GravCHAW: A software framework for the assimilation of time-lapse gravimetry data in groundwater models

We present an open-source python framework GravCHAW (Gravimetric Coupled Hydro Assimilation Workflow) for the assimilation of time-lapse gravimetry (TLG) data into numerical groundwater models. This framework enables quantitative exploration of the full potential of TLG in reducing hydrogeological data gaps. TLG is a non-invasive geophysical method that can be used to monitor spatiotemporal variability of groundwater storage changes. At the software’s core is a site-independent coupled hydrogravimetric model that accurately simulates TLG data. Using a range of advanced optimization and uncertainty analysis approaches in a Bayesian context, built around the hydrogravimetric model, the framework assimilates TLG data to estimate parameters, make predictions, and quantify uncertainty across diverse problem scales. In doing so, it accounts for both parameter priors and observation uncertainty, enabling a probabilistic uncertainty analysis. The framework can perform a coupled hydrogravimetric inversion assimilating TLG data individually or jointly with hydrological observations. To illustrate some of the core capacities of the framework, we apply it to a simple groundwater model and explore the propagation of observation uncertainty to parameter and model predictions. The results show that TLG can accurately estimate model parameters and significantly reduce uncertainty in parameters and predictions, both when assimilated individually and jointly with hydraulic head data, provided that the signal-to-noise (SNR) is sufficiently high. In this condition, while joint assimilation results in greater uncertainty reduction in our example case, TLG appears to have the most substantial contribution. GravCHAW will enable the reduction of uncertainty in groundwater models by integrating TLG data, which will be particularly impactful in data-poor situations.