Heavy rainfall is a major factor for landslide triggering. Here, we present an inventory of 47,523 landslides triggered by two precipitation episodes that occurred in May 2023 in the Emilia-Romagna and conterminous regions (Italy). The landslides are manually mapped from a visual interpretation of satellite images and are mainly triggered by the second rainfall episode (16–17 May 2023); the inventory is entirely original, and the mapping is supplemented with field surveys at a few selected locations. The main goal of this paper is to present the dataset and to investigate the landslide distribution with respect to triggering (precipitation) and predisposing (land use, lithology, slope and distance from roads) factors using a statistical approach. The landslides occurred more frequently on steeper slopes and for the land use categories of “bare rocks and badlands” and woodlands. A weaker positive correlation is found for the lithological classes: silty and flysch-like units are more prone to host slope movements. The inventory presented here provides a comprehensive picture of the slope movements triggered in the study area and represents one of the most numerous rainfall-induced landslide inventories on a global scale. We claim that the inventory can support the validation of automatic products and that our results on triggering and predisposing factors can be used for modeling landslide susceptibility and more broadly for hazard purposes.

Starting on the day after the mainshock, we mapped in the field and compiled all the available observations on earthquake environmental effects (EEEs) caused by the $Mw$ 7.8 subduction earthquake that hit the coastal region of Ecuador on 16 April 2016 (the Pedernales earthquake). These effects include: (a) permanent ground deformation, (b) open cracks, (c) liquefaction, (d) landslides, and (e) tsunami waves. We use these observations to evaluate the macroseismic field of the Environmental Seismic Intensity (ESI‐07) scale and compare our results with published macroseismic data collected using traditional damage‐based intensity scales. We found systematic difference in the assessed earthquake intensity and in the intensity attenuation with distance. A comparison of our dataset with the macroseismic data of six instrumental large‐subduction earthquakes that occurred between Ecuador and Chile suggests that, for the Pedernales earthquake, the ESI‐07 works better in the near field, whereas damage‐based scales are more reliable in the far field. Therefore, we generate a single integrated intensity dataset for the Pedernales earthquake, including data measured with the European Macroseismic Scale 1998 (EMS‐98), the modified Mercalli (MM) scale, and the ESI‐07 scale. This new integrated macroseismic field provides reliable and comparable information on the seismogenic source, suggesting that a similar approach could be successfully applied to refine the source model of past subduction earthquakes in Ecuador and elsewhere.

Moderate to strong earthquakes (i.e., Mw > ~6.0) commonly produce a complex network of ground ruptures, which are responsible for significant damage. Distributed faulting can affect wide areas (tenths of square kilometers), and expected displacement can be estimated through a probabilistic approach, considering distance from the primary fault and earthquake magnitude. Other factors may have a role in driving the occurrence of distributed faulting; nevertheless, they are not adequately addressed in the current modeling, due to a sensible lack of information. We study the 30 October 2016, Central Italy earthquake (Mw 6.5), to analyze the spatial pattern and geometric characteristics of distributed faulting. We found that distance from the primary structure, fault geometry, and lithology are key factors controlling the distributed faulting occurrence; the local structural setting (i.e., synthetic versus antithetic normal faults systems and relay zones) drives the spatial distribution of faults and the partitioning of the deformation. We also examine other four events occurred in the Italian Apennines since 1980, confirming that traditional models can underestimate the probability of distributed faulting. We suggest that a purely distance‐based probabilistic approach should be integrated using additional parameters derived from earthquake deformation fields or considering the reactivation of preexisting faults.