Uncertainty with Varying Subsurface Permeabilities Reduced Using Coupled Random Field and Extended Theory of Porous Media Contaminant Transport Models

To maximize the usefulness of groundwater flow models for the protection of aquifers and abstraction wells, it is necessary to identify and decrease the uncertainty associated with the major parameters such as permeability. To do this, there is a need to develop set of estimates representing subsurface heterogeneity or representative soil permeability estimates. Here, we use a coupled Random Field and extended Theory of Porous Media (eTPM) simulation to develop a robust model with a good predictive ability that reduces uncertainty. The coupled model is then validated with a physical sandbox experiment. Uncertainty is reduced by using 500 realisations of the permeability parameter using the eTPM approach. A multi-layer contaminant transport scenario with varying permeabilities, similar to what could be expected with shallow alluvial sediments, is simulated. The results show that the contaminant arrival time could be strongly affected by random field realizations of permeability compared with a modelled homogenous permeability parameter. The breakthrough time for heterogeneous permeabilities is shorter than the homogeneous condition. Using the 75% confidence interval (CI), the average contaminant concentration shows 4.4% variation from the average values of the considered area and 8.9% variation in the case of a 95% confidence interval.

Automated summary

In order to improve the usefulness of groundwater flow models, it is necessary to reduce uncertainty associated with major parameters like permeability. A coupled Random Field and extended Theory of Porous Media (eTPM) simulation is used to develop a robust model that reduces uncertainty and is validated with a physical sandbox experiment. The results show that random field realizations of permeability can strongly affect contaminant arrival time compared to a homogenous parameter model.