Using Remote Sensing and Integrated Hydrologic Models to Characterize the How Irrigated Agriculture Affects Highly Overdrawn Aquifers in the United States

Irrigated agriculture consumes vast amounts of water and energy, yet it is key to high crop yields. This is especially important as we are facing increasing population and high variability in precipitation related to climate change. However, groundwater storage continues to be overdrawn due to irrigation withdrawals at unsustainable rates. We have been quantifying the effects of irrigated agriculture, management practices, and climate across the High Plains Aquifer, California’s Central Valley, and Michigan in the United States. Our team uses agricultural and integrated hydrologic models to characterize how management changes affect water resources and crop productivity. We map annual changes in irrigated agriculture and solar panel areas by integrating biophysical data and remote sensing imagery using machine learning. We then use this information as input to model the effects of agricultural management strategies on water use, crop yield, energy use, and water supplies. In a portion of the High Plains Aquifer, we showed the effects of two very different irrigation adaptation strategies on irrigation water use. This analysis showed that a local management solution, where groups of regional farmers collectively agreed to reduce their pumping, was a much more effective solution than a technology-based approach where more efficient irrigation technology was adopted that uses less water per acre. This research shows how the integration of remotely-sensed and ground-based data into fully-distributed integrated hydrologic models can help stakeholders move toward more sustainable agricultural practices.