Evaluation and optimization of a site-specific early warning tool for flood hazards in Catalonia, Spain

Heavy rainfall events have become increasingly frequent in recent decades, often triggering severe floodings that pose significant challenges to urban and rural communities. Consequently, robust early warning systems have emerged as a key strategy for adaptation and mitigation measures. The risks and impacts of flooding can vary significantly even within small geographic areas due to factors such as terrain, urban infrastructure, and drainage systems, as well as the sporadic nature of rainfall. Site-specific flood warning tools address local variations by providing warnings tailored to each area's unique conditions. These tools can help decision-makers and emergency responders navigate multiple challenges, improve preparedness for extreme events, and promote public awareness of flood risks.

Catalonia, located in northeast Spain and characterized by a Mediterranean climate, is occasionally affected by intense rainfall episodes. Severe flash floods have caused significant damage in recent years, leaving communities grappling with the aftermath, such as the case of Terrassa municipality in 2023 and 2024. A real-time, site-specific, flood early warning tool has already been applied in pilot locations in Catalonia within the Horizon Europe RESIST project (2023-2027). The tool integrates real-time and forecasted meteorological data to issue flood hazard warnings for vulnerable locations. In this study, we focus on a methodology for evaluating and optimizing the warning tool to minimize false alarms and missed events. Evaluation is essential to ensure the reliability and usability of the tool and build the trust of end-users, particularly emergency responders and affected communities. We present an evaluation of the tool’s different components, including the warning level thresholds, the integration of different data sources, and lead time analysis. Optimization, on the other hand, involves refining algorithms, integrating additional local data sources tailoring the tool to specific local characteristics, and incorporating feedback from end-users.