The U.S. Geological Survey (USGS) has unveiled its new River DroughtCast tool, which leverages machine learning to forecast streamflow drought across the lower 48 states. This tool provides critical insights into river and stream conditions, enabling predictions of drought as far ahead as 13 weeks. The forecasts are based on over a century of streamflow data collected from thousands of USGS streamgages, some of which have been recording data for over 100 years. The River DroughtCast aims to address the gap between short-term weather forecasts and longer-term seasonal water supply assessments.
Currently operational as of March 17, 2026, the tool predicts streamflow conditions through June 15, 2026. Streamflow drought, characterized by prolonged periods of below-normal river and stream levels, can significantly affect water availability, even when normal rainfall resumes. John Hammond, USGS project manager for the drought forecasting system, emphasized the innovative use of historical data, stating, “The USGS is putting more than a century of streamflow data to work in a completely new way, using machine learning to predict streamflow drought weeks in advance.”
Unlike meteorological droughts, which are primarily defined by insufficient rainfall, streamflow droughts are influenced by factors such as soil moisture, snowpack, and groundwater levels. This complexity makes predicting streamflow drought particularly challenging yet essential for effective water resource management. Farmers dependent on surface water for irrigation, for example, may adjust planting schedules based on the forecasts, while municipal water managers could implement conservation measures in anticipation of water shortages.
The River DroughtCast tool currently forecasts conditions for over 3,000 USGS streamgage locations, each with at least 40 years of data. Users can select any forecast length between one and 13 weeks, with the tool offering its most reliable predictions in the first four to six weeks. The initial week of severe or extreme drought conditions is accurately predicted approximately 75% of the time, though this reliability declines to about 55% by the 13th week. Each forecast also includes confidence estimates, allowing users to gauge the reliability of predictions over various timeframes.
As the tool evolves, developers are working toward enhancing public access to areas lacking streamgages while further improving forecast accuracy. This initiative aligns with the USGS’s broader mission, which has increasingly incorporated artificial intelligence to enhance scientific efficiency. From detecting earthquakes to mapping critical mineral resources, the agency aims to integrate AI effectively and responsibly into its scientific and operational practices.
The USGS’s efforts are bolstered by Executive Order 14179, which aims to promote American leadership in artificial intelligence, as well as Secretary’s Order 3444, which focuses on the Department of the Interior’s path toward AI transformation. The agency has published its AI Strategy, which underscores its commitment to innovative practices in science and business.
As drought conditions become more prevalent due to climate change, tools like River DroughtCast are becoming increasingly vital for effective water management and resource planning. By providing timely forecasts, the USGS hopes to empower farmers, water managers, and recreation operators to make informed decisions that can mitigate the impacts of water scarcity. The advent of this technology not only marks a significant advancement in environmental monitoring but also reflects the growing importance of data-driven approaches in addressing climate-related challenges.
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