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This project aims to develop a new methodology to integrate geostatistical methods
with hydro-geophysical measurements in conjunction with terrain and conventional hydrologic
observations to evaluate aquifer recharge dynamics in groundwater recharge areas.
A key limitation of most hydrologic models is that they do not provide reliable quantification of the uncertainty in the model predictions associated to subsurface parameters that govern water movement. We propose a geostatistical methodology based on Bayes theory to predict the spatial distributions of the main variables that control the water supply, such as porosity and water saturation. The model variables generally vary in space and time and cannot be measured directly in the subsurface, except for at a limited number of locations. In the proposed approach, we proposed to predict these model variables from time-lapse geophysical data and integrate the predictions, and the associated uncertainty, into the aquifer potential assessment framework. This approach is then used to quantify the aquifer resilience to continued or increased extraction for human water use. The new methodology will focus on datasets acquired in three Wyoming municipalities experiencing high population growth.