A novel prototype national-scale landslide nowcasting system for Italy combining rainfall thresholds and risk indicators

Regional- and national-scale landslide warning systems are usually based on rainfall thresholds that forecast the possibility of landslide occurrence over wide spatial units called alert zones (AZs). This work proposes a substantial improvement of the state- of-the-art by combining the rainfall threshold outcomes with a set of spatially explicit risk indicators aggregated at the municipality level. The combination of these two different techniques is per- formed by means of a dynamic matrix, which was purposely cali- brated to provide an output in the form of five possible levels of risk (from R0 to R4), which are connected to the growing intensity of expected impacts and a pre-defined confidence in issuing warn- ings without omitting alarms. Italy (about 300,000 km2) is used as a case study, producing a set of rainfall thresholds differentiated for 150 AZs and providing a specific calibration of the dynamic risk matrix for each of them. The verification of the matrix out- puts was satisfactory as no AZs experienced landslides at the R0 level; only two of them had more than 10% of landslides at the R1 level, and most of the AZs had more than 90% of the landslides in the R2 to R4 risk classes. A comparison with a nation-wide data- set of very severe hydrogeological disasters further corroborated the consistency of the model outputs with real scenarios, as most part of the impacts occurred in places and times when the matrix outputs were at the highest levels. The proposed methodology represents a reliable improvement for state-of-the-art territorial warning systems, as it brings two main advances: the spatial reso-lution is greatly improved, as the basic spatial unit for warning is downscaled from AZs to municipalities (whose average extension, in Italy, is about 1770 and 38 km2, respectively); second, the out- puts can better address the needs of landslide emergency man- agement, as the warning are specifically addressed to small areas based on the expected impacts (since risk indicators are used in the dynamic matrices), rather than on the mere probability of landslide occurrence.