Quantifying the added value of impact-based warnings for flash flood monitoring using innovative multi-source impact data

The study examines the performance of impact-based flash-flood early warning systems compared with traditional hazard-based approaches. Instead of focusing only on meteorological thresholds (e.g., rainfall intensity), impact-based systems integrate data on exposure, vulnerability, and potential consequences to forecast where floods will cause the most disruption. The research evaluates how these systems function operationally and whether they provide more decision-relevant information for emergency managers and local authorities.

Findings show that impact-based warnings can significantly improve disaster preparedness by reducing false alarms and better identifying high-risk locations and populations. This allows authorities to prioritize evacuations, resource allocation, and response actions more effectively. However, the study also highlights that their accuracy depends heavily on the availability, quality, and standardization of impact data, meaning investments in data collection and integration are essential to fully realize their benefits.