The World Meteorological Organization says local data and Indigenous and Local Knowledge (ILK) are critical to effective disaster warnings, while also noting that “the systematic integration of ILK into risk knowledge production is still the exception rather than the norm.”
Against that backdrop, an open source flood forecasting workflow has been released to give regional forecasters direct control over AI-powered forecasting models. The system is described as relatively easy and inexpensive to train, with the aim of providing accuracy without the complexity of traditional hydrological forecasting models and letting users incorporate their own specialized data for training and prediction.
The release is positioned as a way to bridge the gap between technological innovation and real-world flood hazard systems, especially for early warning systems. It is also intended to help accelerate capacity development around those systems.
The operational case for the model was highlighted through a partnership with CHMI. The collaboration was used to validate that the AI-based model produces 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, which is 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.
The integration means CHMI and other hydrological services worldwide can use the model in their standard workflows. The release also offers a scalable, accessible tool for resource-constrained regions and local teams that do not have access to costly traditional forecasting infrastructure.
Dr. Hwirin Kim, Chief of Hydrological Modelling and Forecasting Section at the World Meteorological Organization, said: “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.”
Source: research.google.
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