Abstract: The uranium (U) concentrations and isotopic composition of waters and sediment cores were used to investigate the transport and accumulation of U in a water system (tailings pond, two lakes, and the Kalix River) receiving mine waters from the Kiruna mine. Concentrations of dissolved U decrease two orders of magnitude between the inflow of mine waters and in the Kalix River, while the concentration of the element bound to particulate matter increases, most likely due to sorption on iron‑manganese hydroxides and organic matter. The vertical distribution of U in the water column differs between two polluted lakes with a potential indication of dissolved U supply from sediment’s pore waters at anoxic conditions. Since the beginning of exposure in the 1950s, U concentrations in lake sediments have increased \textgreater20-fold, reaching concentrations above 50 μg g-1. The distribution of anthropogenic U between the lakes does not follow the distribution of other mine water contaminants, with a higher relative proportion of U accumulating in the sediments of the second lake. Concentrations of redox-sensitive elements in the sediment core as well as Fe isotopic composition were used to re-construct past redox-conditions potentially controlling early diagenesis of U in surface sediments. Two analytical techniques (ICP-SFMS and MC-ICP-MS) were used for the determination of U isotopic composition, providing an extra dimension in the understanding of processes in the system. The (234 U)/(238 U) activity ratio (AR) is rather uniform in the tailings pond but varies considerably in water and lake sediments providing a potential tracer for U transport from the Kiruna mine through the water system, and U immobilization in sediments. The U mass balance in the Rakkurijoki system as well as the amount of anthropogenic U accumulated in lake sediments were evaluated, indicating the immobilization in the two lakes of 170 kg and 285 kg U, respectively.

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.

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.