Digital recharge rate for the alluvial and terrace deposits along the Beaver-North Canadian River from the panhandle to Canton Lake in northwestern Oklahoma

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 polygons of a constant recharge value 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.

A recharge rate of 1 inch per year was estimated in the ground-water modeling
report for the alluvial and terrace deposits and used in this data set. The
recharge rate was estimated using a base-flow method and a
monthly-water-balance method.

The features in the data set representing 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 were digitized from a
folded paper map, at a scale of 1:250,000 in the 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
recharge 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.