Case Study: Early warning and accurate peak flow predictions during extreme storms

During high flows and shifting weather, traditional forecasting tools often fall short. Gauge outages, equipment failures, and rapidly evolving storm tracks can leave operators without the reliable data they need to make timely decisions.

HydroForecast uses machine learning to deliver probabilistic streamflow forecasts that give water managers an added layer of confidence, even when conditions are most volatile. With accurate peak flow predictions arriving days ahead of standard models, operators had the early warning they needed to stay ahead of rapidly changing inflows.

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