Managing water has never been an easy task; learn how HydroForecast's novel approach to hydrologic modeling can help provide reliable and accurate flow forecasts even under today's more variable and extreme events.
HydroForecast learns to represent hydrologic processes by identifying relationships between satellite observations, basin characteristics, meteorological forecasts, and streamflow measurements.
Our approach is distinct from purely data-driven machine learning models in that we use physical science to guide and constrain the relationships between inputs and streamflow predictions. We believe that the best model is one grounded in scientific principles and enhanced by finding deep connections in the relationships between data.
Deploy at a specific point of interest, anywhere on Earth, even if there is not a physical gauge nearby.
Engineered with industry-leading cybersecurity practices to ensure that sensitive data remains private and secure.
Deployed in under 90 days and seamlessly integrated with existing solutions such as Delft-FEWS, HEC-RAS, RiverWare, PLEXOS, and Excel-based workflows.
Incorporates dynamic land surface and climatological inputs that reflect our changing world and ensure reliable performance in extreme conditions.
Provides the full distribution of outcome probabilities so users can anticipate periods of uncertainty and manage risk