Numerical and Experimental Validation of the Applicability of Active‐DTS Experiments to Estimate Thermal Conductivity and Groundwater Flux in Porous Media

Abstract

In groundwater hydrology, the characterization of the distribution of groundwater flow within the critical zone received considerable attention in the last decades (Freeze & Cherry, 1979). Our ability to quantify groundwater flow greatly controls our ability to characterize aquifers, predict contaminant transport, and understand biogeochemical reactions and processes occurring in the subsurface (Kalbus et al., 2009; Poeter & Gaylord, 1990). Groundwater flow at interfaces such as recharge and discharge areas also plays a key role in the preservation of groundwater-dependent ecosystems (Kalbus et al., 2006; Sophocleous, 2002). The quantification of groundwater fluxes is also particularly relevant for geothermal energy since they control heat exchange and storage capacities (Diao et al., 2004). Similarly, the characterization of seepage through dams, dikes, and reservoirs is also critical for geotechnical engineering (Foster et al., 2000). The spatial distribution of groundwater fluxes is largely driven by subsurface heterogeneities. Thus, in past decades, the characterization of the distribution of groundwater fluxes and their quantification relied on the capacity of characterizing and modeling the spatial variability of hydraulic conductivities (de Marsily, 1976). Considering the challenge in characterizing the field variability of hydraulic properties, the use of heat as a tracer has been widely developed and applied to characterize flow in aquifers or at interfaces such as the hyporheic zone (

Individual and joint inversion of head and flux data by geostatistical hydraulic tomography

“…The resulting measurement errors range from 0.05 to 0.013 (m) for the head and 0.055 to 0.01 (m/day) for the flux. The relation between values of groundwater flux and measurement uncertainty is provided by Simon et al (2021). This relation was used to define a reasonable range of the noise model.…”

“…The recent advent of Fiber Optic Distributed Temperature Sensors (FO-DTS) has led to new possibilities for measuring groundwater discharge with unprecedented spatial resolution (des Tombe et al, 2019;Simon et al, 2021, del Val et al, 2021. FO-DTS is a distributed sensor type that allows measurements of temperature all along the fiber optic cable.…”

“…In this case, the buried cable is in direct contact with the ground with minimum subsurface perturbations. Simon et al (2021) showed that active-DTS can be used for measuring both sediment thermal conductivities and groundwater fluxes on a large range of values with excellent accuracy (with errors of less than 10% for groundwater flux in the range of 10 -5 to 10 -3 m/s). The principle of such an experiment (Figure 1) is to monitor the temperature evolution with time, which depends on the surrounding area's thermal properties and also groundwater fluxes that limits the temperature rise.…”

“…Typically, the slope of the temperature rise in the conduction regime (with time in logarithmic scale) is inversely proportional to thermal conductivity while the greater the groundwater fluxes, the lower the temperature at stabilization. The temperature evolution with time may be easily interpreted to estimate groundwater fluxes through an analytical solution or by using a graphical analysis (Simon et al, 2021). The application of Active FO-DTS for flux measurements is currently limited to shallow and unconsolidated aquifers due to limitations in deployment by the direct push method.…”

“…Each curve corresponds to one-point measurement along the FO-cable. (c) In semi-logarithmic plot, the slope of the temperature profile is used to estimate the thermal conductivity while the temperature at stabilization is dependent on groundwater fluxes (Simon et al, 2021). (d) Measured temperature data along the fiber optic cable are converted to a flux profile with depth, with each "+" sign indicate one datum.…”