Enhanced water management systems depend on accurate estimation of
hydraulic properties of subsurface formations. This is while hydraulic
conductivity of geologic formations could vary significantly. Herein, we
studied an intensively managed area located in the Upper Sangamon
Watershed in Central Illinois, U.S.A., and generated 2D maps of
hydraulic conductivity over a large-scale region with quantified
uncertainties in different depth layers. In doing so, we made use of low
cost, small-scale measurements obtained from the Electrical Earth
Resistivity together with more accurate, more expensive pumping tests in
a calibration framework based on Kriging. We offered a cost-effective
approach to reliably characterize the hydraulic conductivity properties
in under-sampled sites and can be particularly used in obtaining
large-scale parameter maps for a region using small-scale measurements
in an efficient way. This work also includes optimal sensor placement,
where the best locations for future data collection are selected by
considering the current confidence levels estimated by the Kriging
model, which is related to the expected value of information from future
sensor data. Our approach is based on the Bayesian experimental design,
which selects the best locations, out of a set of candidate locations,
based on the value of information that each location is expected to
offer.