RER2023: a freely accessible landslide inventory dataset from the May 2023 Emilia-Romagna event

Landslide inventories play a crucial role in assessing susceptibility, hazards, and risks, particularly in mountainous regions where devising resilience strategies becomes essential. The significance of such inventories becomes even more pronounced in the context of climate change, which may render existing databases inadequate due to evolving stability conditions. A clear illustration of this was seen in May 2023, when the Emilia-Romagna region in Italy experienced two significant rainfall events. These events triggered widespread flooding and thousands of landslides, including shallow debris slides and flows on slopes that had been considered stable, as historical data had not recorded previous landslides there. The total damages have been estimated to surpass 9 billion euros, affecting roads, railways, buildings, and cultural heritage sites, along with the destruction of bridges, power facilities, and communication lines. Additionally, agricultural fields, farming operations, and cultivated slopes saw significant disruption over an area of about 1000 km². Fifteen people lost their lives due to the flooding and two due to landslides.In the aftermath, our team supported local and national agencies by engaging in field surveys and immediate assessments to address urgent public safety concerns. Our focus then shifted to mapping the landslides, initially identifying impacted roads and buildings to coordinate emergency responses and perform preliminary damage evaluations. We subsequently completed a detailed landslide inventory, producing a comprehensive map of all landslides triggered by the rainfall. This map has now been adopted by the Po River Authority and the Emilia-Romagna region as the official record for the May 2023 event and is being used by the Commission for Reconstruction to guide the recovery efforts.The landslide inventory includes 80,997 polygons and has been made publicly available through the Zenodo repository (DOI: 10.5281/zenodo.13742643; https://essd.copernicus.org/preprints/essd-2024-407/). The dataset is provided in a shapefile format, which includes detailed attributes like the type of landslide and the geological unit of each polygon, facilitating in-depth analysis. Additionally, the Emilia-Romagna region's geoportal offers unrestricted access to extensive spatial data, which can be integrated with our landslide map to refine both traditional and advanced machine-learning predictive models. The frequent shallow planar failures observed during the event also offer an exceptional opportunity to test physically-based slope stability models. We invite the scientific community to utilize this dataset or to collaborate on research projects that could leverage this tragic event to deepen our understanding of landslide risks.