Open sourcing Google’s hydrology framework

From theory to operational reality

In the Global Status of Multi-Hazard Early Warning Systems 2025 report, the World Meteorological Organization recognizes that both local data and Indigenous and Local Knowledge (ILK) are critical components of effective disaster warnings, and notes that “[t]he systematic integration of ILK into risk knowledge production is still the exception rather than the norm.” Our open source flood forecasting workflow addresses the report’s finding by allowing regional forecasters to take direct, hands-on control over AI-powered forecasting models. These frameworks are relatively easy and inexpensive to train, providing accuracy without the complexity of traditional hydrological forecasting models and allowing users to incorporate their own specialized data for training and prediction.

Readily adoptable open-source tools are critical for bridging the gap between technological innovation and the real-world effectiveness of flood hazard systems, particularly for accelerating capacity development around early warning systems.

The operational potential of this release is best illustrated by our partnership with CHMI. Their collaboration was key to validating that our AI-based model provides forecasts comparable in quality to traditional, locally calibrated conceptual models. CHMI also developed an adapter that integrates the hydrology open source framework into the Delft-FEWS platform, a popular operational flood forecasting tool used by national and local flood forecasting agencies, NGOs, and private companies to drive predictive models. Delft-FEWS is operated and maintained by the Deltares research institute. This allows CHMI and other hydrological services worldwide to use the model in their standard workflows. This integration serves as a blueprint for how global agencies can include machine learning in their water management workflows.

Beyond larger institutions like CHMI, the open source model release offers a scalable, accessible tool, democratizing access to advanced forecasting and opening the door for resource-constrained regions and local teams to leverage high caliber insights without the need for costly traditional forecasting infrastructure.

The international meteorological community has recognized the value of this open-science approach. Dr. Hwirin Kim, Chief of Hydrological Modelling and Forecasting Section at the World Meteorological Organization, notes: “I welcome the expansion of open-source hydrological modeling tools that are critical to supporting how societies manage water resources and respond to environmental challenges. We at WMO are keen to support open-source, interoperable, Member-driven models and tools that can help save lives and advance the global mission to ensure communities everywhere are forewarned about hazards to protect their lives and livelihoods.”