SCIBA: A Geo‐Dataset of Damaging Rainfall Related Landslides and Floods Throughout 113 Years on a Mediterranean Study Area

This paper introduces SCIBA, a novel dataset documenting floods (F) and landslides (L) triggered by rainfall that affected the road-railway network in the municipalities of Scilla and Bagnara (Calabria, Italy) between 1911 and 2024. The study addresses the central research question: How can historical rainfall-induced flood and landslide events be systematically documented and used to improve predictive models for early warning systems in transport infrastructure? In response, SCIBA offers a comprehensive, spatially and temporally detailed dataset aimed at supporting the Disaster Risk Reduction (DRR) community and researchers developing empirical models for forecasting rainfall thresholds that precede F and L events. The unique contribution of this work lies in the systematic compilation and georeferencing of 281 historical FL events—a rare and valuable resource in a context where such data are typically fragmented or unavailable. SCIBA bridges this gap through extensive archival research, such as the State Archive, the Regional Civil Protection archive, and ANAS, the agency responsible for state roads in the region. All the records include the spatial references (geographic coordinates and place names) and temporal localization (to the day, and in 18.6% of cases, the exact hour). Moreover, each record integrates daily rainfall data from two operational rain gauges (Scilla at 73 m a.s.l. and Bagnara at 30 m a.s.l.) for the day of the event and the preceding 4 days, enabling analysis of both daily and cumulative rainfall as triggering factors. Despite some unavoidable gaps in historical documentation, SCIBA stands out as a ready-to-use dataset that supports the development of cause-effect models for rainfall-induced hazards. Provided in GIS format, the dataset not only enhances understanding of past events but also identifies critical hotspots for monitoring during intense rainfall, contributing directly to emergency planning, traffic management, and the resilience of transport networks.