Adding geological knowledge in transport models to improve their predictive capacity for risk mitigation in heterogeneous aquifers

Connected, highly permeable subsurface features act as preferential flow paths and strongly influence solute transport. Transport models are frequently used for risk assessment and mitigation and should properly account for these connected structures to provide unbiased and informative predictions.The parameterization of hydraulic properties typically relies on multi-Gaussian distributions and poorly integrate geological knowledge in the prior. They allow for a flexible and efficient data assimilation of process variables (heads, concentrations), but may lead to unrealistic geology and insufficient description of connectivity. Alternatively, detailed descriptions of heterogeneities can be obtained with advanced geostatistical methods, such as multiple-point statistics. They account for geological knowledge and can be conditioned to geological or geophysical data. Unfortunately, they are hardly compatible with a data assimilation process and therefore usually fail to match observed data.To address this issue, we compared several approaches capable of integrating both geological knowledge and observation data. We employed different parameterization strategies by using pilot points (de Marsily, 1978), adopting a facies-like representation of the subsurface with truncated pluri-Gaussian simulations (Matheron et al., 1987), or inserting structures whose positions can be adjusted during parameter estimation (Khambhammettu et al., 2020). We also implemented data space inversion (Delottier et al, 2023), which bypasses parameter estimation and focuses on the link between observations and forecasts.The approaches are tested with a transport model considering a contaminant migration scenario in a synthetic alluvial aquifer with permeable channels. The predictions of interest are the mass flow, peak time, and total mass of contaminant reaching the river. Results show that there is a compromise to find between a simple but effective parameterization, which can struggle to represent connectivity, and a more detailed one, which is more difficult to make consistent with observations.