2016
DOI: 10.1002/2016wr018841
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Impacts of three‐dimensional nonuniform flow on quantification of groundwater‐surface water interactions using heat as a tracer

Abstract: Use of heat‐as‐a‐tracer is a common method to quantify surface water‐groundwater interactions (SW‐GW). However, the method relies on assumptions likely violated in natural systems. Numerical studies have explored violation of fundamental assumptions such as heterogeneous streambed properties, two‐dimensional groundwater flow fields and uncertainty in thermal parameters for the 1‐D heat‐as‐a‐tracer method. Few studies to date have modeled complex, fully three‐dimensional groundwater flows to address the impacts… Show more

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Cited by 23 publications
(24 citation statements)
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“…Using heat as a natural tracer offers quantitative methods to estimate one‐dimensional vertical water fluxes in near surface sediments (e.g., Hatch et al, ; Munz & Schmidt, ). The presence of horizontal flows, including hyporheic exchange, will cause uncertainty in these analytical solution results (Brookfield & Sudicky, ; Cuthbert & Mackay, ; Reeves & Hatch, ). In contrast, three‐dimensional flow and heat transport models can be used to simulate the complex river‐groundwater exchange and thermal transport process in river‐riparian systems, spatially distributed and at a high temporal resolution (Brookfield et al, ; Karan et al, ; Nützmann et al, ).…”
Section: Introductionmentioning
confidence: 99%
“…Using heat as a natural tracer offers quantitative methods to estimate one‐dimensional vertical water fluxes in near surface sediments (e.g., Hatch et al, ; Munz & Schmidt, ). The presence of horizontal flows, including hyporheic exchange, will cause uncertainty in these analytical solution results (Brookfield & Sudicky, ; Cuthbert & Mackay, ; Reeves & Hatch, ). In contrast, three‐dimensional flow and heat transport models can be used to simulate the complex river‐groundwater exchange and thermal transport process in river‐riparian systems, spatially distributed and at a high temporal resolution (Brookfield et al, ; Karan et al, ; Nützmann et al, ).…”
Section: Introductionmentioning
confidence: 99%
“…The implication of this finding is that the inverse solution method can be applied with an arbitrary temperature forcing, including strong low‐frequency components that would produce conditions looking like steady‐state temperature gradients (e.g., Caissie & Luce, ). Errors previously attributed to these problems more likely result from any number of other issues in estimating fluxes and parameters, including divergence of fluxes from curving flow paths (Cardenas et al, ; Cuthbert & Mackay, ; Reeves & Hatch, ), heterogeneity in bed sediments (Cardenas et al, ; Hester et al, ; Irvine et al, ), temporal variability in head and water flux rates (McCallum et al, ; Rau et al, ), signal processing methods (Lautz, ; Rau et al, ), or simply from errors propagating from measurement uncertainty (Luce et al, ; Shanafield et al, ). Accurate model evaluation requires a firm theoretical foundation (Clark et al, ), and here we more thoroughly develop the theory for the analytical inverse solution of the 1‐D advection‐diffusion equation.…”
Section: Introductionmentioning
confidence: 99%
“…The attributes of the diurnal temperature signal-based analytical solutions have been investigated broadly, including the influence of heterogeneity (Birkel et al, 2016;Irvine, Cranswick, Simmons, Shanafield, & Lautz, 2015), nonsinusoidal temperature signals (Luce, Tonina, Applebee, & DeWeese, 2017), nonconstant fluid fluxes Rau, Cuthbert, McCallum, Halloran, & Andersen, 2015), multidimensional flow (Cuthbert & Mackay, 2013;Lautz, 2010;Reeves & Hatch, 2016), and the uncertainty in flux estimates that results from uncertainties in thermal properties Shanafield, Hatch, & Pohll, 2011).…”
Section: Introductionmentioning
confidence: 99%