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 (

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For many environmental applications, the interpretation of fiber-optic Raman distributed temperature sensing (FO-DTS) measurements is strongly dependent on the spatial resolution of measurements, especially when the objective is to detect temperature variations over small scales. Here, we propose to compare three different and complementary methods to estimate, in practice, the “effective” spatial resolution of DTS measurements: The classical “90% step change” method, the correlation length estimated from experimental semivariograms, and the derivative method. The three methods were applied using FO-DTS measurements achieved during sandbox experiments using two DTS units having different spatial resolutions. Results show that the value of the spatial resolution estimated using a step change depends on both the effective spatial resolution of the DTS unit and on heat conduction induced by the high thermal conductivity of the cable. The correlation length method provides an estimate much closer to the value provided by the manufacturers, representative of the effective spatial resolutions along cable sections where temperature gradients are small or negligible. Thirdly, the application of the derivative method allows for verifying the representativeness of DTS measurements all along the cable, by localizing sections where measurements are representative of the effective temperature. We finally show that DTS measurements could be validated in sandbox experiments, when using devices with finer spatial resolution.

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Abstract. Essentially all hydrogeological processes are strongly influenced by the subsurface spatial heterogeneity and the temporal variation of environmental conditions, hydraulic properties, and solute concentrations. This spatial and temporal variability needs to be considered when studying hydrogeological processes in order to employ adequate mechanistic models or perform upscaling. The scale at which a hydrogeological system should be characterized in terms of its spatial heterogeneity and temporal dynamics depends on the studied process and it is not always necessary to consider the full complexity. In this paper, we identify a series of hydrogeological processes for which an approach coupling the monitoring of spatial and temporal variability, including 4D imaging, is often necessary: (1) groundwater fluxes that control (2) solute transport, mixing and reaction processes, (3) vadose zone dynamics, and (4) surface-subsurface water interaction occurring at the interface between different subsurface compartments. We first identify the main challenges related to the coupling of spatial and temporal fluctuations for these processes. Then, we highlight some recent innovations that have led to significant breakthroughs in this domain. We finally discuss how spatial and temporal fluctuations affect our ability to accurately model them and predict their behavior. We thus advocate a more systematic characterization of the dynamic nature of subsurface processes, and the harmonization of open databases to store hydrogeological data sets in their four-dimensional components, for answering emerging scientific question and addressing key societal issues.