Co-creating next-generation cloudburst warnings using ensemble forecasts and surface flood modelling

Extreme rainfall and cloudburst events are becoming increasingly frequent in Europe, placing growing pressure on urban drainage systems and local water utilities. In Denmark, current heavy-rainfall warnings are largely municipality-based binary alerts, which utilities often experience as too frequent, insufficiently localised, and difficult to translate into early operational action. Limited communication of forecast uncertainty and warning thresholds that are not aligned with drainage system design standards further reduce their practical value. As a result, many utilities primarily respond to cloudburst impacts after events occur rather than acting proactively.

This presentation presents the concepts and overall workflow of a co-created urban flood warning framework developed within the Horizon Europe CLEAR-EO project and the Danish funded initiative (VUDP) on future precipitation and flood warnings. The framework is designed to translate probabilistic rainfall forecasts into surface flood impact information that supports earlier and more confident decision-making by water utilities.

Within CLEAR-EO, we develop a modular, end-to-end workflow that links ensemble-based precipitation forecasts with near-real-time EO satellite data and high-resolution surface data. When rainfall thresholds are exceeded, the workflow activates an urban surface flood model that routes water across the urban terrain while accounting for drainage capacity and infiltration, enabling on-demand simulation of pluvial flood impacts. The modelling chain produces spatially explicit, probabilistic flood indicators, including flood depth, spatial extent, and warning levels at sub-metre resolution.

This presentation introduces the overall warning workflow, data integration strategy, and key design choices emerging from the combination of ensemble forecasting, EO-based datasets, surface flood models, and close collaboration and co-creation with end users. A key component of the framework is the generation of hydrologically conditioned, high-resolution DSM, which provides the topographic basis for urban drainage modelling and flood simulations. The workflow integrates classified airborne LiDAR point clouds with semantic infrastructure information from OpenStreetMap, improving drainage connectivity while preserving geometric fidelity. Early experiences indicate that co-developing probabilistic, impact-based warning products that explicitly communicate forecast uncertainty can strengthen utilities’ ability to act earlier and more precisely under uncertain cloudburst conditions.

Ongoing work will further refine the modelling chain, strengthen validation, and extend the approach to additional European case studies, contributing to the development of future national and local heavy-rainfall warning services.