Impacts of nonuniform flow on estimates of vertical streambed flux

Cuthbert, Mark O.; Mackay, R.

doi:10.1029/2011wr011587

Cited by 52 publications

(79 citation statements)

References 27 publications

(47 reference statements)

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“…[] demonstrate that errors in vertical flux estimates increase with the magnitude of a horizontal flow component. Cuthbert and Mackay [] on the other hand suggest that the existence of nonvertical flow components does not necessarily lead to erroneous flux estimates. Rather is it the form of the flow field (divergent or convergent flow lines) that determines whether the use of analytical 1‐D solutions is appropriate.…”

Section: Limitations Of the Lpml Methodsmentioning

confidence: 99%

Determining groundwater‐surface water exchange from temperature‐time series: Combining a local polynomial method with a maximum likelihood estimator

Vandersteen

Schneidewind

Anibas

et al. 2015

Water Resources Research

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The use of temperature-time series measured in streambed sediments as input to coupled water flow and heat transport models has become standard when quantifying vertical groundwater-surface water exchange fluxes. We develop a novel methodology, called LPML, to estimate the parameters for 1-D water flow and heat transport by combining a local polynomial (LP) signal processing technique with a maximum likelihood (ML) estimator. The LP method is used to estimate the frequency response functions (FRFs) and their uncertainties between the streambed top and several locations within the streambed from measured temperature-time series data. Additionally, we obtain the analytical expression of the FRFs assuming a pure sinusoidal input. The estimated and analytical FRFs are used in an ML estimator to deduce vertical groundwater-surface water exchange flux and its uncertainty as well as information regarding model quality. The LPML method is tested and verified with the heat transport models STRIVE and VFLUX. We demonstrate that the LPML method can correctly reproduce a priori known fluxes and thermal conductivities and also show that the LPML method can estimate averaged and time-variable fluxes from periodic and nonperiodic temperature records. The LPML method allows for a fast computation of exchange fluxes as well as model and parameter uncertainties from many temperature sensors. Moreover, it can utilize a broad frequency spectrum beyond the diel signal commonly used for flux calculations.

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Section: Limitations Of the Lpml Methodsmentioning

confidence: 99%

Determining groundwater‐surface water exchange from temperature‐time series: Combining a local polynomial method with a maximum likelihood estimator

Vandersteen

Schneidewind

Anibas

et al. 2015

Water Resources Research

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“…The solution to equation , assuming sinusoidal variation in surface temperature and constant temperature at depth in the streambed, can be expressed as [ Hatch et al ., ; Keery et al ., ; Stallman , ]:

T true(z, t true) = T_{0} + A \exp true(- a z true) \cos true(ω t - b z true)

where T 0 is the average surface temperature (°C), A is the amplitude of the temperature fluctuations at the surface (°C) and ω is the angular frequency, defined as

ω = 2 π / P

where P is the period of the temperature fluctuation (86,400 seconds for daily frequency). This solution can be applied with nonvertical (curvilinear) paths as long as fluxes in the directions orthogonal to the vertical do not result in a large divergence of energy fluxes within the space between the two sensors [ Cuthbert and Mackay , ]. In equation , the a and b terms can be expressed as:

a \approx \frac{- \ln true(\frac{A_{2}}{A_{1}} true)}{true(z_{2} - z_{1} true)} = \frac{- \ln true(\frac{A_{2}}{A_{1}} true)}{Δ z}

b \approx \frac{ϕ_{2} - ϕ_{1}}{true(z_{2} - z_{1} true)} = \frac{ϕ_{2} - ϕ_{1}}{Δ z}

where the subscripts 1 and 2 refer to the shallower and deeper temperature sensors, respectively, and ϕ 2 − ϕ 1 is the phase shift (rad) associated with two temperature sensors.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal variability of hyporheic exchange through a pool‐riffle‐pool sequence

Gariglio

Tonina

Luce

2013

Water Resources Research

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[1] Stream water enters and exits the streambed sediment due to hyporheic fluxes, which stem primarily from the interaction between surface water hydraulics and streambed morphology. These fluxes sustain a rich ecotone, whose habitat quality depends on their direction and magnitude. The spatiotemporal variability of hyporheic fluxes is not well understood over several temporal scales and consequently, we studied their spatial and temporal variation over a pool-riffle-pool sequence at multiple locations from winter to summer. We instrumented a pool-riffle-pool sequence of Bear Valley Creek, an important salmonid spawning gravel-bed stream in central Idaho, with temperature monitoring probes recording at high temporal resolution (12 minute intervals). Using the thermal time series, weekly winter season seepage fluxes were calculated with a steady-state analytical solution and spring-summer fluxes with a new analytical solution that can also quantify the streambed thermal properties. Longitudinal pool-riffle-pool conceptualizations of downwelling and upwelling behavior were generally observed, except during the winter season when seepage fluxes tended toward downwelling conditions. Seepage fluxes near the edges of the channel were typically greater than fluxes near the center of the channel, and demonstrated greater seasonal variability. Results show that the interaction between streamflow and streambed topography has a primary control near the center of the channel, whereas the interaction between stream water and groundwater table has a primary control on seepage fluxes near the banks of the stream.

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“…Sensors could be positioned to sample temperature gradients representatively across an entire site to estimate total groundwater influx. Furthermore, their deployment could also be based upon an understanding of the flow field, which is important to avoid misinterpretation of temperature time series (Cuthbert and Mackay, 2013). For example, at this site there is evidence for non-vertical flows which have been considered the greatest source for error when implementing 1D solutions (Lautz, 2010).…”

Section: Value and Limitations Of A High Resolution 3d Temperature Modelmentioning

confidence: 99%

Discrete wetland groundwater discharges revealed with a three-dimensional temperature model and botanical indicators (Boxford, UK)

et al. 2015

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Wetlands provide unique goods and services, as habitats of high biodiversity. Hydrology is the principal control on wetland functioning, hence understanding the water source is fundamental. However, groundwater inflows may be discrete and easily missed. Research techniques are required with low cost and minimal impact in sensitive settings. In this study, the effectiveness of using a three-dimensional (3D) temperature model and botanical indicators to characterise groundwater discharge is explored at the CEH (Centre for Ecology and Hydrology) River Lambourn Observatory, Boxford, UK. This comprises a 10-ha lowland riparian wetland, designated for its scientific interest and conservation value. Temperature data were collected in winter at multiple depths down to 0.9 m over approximately 3.6 ha and transformed into a 3D model via ordinary kriging. Anomalous warm zones indicated distinct areas of groundwater upwelling which were concurrent with relic channel structures. Lateral heat propagation from the channels was minimal and restricted to within 5-10 m. Vertical temperature sections within the channels suggest varying degrees of groundwater discharge 2 along their length. Hydrochemical analysis showed warmer peat waters were akin to deeper aquifer waters, confirming the temperature anomalies as areas of groundwater discharge.Subsequently, a targeted vegetation survey identified Carex paniculata as an indicator of groundwater discharge. The upwelling groundwater contains high concentrations of nitrate which is considered to support the spatially restricted growth of Carex paniculata against a background of poor fen communities located in reducing higher-phosphate waters.

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Impacts of nonuniform flow on estimates of vertical streambed flux

Cited by 52 publications

References 27 publications

Determining groundwater‐surface water exchange from temperature‐time series: Combining a local polynomial method with a maximum likelihood estimator

Determining groundwater‐surface water exchange from temperature‐time series: Combining a local polynomial method with a maximum likelihood estimator

Spatiotemporal variability of hyporheic exchange through a pool‐riffle‐pool sequence

Discrete wetland groundwater discharges revealed with a three-dimensional temperature model and botanical indicators (Boxford, UK)

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