The focus of this study was estimation of hydraulic conductivity in a watershed using Multi-source Data via Co-Kriging and Bayesian Experimental Design. We presented a quantitative Multi-fidelity framework and for the first time combined information from Electrical Earth Resistivity (EER) tests and pumping tests (as two tests with different accuracies) to enhance the understanding about the geological and hydrological characteristics of a watershed. Also, for the first time, we investigated how future tests with different fidelities should be conducted to optimally enhance our understanding about the hydraulic properties of a region. Specifically, 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. We also investigated how a more accurate model can “learn” from new sensors using probablistic statistical tools, and how the best locations for future data collection can be selected.
- PI: Maryam Ghadiri
- PI Institution: University of Illinois
- March 1, 2020 – December 31, 2021