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Rapid transmission of contaminants in groundwater can occur in alluvial gravel aquifers that are permeated by highly conductive small-scale open framework gravels (OFGs). This open framework gravel structure and the associated distribution of hydraulic properties is complex, and so assessments of contamination risks in these aquifers are highly uncertain. Geostatistical models, based on lithological data, can be used to quantitatively characterize this structure. These models can then be used to support analyses of the risks of contamination in groundwater systems. However, these geostatistical models are themselves accompanied by significant uncertainty. This is seldom considered when assessing risks to groundwater systems. Geostatistical model uncertainty can be reduced by assimilating information from hydraulic system response data, but this process can be computationally challenging. We developed a sequential conditioning method designed to address these challenges. This method is demonstrated on a transition probability based geostatistical simulation model (TP), which has been shown to be superior for representing the connectivity of high permeability pathways, such as OFGs. The results demonstrate that the common modelling practice of adopting a single geostatistical model may result in realistic predictions being overlooked, and significantly underestimate the uncertainties of groundwater transport predictions. This has important repercussions for uncertainty quantification in general. It also has repercussions if using ensemble?based methods for history matching, since it also relies on geostatistical models to generate prior parameter distributions. This work highlights the need to explore the uncertainty of geostatistical models in the context of the predictions being made.

The nitrogen cycle plays a major role in aquatic nitrogen transformations, including in the terrestrial subsurface. However, the variety of transformations remains understudied. To determine how nitrogen cycling microorganisms respond to different aquifer chemistries, we sampled groundwater with varying nutrient and oxygen contents. Genes and transcripts involved in major nitrogen-cycling pathways were quantified from 55 and 26 sites, respectively, and metagenomes and metatranscriptomes were analyzed from a subset of oxic and dysoxic sites (0.3-1.1 mg/L bulk dissolved oxygen). Nitrogen-cycling mechanisms (e.g. ammonia oxidation, denitrification, dissimilatory nitrate reduction to ammonium) were prevalent and highly redundant, regardless of site?specific physicochemistry or nitrate availability, and present in 40% of reconstructed genomes, suggesting that nitrogen cycling is a core function of aquifer communities. Transcriptional activity for nitrification, denitrification, nitrite-dependent anaerobic methane oxidation and anaerobic ammonia oxidation (anammox) occurred simultaneously in oxic and dysoxic groundwater, indicating the availability of oxic-anoxic interfaces. Concurrent activity by these microorganisms indicates potential synergisms through metabolite exchange across these interfaces (e.g. nitrite and oxygen). Fragmented denitrification pathway encoding and transcription was widespread among groundwater bacteria, although a considerable proportion of associated transcriptional activity was driven by complete denitrifiers, especially under dysoxic conditions. Despite large differences in transcription, the capacity for the final steps of denitrification was largely invariant to aquifer conditions, and most genes and transcripts encoding N2O reductases were the atypical Sec-dependant type, suggesting energy-efficiency prioritization. Results provide insights into the capacity for cooperative relationships in groundwater communities, and the richness and complexity of metabolic mechanisms leading to the loss of fixed nitrogen.