"AI, particularly machine learning, is beginning to help. Because AI is skilled at analyzing large datasets and identifying complex patterns, it has shown potential to fill in important gaps for California water. These include estimates of past streamflows—both in places that weren’t monitored and/or were altered by human actions—as well as forecasting future streamflows. However, AI’s ability to accurately predict streamflow hinges on having sufficient data. In California, these models are further challenged by the state’s complex, human-managed water system."

"AI can also be used to supplement stream gages, the gold standard for monitoring, when there is limited funding. The USGS-developed Flow Photo Explorer, for example, uses machine learning models (namely, RankNet) to rank photos of flow and estimate relative river flows. These estimates provide timing of extreme high- and low-flow events and—combined with other stream information—can estimate the flow of water, providing an affordable way to supplement river flow data from traditional stream gages at a fraction of the cost."