Groundwater sustains much of the world’s irrigated agriculture. This reliance comes at a cost, as overextraction of groundwater has triggered widespread environmental consequences. However, the consequences and its actual use remain poorly monitored. To address these gaps, Fahim’s research integrates satellite and UAV remote sensing, artificial intelligence, and process-based modeling to develop scalable, data-driven frameworks for understanding and predicting groundwater dynamics across multiple spatial scales.

The talk will begin with a global perspective, highlighting Fahim’s work on mapping land subsidence induced by groundwater withdrawal, an environmental threat overlooked in many regions. This research identified the global hotspots of groundwater stress, revealing subsidence as a global indicator of unsustainable groundwater use. Transitioning from global scale, Fahim will present his current research on the Western United States, a region bearing the consequences of decades of groundwater overextraction. Here, Fahim will demonstrate how satellite data coupled with physics-constrained machine learning are used to quantify complex hydrologic fluxes and groundwater pumping in irrigated agriculture. Finally, the talk will showcase a framework of plant-scale evapotranspiration (ET) modeling, a key indicator of agricultural water use, driven by very high-resolution UAV data and energy balance model.