SEA - Global Early Warning System for Severe Flood Events

Floods are one of the most common natural disasters, and the rate of flood-related disasters has more than doubled since the year 2000. Consequently, accurate and timely warnings are critical for mitigating flood risks, especially for major events impacting thousands. While recent advancements in global hydrological models offer significant potential for widespread discharge predictions, their operational use in warning systems is often limited by the difficulty of validating forecasts in individual ungauged locations.

To address this limitation, we present a global early warning system for major flood events that builds on top of discharge predictions from the Global Hydrological Model (Nearing et al., 2024). Our core concept shifts the focus from predicting and validating the hydrological predictions to the targeted identification of severe events.

Our approach employs a two-step algorithm. First, an agglomerative clustering algorithm is used to group discharge predictions that exceed predefined return period thresholds, resulting in extended spatiotemporal clusters representing potential flood events. Second, a supervised machine learning classification model is trained to identify clusters with a high likelihood of major flooding based on their properties, such as the affected area, estimated affected population, and severity at individual gauges. For training and validation, we utilize the Dartmouth Flood Observatory (DFO) and GDACS datasets, providing records of historical flood events.

Leveraging this approach, we were able to enhance our global early warning capabilities for severe flood events, increase coverage and extend reach to previously uncovered regions. Our results underscore the potential of our approach to improve global early warning for severe flood events.