Access Type

Open Access Thesis

Date of Award

January 2016

Degree Type


Degree Name




First Advisor

Lawrence D. Lemke


Hybrid models incorporating stochastic variability within a deterministic hydrostratigraphic framework provide an effective way to assess uncertainty in flow and transport model predictions. This study evaluated the distribution of groundwater flow and contaminant transport pathways in two ensembles of spatially variable hydraulic conductivity (K) distributions. The models comprised a 360 ft-thick sequence of Pleistocene glacial sediments in in an approximately 8 mi2 area across Washtenaw County, Michigan. Conditioned K fields were generated using Sequential Gaussian Simulation (SGS) and Sequential Indicator Simulation (SIS) constructed using indicator classes based on natural gamma ray logs from 77 monitoring wells. K fields were modeled independently for aquifer and aquitard materials and subsequently embedded within a 3D MODFLOW model constructed using a deterministic framework of eight aquifer and aquitard layers.

MODPATH was used to track the pathways of 100 particles released as line sources at five depth intervals with documented 1,4-dioxane concentrations along the boundary of the contaminant source area. Pathways for 100 realizations of each ensemble were combined to produce maps and cross sections showing the frequency of particles passing through model cells downgradient of the line source and upgradient of the hypothesized groundwater discharge location along the Huron River approximately 8 km from the line source. Differences in the spatial distribution of particle pathways observed between the SIS and SGS ensembles were observed, and compared to distribution resulting from deterministic modeling. Results revealed channelization and dispersion patterns downgradient are influenced by several factors, including model type, distance from source, dispersion orientation, head differentials, and percentage of aquifer and aquitard material.