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The scaling challenge: Local precision vs. global reach
Specialized, hyper-local early warning systems have been engineered to address flash floods from rainfall in specific urban settings, with examples in Florida (US), Barranquilla (Colombia), Manila (Philippines), Nakhon Si Thammarat (Thailand), Mayaguez (Puerto Rico), and Barcelona (Spain). These systems typically rely on a network of physical sensors monitoring variables like direct and radar-inferred precipitation, water levels and flow velocities. While highly accurate for their specific locations, they are difficult to scale because of the high costs of hardware deployment, the need for site-specific calibration algorithms and engineering expertise.
At a broader level, initiatives such as the WMO’s Flash Flood Guidance System (FFGS), the European Runoff Index based on Climatology (ERIC) flash flood indicator, and the US National Weather Service (NWS) Flash Flood Warnings system provide wider coverage through remote sensing and numerical weather models. These systems, however, encounter significant hurdles regarding global implementation. A primary issue is their dependency on high-resolution hydrological maps and radar-based weather forecasts, resources that are largely unavailable within the Global South. Furthermore, the reliance on professional hydrologists to interpret complex model data and distribute actionable warnings presents a second major challenge.
To achieve near-global reach, our model uses only global weather products (NASA IMERG, NOAA CPC) as well as real-time global weather forecasts from the ECMWF Integrated Forecast System (IFS) High Resolution (HRES) atmospheric model and the AI-based medium-range global weather forecasting model by Google DeepMind. The system currently operates at a 20×20 kilometer spatial resolution, a constraint primarily driven by the resolution of globally available data sources.
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