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 generally leads to effective behaviors and emerging phenomena that cannot be predicted from conventional approaches based on homogeneous assumptions and models. However, it is not always clear when, why, how, and at what scale the 4D (3D + time) nature of the subsurface needs to be considered in hydrogeological monitoring, modeling, and applications. In this paper, we discuss the interest and potential for the monitoring and characterization of spatial and temporal variability, including 4D imaging, in a series of hydrogeological processes: (1) groundwater fluxes, (2) solute transport and reaction, (3) vadose zone dynamics, and (4) surface–subsurface water interactions. We first identify the main challenges related to the coupling of spatial and temporal fluctuations for these processes. We then highlight recent innovations that have led to significant breakthroughs in high-resolution space–time imaging and modeling the characterization, monitoring, and modeling of these spatial and temporal fluctuations. We finally propose a classification of processes and applications at different scales according to their need and potential for high-resolution space–time imaging. We thus advocate a more systematic characterization of the dynamic and 3D nature of the subsurface for a series of critical processes and emerging applications. This calls for the validation of 4D imaging techniques at highly instrumented observatories and the harmonization of open databases to share hydrogeological data sets in their 4D components.

Heat as a tracer in fractured porous aquifers is more sensitive to fracture-matrix processes than a solute tracer. Temperature evolution as a function of time can be used to differentiate fracture and matrix characteristics. Experimental hot (50 degrees C) and cold (10 degrees C) water injections were performed in a weathered and fractured granite aquifer where the natural background temperature is 30 degrees C. The tailing of the hot and cold breakthrough curves, observed under different hydraulic conditions, was characterized in a log-log plot of time vs. normalized temperature difference, also converted to a residence time distribution (normalized). Dimensionless tail slopes close to 1.5 were observed for hot and cold breakthrough curves, compared to solute tracer tests showing slopes between 2 and 3. This stronger thermal diffusive behavior is explained by heat conduction. Using a process-based numerical model, the impact of heat conduction toward and from the porous rock matrix on groundwater heat transport was explored. Fracture aperture was adjusted depending on the actual hydraulic conditions. Water density and viscosity were considered temperature dependent. The model simulated the increase or reduction of the energy level in the fracture-matrix system and satisfactorily reproduced breakthrough curves tail slopes. This study shows the feasibility and utility of cold water tracer tests in hot fractured aquifers to boost and characterize the thermal matrix diffusion from the matrix toward the flowing groundwater in the fractures. This can be used as complementary information to solute tracer tests that are largely influenced by strong advection in the fractures.

Abstract Chalk porosity plays a decisive role in the transport of solutes and heat in saturated chalk. From a geological point of view, there are at least two types of porosity: the porosity of pores corresponding to the micro-spaces between the fossil coccoliths that form the chalk matrix and the porosity owing to the micro- and macro-fractures (i.e. secondary porosity). For groundwater flow, the fracture porosity is a determining factor at the macroscopic scale. The multiscale heterogeneity of the porous/fractured chalk induces different effects on solute and heat transport. For solute transport considered at the macroscopic scale, tracer tests have shown that the ‘effective transport porosity’ is substantially lower than the ‘effective drainable porosity’. Moreover, breakthrough curves of tracer tests show an important influence of diffusion in a large portion of the ‘immobile water’ (‘matrix diffusion’) together with rapid preferential advection through the fractures. For heat transport, the matrix diffusion in the ‘immobile water’ of the chalk is hard to distinguish from conduction within the saturated chalk.

ID 53 FOR SESSION 14: Advances in forward and inverse groundwater modeling and open source tools for computational subsurface hydrology (uncertainty) THE POTENTIAL OF A MONTE CARLO BASED SENSITIVITY ANALYSIS FOR TRANSPORT USING A HEAT-SOLUTE TRACER TEST IN ALLUVIAL SEDIMENTS Richard Hoffmann, Pascal Goderniaux, Alain Dassargues and Thomas Hermans 1Hydrogeology and Environmental Geology, Urban and Environmental Engineering, Liège University, Belgium 2Geology and Applied Geology, Polytech Mons, University of Mons, Belgium 3Department of Geology, Ghent University, Belgium E-mail address of corresponding author: Richard.Hoffmann@uliege.be For numerical aquifer modeling with uncertainty quantification, a sensitivity analysis is a mandatory process. One-factor-at-a-time procedures, i.e., changing one, calibrated input parameter and keeping the other fixed, are still very popular for hydrogeologists. This show immediately which input parameters have the most influence on the results, but the simultaneous variation of multiple input parameters cannot be taken into consideration. This avoids the detection of interactions between input parameters and makes this procedure uncertain. A sensitivity analysis must quantify the relationship between input and model response uncertainty. Thus, a Monte Carlo based sensitivity analysis as a distance-based global sensitivity analysis (DGSA), is here performed. This new kind of sensitivity analysis can reveal key information about parameters most influencing the model outcomes. In this study, the basis for DGSA are 250 Monte Carlo realizations, sampled from a prior distribution that was not previously rejected (i.e., falsified) considering a joint heat-solute tracer experiment in alluvial sediments. In other words, several sets of randomly chosen model parameters were tested for their consistency with the observed data (i.e., prior falsification). In DGSA, the distance between model outcomes is calculated and projected in a low dimensional space. Simulations with a comparable distance to the reference data are a cluster. The parameter cumulative distribution function within k clusters is compared to the reference distribution to deduce the sensitivity. DGSA analyzes both, global parameters (Mean hydraulic conductivity, porosity, etc.) and local high dimensional parameters characterizing the spatial heterogeneity like the complex hydraulic conductivity field generated with sequential Gaussian simulation in the prior. The latter are considered through their principal components replacing multiple statistical parameters with a limited, smaller, and approximated amount of linear combinations. The results show that the heat tracer seems to be less sensitive to global advective parameters like porosity, indicating the complementary tracer behavior. The principal components describing local spatial heterogeneity are sensitive for the heat and the solute tracer, but heat tends to remain more dominated by conduction. Thus, for robust transport decisions using any stochastic Bayesian inversion, an adequate prior description in conjunction with a global sensitivity analysis considering uncertainty is a prerequisite. References Dassargues A., 2018. Hydrogeology: groundwater science and engineering. Taylor & Francis CRC press, Boca Raton. Hermans T., Wildemeersch S., Jamin P., Orban P., Brouyère S., Dassargues A. and Nguyen F., 2015, Quantitative temperature monitoring of a heat tracing experiment using cross-borehole ERT, Geothermics 53: 14-26 Hermans Th., Nguyen F., Klepikova M., Dassargues A. and J. Caers, 2018. Uncertainty quantification of medium-term heat storage from short-term geophysical experiments, Water Resources Research, 54: 2931-2948. Hoffmann R., Dassargues A., Goderniaux P. and Th. Hermans, 2019. Heterogeneity and prior uncertainty investigation using a joint heat and solute tracer experiment in alluvial sediments. Frontiers in Earth Science, 7: 10.3389/feart.2019.00108 Klepikova M., Wildemeersch S., Jamin P., Orban Ph., Hermans T., Nguyen F., Brouyere S. and Dassargues A., 2016, Heat tracer test in an alluvial aquifer: field experiment and inverse modelling, Journal of Hydrology 540: 812-823. Wildemeersch S., Jamin P., Orban Ph., Hermans T., Klepikova M., Nguyen F., Brouyère S. & Dassargues A., 2014, Coupling heat and chemical tracer experiments for estimating heat transfer parameters in shallow alluvial aquifers, Journal of Contaminant Hydrology 169: 90-99.

Transport in fractured media plays an important role in a range of processes, from rock weathering and microbial processes to contaminant transport, and energy extraction and storage. Diffusive transfer between the fracture fluid and the rock matrix is often a key element in these applications. But the multiscale heterogeneity of fractures renders the field assessment of these processes extremely challenging. This study explores the use of dissolved gases as tracers of fracture-matrix interactions, which can be measured continuously and highly accurately using mobile mass spectrometers. Since their diffusion coefficients vary significantly, multiple gases are used to probe different scales of fracture-matrix exchanges. Tracer tests with helium, xenon, and argon were performed in a fractured chalk aquifer, and resulting tracer breakthrough curves are modeled. Results show that continuous dissolved gas tracing with multiple tracers provides key constrains on fracture-matrix interactions and reveal unexpected scale effects in fracture-matrix exchange rates.

Time-lapse geophysical imaging of tracer tests is essential to infer preferential pathway characterization. Thereby, the mapping of small-scale heterogeneities within aquifers is crucial for characterizing flow and transport in the critical zone as realistic as possible. Compared to ray-based inversion methods for ground penetrating radar (GPR) data [1], the full-waveform inversion (FWI) of crosshole GPR data has shown a high potential to characterize soil properties of the near surface with a decimeterscale resolution [2]. GPR FWI can provide high-resolution images of the geophysical properties relative dielectric permittivity εr and electrical conductivity σ. The εr can be linked to the aquifer porosity, and σ can the related to the pore fluid and clay content. Thus, tracers of different geophysical properties, which change (a) only electrical conductivity (e.g., salt [3]), and, (b) both electrical conductivity and permittivity (e.g., heat [4], ethanol [5]) are promising for GPR techniques.

In heterogeneous aquifers, imaging preferential flow paths, and non-Gaussian effects is critical to reduce uncertainties in transport predictions. Common deterministic approaches relying on a single model for transport prediction show limitations in capturing these processes and tend to smooth parameter distributions. Monte-Carlo simulations give one possible way to explore the uncertainty range of parameter value distributions needed for realistic predictions. Joint heat and solute tracer tests provide an innovative option for transport characterization using complementary tracer behaviors. Heat tracing adds the effect of heat advection-conduction to solute advection-dispersion. In this contribution, a joint interpretation of heat and solute tracer data sets is proposed for the alluvial aquifer of the Meuse River at the Hermalle-sous-Argenteau test site (Belgium). First, a density-viscosity dependent flow-transport model is developed and induce, due to the water viscosity changes, up to 25 % change in simulated heat tracer peak times. Second, stochastic simulations with hydraulic conductivity (K) random fields are used for a global sensitivity analysis. The latter highlights the influence of spatial parameter uncertainty on the resulting breakthrough curves, stressing the need for a more realistic uncertainty quantification. This global sensitivity analysis in conjunction with principal component analysis assists to investigate the link between the prior distribution of parameters and the complexity of the measured data set. It allows to detect approximations done by using classical inversion approaches and the need to consider realistic K-distributions. Furthermore, heat tracer transport is shown as significantly less sensitive to porosity compared to solute transport. Most proposed models are, nevertheless, not able to simultaneously simulate the complementary heat-solute tracers. Therefore, constraining the model using different observed tracer behaviors necessarily comes with the requirement to use more-advanced parameterization and more realistic spatial distribution of hydrogeological parameters. The added value of data from both tracer signals is highlighted, and their complementary behavior in conjunction with advanced model/prediction approaches shows a strong uncertainty reduction potential.

Introduction and Motivation Imaging preferential pathways of transport processes in heterogeneous porous media is critical to reduce uncertainties in transport simulations and predictions. In heterogeneous aquifers such as the alluvial sediments at the Hermalle-sousArgenteau test site (Liege, Belgium), preferential flow paths and non-gaussian effects are often observed. Such phenomena are not easily captured by deterministic approaches, which tend to smooth spatial parameter distributions and therefore reduce heterogeneity. Stochastic approaches allow considering larger uncertainty and heterogeneity and do not rely on the unique prediction obtained by deterministic calibrations. However, there are often too computationally expensive to be used in practice. Bayesian Evidential Learning (BEL) relies on a limited number of Monte Carlo simulations sampling the prior distribution of model parameters to analyze the global sensitivity of parameters [1]. It is used to produce a statistical forecast based on a statistical relationship between historical and forecast variables in conjunction with the actual production data and is called direct forecasting Prediction-Focused Approach (PFA) [2].