffle‐pool sequences are responsible for most of the N‐processing, despite the fact that fluvial ripples generate 3–11 times more hyporheic exchange flux. Across all scenarios, hyporheic exchange flux and the Damköhler Number emerge as primary controls on stream N‐cycling; the former regulates trafficking of nutrients and oxygen across the sediment‐water interface, while the latter quantifies the relative rates of organic carbon mineralization and advective transport in streambed sediments. Vertical groundwater flux modulates both of these master variables in ways that tend to diminish stream N‐cycling. Thus, anthropogenic perturbations of ambient groundwater flows (e.g., by urbanization, agricultural activities, groundwater mining, and/or climate change) may compromise some of the key ecosystem services provided by streams.

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“…As a water parcel exits the HZT and returns to the stream, its final nitrate concentration (denoted here by the function

C_{HZT - normalN {normalO}_{3}^{-}} true(τ; " chemistry " true)

, units mol m −3 ) will depend on the water parcel's travel time through the hyporheic zone (

τ

v_{f} = q_{H} true[{\overset{C}{true}}_{HZT - normalN {normalO}_{3}^{-}} - 1 true]

{\overset{C}{true}}_{HZT - normalN {normalO}_{3}^{-}} = \frac{1}{C_{normalS - normalN {normalO}_{3}^{-}}} \int_{0}^{\infty} C_{HZT - N O_{3}^{-}} (τ; “ chemistry ”) \times E (τ) d τ

…”

Section: Pumping and Streamline Segregation (Pass) Model For Nitrate mentioning

confidence: 99%

Section: 1002/2016wr020048mentioning

confidence: 99%

Ambient groundwater flow diminishes nitrate processing in the hyporheic zone of streams

Azizian

Boano

Cook

et al. 2017

Water Resources Research

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show abstract

“…Stream water temperature plays an important role in aquatic ecosystems and is an important cue for organism behavior (Isaak, Wollrab, Horan, & Chandler, ; Jobling, ; Rice, Breck, Bartell, & Kitchell, ; Rieman et al, ), fish metabolism (Forseth & Jonsson, ; Isaak, Young, Nagel, Horan, & Groce, ; Mesa, Weiland, Christiansen, & Sauter, ; Railsback & Rose, ), and growth rates (Brett, ; Crozier, Zabel, Hockersmith, & Achord, ; Xu, Letcher, & Nislow, ). Stream water temperature controls dissolved oxygen concentrations, which may affect water quality and biogeochemically reactive solutes (Marzadri, Tonina, & Bellin, ; Marzadri, Tonina, & Bellin, ; Tonina, Marzadri, & Bellin, ; Webb, Hannah, Moore, Brown, & Nobilis, ) whereas high stream water temperatures may negatively affect industrial activity (Boogert & Dupont, ; Null, Viers, Deas, Tanaka, & Mount, ; Vliet, Vogele, & Rubbelke, ; Vliet, Yearsley, Ludwig et al, ). These studies indicate the value of accurate estimates of daily stream water temperatures for dam and water resource managers, ecologists, economists, and decision makers.…”

Section: Introductionmentioning

confidence: 99%

Estimation of daily stream water temperatures with a Bayesian regression approach

et al. 2017

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Stream water temperature plays a significant role in aquatic ecosystems where it controls many important biological and physical processes. Reliable estimates of water temperature at the daily time step are critical in managing water resources. We developed a parsimonious piecewise Bayesian model for estimating daily stream water temperatures that account for temporal autocorrelation and both linear and nonlinear relationships with air temperature and discharge. The model was tested at 8 climatically different basins of the USA and at 34 sites within the mountainous Boise River Basin (Idaho, USA). The results show that the proposed model is robust with an average root mean square error of 1.25 °C and Nash–Sutcliffe coefficient of 0.92 over a 2‐year period. Our approach can be used to predict historic daily stream water temperatures in any location using observed daily stream temperature and regional air temperature data.

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“…N 2 O is one of the most important greenhouse gases contributing, for example, to the stratospheric ozone destruction (Ravishankara et al, 2009). Current models of DO and DIN dynamics in the HZ assume either constant or periodically varying in-stream loads and constant reaction rate coefficients (Tonina et al, 2015), thus ignoring the impact of their variability Water Resources Research 10.1029/2018WR022525 on the overall dynamics. Uncertainty about the role of these driving forces (boundary and initial conditions) on the overall N 2 O emissions, together with paucity of available data (field measurements are often time-consuming and costly; Baulch et al, 2011), motivates the development of quantitative methods that leverage the knowledge of appropriate physical and chemical laws to statistically characterize the global N 2 O budget.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Forecasting of Nitrogen Dynamics in Hyporheic Zone

Boso

Marzadri

Tartakovsky

2018

Water Resources Research

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Nitrification‐denitrification processes in the hyporheic zone control the dynamics of dissolved inorganic nitrogen (DIN) species and can lead to production of nitrous oxide, which contributes to the greenhouse effect. We consider DIN dynamics in an advection‐dominated regime, wherein transport and reactions occur along streamlines crossing hyporheic sediments. Our focus is on the impact of uncertainty in both stream water quality and rate constants of the subsurface reactions on predictions of DIN concentrations. We derive equations for a joint probability density function (PDF), and corresponding marginal PDFs and cumulative distribution functions (CDFs), of the species concentrations. Their derivation requires a novel closure, which depends on the mean and (co)variance of the species concentrations. We use streamline coordinates to reduce the dimensionality of the PDF/CDF equations and, hence, the computational effort of solving them. For the sake of completeness, we also present similar equations in Cartesian coordinates. By providing a complete probabilistic description of species concentrations at the bedform scale, our PDF/CDF equations allow one to evaluate the impact of random spatiotemporal variability in the inputs on the DIN dynamics. They yield physically based prior distributions for data assimilation and can be deployed to guide measurement campaigns by identifying regions with the largest predictive uncertainty.

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Benthic Uptake Rate due to Hyporheic Exchange: The Effects of Streambed Morphology for Constant and Sinusoidally Varying Nutrient Loads

Cited by 17 publications

References 61 publications

Ambient groundwater flow diminishes nitrate processing in the hyporheic zone of streams

Ambient groundwater flow diminishes nitrate processing in the hyporheic zone of streams

Estimation of daily stream water temperatures with a Bayesian regression approach

Probabilistic Forecasting of Nitrogen Dynamics in Hyporheic Zone

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