Abstract: Uranium (U) roll-front deposits constitute a valuable source for an economical extraction by in situ recovery (ISR) mining. Such technology may induce changes in the subsurface microbiota, raising questions about the way their activities could build a functional ecosystem in such extreme environments (i.e.: oligotrophy and high SO4 concentration and salinity). Additionally, more information is needed to dissipate the doubts about the microbial role in the genesis of such U orebodies. A U roll-front deposit hosted in an aquifer driven system (in Zoovch Ovoo, Mongolia), intended for mining by acid ISR, was previously explored and showed to be governed by a complex bacterial diversity, linked to the redox zonation and the geochemical conditions. Here for the first time, transcriptional activities of microorganisms living in such U ore deposits are determined and their metabolic capabilities allocated in the three redox-inherited compartments, naturally defined by the roll-front system. Several genes encoding for crucial metabolic pathways demonstrated a strong biological role controlling the subsurface cycling of many elements including nitrate, sulfate, metals and radionuclides (e.g.: uranium), through oxidation-reduction reactions. Interestingly, the discovered transcriptional behaviour gives important insights into the good microbial adaptation to the geochemical conditions and their active contribution to the stabilization of the U ore deposits. Overall, evidences on the importance of these microbial metabolic activities in the aquifer system are discussed that may clarify the doubts on the microbial role in the genesis of low-temperature U roll-front deposits, along the Zoovch Ovoo mine.

Abstract: Travel time distributions (TTDs) provide an effective way to describe the transport and mixing processes of water parcels in a subsurface hydrological system. A major challenge in characterizing catchment TTD is quantifying the travel times in deep groundwater and its contribution to the streamflow TTD. Here, we develop and test a novel modeling framework for an integrated assessment of catchment scale TTDs through explicit representation of 3D-groundwater dynamics. The proposed framework is based on the linkage between a flux tracking scheme with the surface hydrologic model (mHM) for the soil-water compartment and a particle tracking scheme with the 3D-groundwater model OpenGeoSys (OGS) for the groundwater compartment. This linkage provides us with the ability to simulate the spatial and temporal dynamics of TTDs in these different hydrological compartments from grid scale to regional scale. We apply this framework in the Nägelstedt catchment in central Germany. Simulation results reveal that both shape and scale of grid-scale groundwater TTDs are spatially heterogeneous, which are strongly dependent on the topography and aquifer structure. The component-wise analysis of catchment TTD shows a time-dependent sensitivity of transport processes in soil zone and groundwater to driving meteorological forcing. Catchment TTD exhibits a power-law shape and fractal behavior. The predictive uncertainty in catchment mean travel time is dominated by the uncertainty in the deep groundwater rather than that in the soil zone. Catchment mean travel time is severely biased by a marginal error in groundwater characterization. Accordingly, we recommend to use multiple summary statistics to minimize the predictive uncertainty introduced by the tailing behavior of catchment TTD.

Abstract: To effectively manage sustainably groundwater bodies, it is essential to establish what the naturally occurring ranges of chemical concentrations in groundwaters are and how they change over time. We defined baseline trends for New Zealand groundwaters using: 1) pattern recognition techniques to deal with inconsistent monitoring suites between the national (110 sites) and the denser regional network (\textgreater1000 sites), and 2) multivariate statistics to identify and remove impacted sites from the enhanced dataset. Rates of changes were calculated for 13 parameters between January 2005 and December 2014 at more than 1000 groundwater quality monitoring sites. The resulting dataset included 262 complete cases (CC), which was enhanced using Machine-Learning (ML) techniques to a total of 607 sites. Hierarchical cluster analysis was used to identify trend clusters that were consistent between the CC, ML-enhanced datasets and a 2006 study based on solely on the national network. The largest cluster (WR) consisted of low magnitude changes across all parameters and was attributed to water-rock interaction processes. The second largest cluster (I) exhibited fast changes particularly for parameters linked to human-induced impact. The third largest cluster (D) comprised decreases of all parameters and was associated with dilution processes. Trend clusters were further refined using groundwater quality state information, enabling the identification of impacted sites outside of Cluster I in the ML-enhanced and CC datasets. Corresponding trend baselines were subsequently derived at unimpacted sites using univariate quantile distribution (5th and 95th percentile thresholds). Finally, we developed classifications combining baselines (state and trend) and natural variability to enhance state of the environment reporting. This allowed the new identification of deteriorating trends at sites where groundwater quality state is not yet affected in addition to trend reversals. These classifications can be adapted to incorporate new knowledge or align with surface water quality reporting.