Effective water resource management depends on understanding how water moves and changes underground. One way to observe these changes is through time-lapse geophysical monitoring, such as electrical resistivity tomography (ERT). While this method is powerful, it can be difficult to interpret what the changes in resistivity mean for actual water-related processes.

To improve this, we use computer models that simulate how water and chemicals move underground and then convert their output into expected changes in resistivity. This allows us to predict what ERT data should look like for a given hydrological process, such as groundwater pumping or aquifer storage and recovery. These predictions can help assess if ERT monitoring will be useful at a site, and can also be used to train machine learning models.

We’ve built a web-based platform that automates this entire process in the cloud, allowing users to simulate various water scenarios, run models, and view results alongside key data like rainfall or pumping rates. This tool helps researchers and practitioners evaluate the potential of time-lapse geophysics for monitoring water resources quickly and with minimal effort.