Mitigating uncertainty in hydrogeological modeling by integrating time-lapse gravity data

Hydrogeological modeling is critical in effective water management, particularly in response to increasing water demands and climate variability. However, it is subject to significant uncertainty, especially in hydrological data-scarce regions such as mountainous areas. Reducing parameter and prediction uncertainty and efficiently quantifying and analyzing uncertainty are essential for optimizing water resource management. Time-lapse gravity (TLG) is an emerging hydrogeophysical technique that provides spatially-integrative information on water storage changes. It is a promising, non-invasive solution for filling hydrological data gaps, yet efficient assimilation into hydrogeological models has not yet been achieved.

To help address these challenges, we have developed a numerical framework for the coupled assimilation of TLG and traditional hydro(geo)logical data into groundwater models. The open-source, user-friendly python tool integrates coupled groundwater-gravity forward modelling and powerful inverse modeling procedures. It implements a highly accurate and computationally efficient forward 3-D gravity model. The framework accommodates varying levels of hydrological model complexity, as developed in FloPy (a python wrapper for MODFLOW-based models). Moreover, by integrating PyEMU (a python wrapper for PEST++), the framework employs First-Order, Second-Moment (FOSM)- based techniques, offering an efficient approach for estimating uncertainty. Our tool facilitates the assimilation of TLG data to constrain parameters, make predictions, and perform uncertainty analyses. Finally, we employ our framework to test the impacts of including TLG data in groundwater models. Our results show that TLG data can significantly reduce parameter and prediction uncertainty, as well as computational time.