The Colorado River begins as snow. Every spring, the mountain snowpack of the Rockies melts into streams that feed into reservoirs that supply 40 million people across seven U.S. states. The system has worked, more or less, for a century. That century is over. By some measures, 2026 is shaping up to be the worst year the river has seen since records began. Flows are down 20 percent from 2000 levels. Lake Powell, the reservoir straddling Utah and Arizona, may drop below the threshold for generating hydropower before the year is out. The negotiations between the seven states over how to share what’s left have collapsed twice, and the U.S.

federal government is threatening to impose its own plan. While the states argue and the river shrinks, a growing set of machine learning tools is being deployed across the basin. Federal water managers are running millions of simulations to stress-test reservoir strategies against different possible futures. Researchers are forecasting streamflow months out using satellite data and deep learning. These technologies don’t promise to resolve the crisis, but they’re making the trade-offs visible. They’re showing, more precisely than ever before…