GROUNDWATER MODELING & VISUALIZATION
Figure 2. Models used to create time series groundwater contamination migration models (A-G) and sample output (H).
7. Creating a Hydraulic Conductivity (K) Model: A ta- ble of hydraulic conductivities for the various lithotypes was used to convert the lithology model into a hydrau- lic conductivity (K) model (Figure 2E). This model was vertically truncated based on a maximum groundwater elevation model (Figure 2F) to limit subsequent model- ing. Finally, the truncated hydraulic conductivity model (BPI) model (Figure 2G) in which lithologies with perme- abilities less than 2x10-5 feet per second were converted to zero while voxels (volumetric elements within a block model) above 2x10-5 ft./sec were set to 1.0. At the same unconnected permeable zones.
8. Creating the Annual 1,4-Dioxane Models: Geochemi- cal data from the borehole database was extracted for each year, from 1986 to 2020 and modeled. This modeling (Figure 2H) used an anisotropic inverse-distance weight-
8 TPG •
Oct.Nov.Dec 2022
ing algorithm that implicitly biased the interpolations horizontally as constrained by the BPI, MHWL, and bed- rock models. A visual interpretation of these results can be very misleading because additional data was added over the years thereby extending the lateral extents of the plume. For example, the plume extents in 1986 may have been much larger, however the diagrams are limited the modeling. This can produce a misleading interpreta- tion of the expansion of the plume over time. As a conse- quence, the exact historical geometry of the plume may never be known. On the other hand, a visual examination of the historical and spatial changes in the 1,4-dioxane concentration levels can be used to accurately determine where the contaminant was increasing or decreasing.
9. Creating Depth-to-Dioxane Models: The 2020 mod- els were used to create grid models representing the minimum depths to 1,4-dioxane contamination greater
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