Predicting how much water will flow in rivers during upcoming seasons is crucial for managing water resources, especially in regions like southern Africa that face unpredictable climate conditions. Traditional forecasting tools often don’t work well at the local level, making it hard to plan ahead.

This study uses advanced Machine Learning (ML) techniques to improve seasonal streamflow forecasts for Lake Kariba on the Zambezi River. The approach combines sea surface temperature data from key global climate patterns—like El Niño and the Pacific Decadal Oscillation—with a special model that selects only the most relevant climate signals for prediction.

Compared to leading global forecasting systems, the ML-based model performs much better in predicting seasonal water flows, especially during years with extreme conditions. The improved forecasts are then used to guide smarter reservoir operations, helping to produce more reliable hydropower.

As a result, Kariba Dam could generate an additional 4 gigawatt-hours of electricity per year—worth about $320,000—by using these improved forecasts. This shows how machine learning can turn better climate predictions into real economic and energy benefits.