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Estimation of Hydraulic Conductivity in a Watershed Using Multi-source Data via Co-Kriging and Bayesian Experimental Design
  • Chien-Yung Tseng,
  • Maryam Ghadiri,
  • Hadi Meidani
Chien-Yung Tseng
University of Illinois at Urbana-Champaign,University of Illinois at Urbana-Champaign

Corresponding Author:cytseng2@illinois.edu

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Maryam Ghadiri
University of Illinois at Urbana Champaign
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Hadi Meidani
University of Illinois at Urbana Champaign
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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.