Using sequential conditioning to explore uncertainties in geostatistical characterization and in groundwater transport predictions

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