Digital hydraulic conductivity values

This data set was created for a project to develop data sets to support
ground-water vulnerability analysis. The objective was to create and document a
digital geospatial data set from a published report or map, or existing digital
geospatial data sets that could be used in ground-water vulnerability analysis.

This data set consists of digital hydraulic conductivity values for the
alluvial and terrace deposits along the Beaver-North Canadian River from the
panhandle to Canton Lake in northwestern Oklahoma. Ground water in 830 square
miles of the Quaternary-age alluvial and terrace aquifer is an important source
of water for irrigation, industrial, municipal, stock, and domestic supplies.
The aquifer consists of poorly sorted, fine to coarse, unconsolidated quartz
sand with minor amounts of clay, silt, and basal gravel. The hydraulically
connected alluvial and terrace deposits unconformably overlie the Tertiary-age
Ogallala Formation and Permian-age formations.

Six zones of ranges of hydraulic conductivity values for the alluvial and
terrace deposits reported in a ground-water modeling report are used in this
data set. The hydraulic conductivity values range from 0 to 160 feet per day,
and average 59 feet per day.

The features in the data set representing aquifer boundaries along geological
contacts were extracted from a published digital surficial geology data set
based on a scale of 1:250,000. The geographic limits of the aquifer and zones
representing ranges of hydraulic conductivity values were digitized from folded
paper maps, at a scale of 1:250,000 from a ground-water modeling report.

Ground-water flow models are numerical representations that simplify and
aggregate natural systems. Models are not unique; different combinations of
aquifer characteristics may produce similar results. Therefore, values of
hydraulic conductivity used in the model and presented in this data set are not
precise, but are within a reasonable range when compared to independently
collected data.