Rainfall-Driven Flash Flood Detection: A Framework for Ungauged Basins

Flash floods have the potential to damage infrastructure and buildings, and pose a considerable threat to human life. The short lag times associated with flash floods (typically a few hours) present a significant challenge to existing flood warning systems. These systems currently rely on rainfall and runoff measurements, as well as hydrological models. Consequently, they are of limited applicability in ungauged catchments. This is a critical issue given that climate change is intensifying extreme rainfall and thereby increasing the potential for flash flooding.

This study investigates the detection of flash floods based solely on rainfall characteristics, thus eliminating dependency on runoff measurements and hydrological infrastructure. Using high-resolution radar rainfall data and 15-minute runoff observations, 1,330 extreme rainfall-runoff events are selected across 147 German catchments with an area of up to 100 km². These events are subsequently classified as either flash or non-flash floods using a rainfall-runoff-based classification scheme as a reference, with 103 of the selected events identified as flash floods. For each event, various space-time rainfall characteristics are quantified. A random forest model for flash flood detection is then trained using only the rainfall metrics and static catchment attributes. The main aim is to assess the potential for rainfall-driven flash flood detection without relying on runoff. In addition, the most relevant rainfall characteristics associated with flash floods are identified, thereby improving our understanding of the underlying drivers.

In future work, the developed detection approach will be combined with real-time rainfall nowcasting to enable earlier prediction and warning of flash floods.