2011
DOI: 10.1029/2010wr009574
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Uncertainty in thermal time series analysis estimates of streambed water flux

Abstract: [1] Streambed seepage can be predicted using an analytical solution to the one-dimensional heat transport equation to take advantage of the relationship between streambed thermal properties, seepage flux, and the amplitude ratio and phase shift associated with streambed temperature signals. This paper explores the accuracy of streambed-seepage velocity estimates from this method when uncertainty in input parameters exists. Uncertainty in sensor spacing, thermal diffusivity, and the accuracy of temperature sens… Show more

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Cited by 85 publications
(106 citation statements)
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“…lagged thermal response within a well pipe due to thermal buffering), temperature sensor accuracy, and discretization of the temperature time series (i.e. temporal resolution) (Cardenas, 2010;Shanafield et al, 2011;Soto-Lopez et al, 2011). Such comparisons have also quantified errors associated with thermal parameter uncertainty, such as assumed values of thermal diffusivity (Shanafield et al, 2011), and errors associated with violation of the model assumptions, such as non-vertical flow and non-sinusoidal temperature oscillations at the upstream boundary (Lautz, 2010).…”
Section: Introductionmentioning
confidence: 98%
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“…lagged thermal response within a well pipe due to thermal buffering), temperature sensor accuracy, and discretization of the temperature time series (i.e. temporal resolution) (Cardenas, 2010;Shanafield et al, 2011;Soto-Lopez et al, 2011). Such comparisons have also quantified errors associated with thermal parameter uncertainty, such as assumed values of thermal diffusivity (Shanafield et al, 2011), and errors associated with violation of the model assumptions, such as non-vertical flow and non-sinusoidal temperature oscillations at the upstream boundary (Lautz, 2010).…”
Section: Introductionmentioning
confidence: 98%
“…Cardenas, 2010;Lautz, 2010;Shanafield et al, 2011;Ferguson and Bense, 2011;Schornberg et al, 2010;Soto-Lopez et al, 2011). Differences between known flux rates through numerical water and heat transport models and those quantified by analysis of the model-generated temperature time series have revealed errors caused by deployment of temperature sensors, including sensor spacing uncertainty, thermal skin effects (i.e.…”
Section: Introductionmentioning
confidence: 99%
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“…Measurement of such variability is limited due to difficulties in applying conventional Darcian flux based methods. On the other hand, the measurement of diurnal fluctuations of water temperature at different depths in the streambed has substantial benefits for monitoring streambed water fluxes over time compared with other methods because temperature is a robust and relatively inexpensive parameter to measure, and is naturally occurring (Stallman 1965;Constantz and Stonestrom 2003;Anderson 2005;Bhaskar et al 2012;Constantz 2008;Hatch et al 2006;Lautz 2012;Rau et al 2010;Shanafield et al 2011). Using heat as a tracer is a particularly favorable method of assessing the spatial variability of HEF.…”
Section: Introductionmentioning
confidence: 98%
“…Lautz (2010) showed that non-uniform flow causes the biggest error in 1-D velocity estimates compared to other non-ideal field conditions, such as a non-sinusoidal signal and a thermal gradient. In addition, uncertainty in thermal diffusivity, sensor spacing, and the accuracy of temperature sensors can cause erroneous predictions of seepage velocities, especially for gaining conditions and low flow velocities (Shanafield et al, 2011). Rau et al (2010) tested the advantages, limitations and applicability of the methods of Hatch et al (2006) and Stallman (1965).…”
Section: T Vogt Et Al: Investigating Riparian Groundwater Flow Closmentioning
confidence: 99%