Earth observation (EO) data are useful tools to analyse geomorphological processes, among which slow-moving landslides triggered by rainfall. EO data are also used to evaluate climate change and to assess its impact on geomorphological processes and geo-hydrological phenomena. The latter is the topic of the Project OT4Clima (Innovative Earth Observation technologies to study Climate Change and its impact on the environment) joined by CNR-IRPI within a consortium that includes other CNR institutes, universities and private companies. The OT4CLIMA project moves from the awareness that the impacts of climate change on the environment need to be better observed, understood, and modelled, especially at a regional scale, in order to put in place appropriate and effective risk mitigation strategies. Within the project, the CNR-IRPI group works on the development of rigorous methods and procedures for evaluating the impact of climate and its change on landslides, in particular on those characterized by a slow cinematic, at a regional scale. The test site is represented by four catchments located in the Basilicata region, southern Italy, namely the basins of the Bradano, Basento, Agri, and Sinni rivers. Long-term rainfall series gathered from 22 rain gauges located in the four catchments are analysed to evaluate the presence of temporal trends. To this aim, non-parametric and statistical tests are applied to the series. Historical landslide information is gathered from the analysis of the IFFI (Inventario dei Fenomeni Franosi in Italia) database, the Idrogeo platform (https://idrogeo.isprambiente.it/app/) and the AVI (Aree Vulnerate in Italia) catalogue. Only some types of landslide movements are considered, namely rotational-translational slides, slow slides/flows, complex movements. Moreover, Copernicus Sentinel-1 images are employed to detect the spatial and temporal distribution of slow earth surface deformations. The obtained results are used for checking the completeness of the landslide inventories. More in detail, the deformation maps of the test site are obtained by means of the application of the SBAS (Small BAseline Subset) technique to three datasets of Sentinel-1 images_ t146 ascending orbit and t51 and t124 descending orbits, for the period 2015-2020. Then, a comparative analysis of rainfall data with displacement series is carried out with the aim of identifying clusters of satellite measurements with homogeneous behaviour likely correlated to variations in the rainfall regime. In particular, only the points with a mean velocity in the observation higher than 0.1 cm/year are considered to be moving. Moreover, only the displacement series of points located in areas mapped as landslides - as for the historical inventories - and sited within the influence regions of each rain gauge in the study area are analysed. A 10-km circular buffer centred in the stations are used to define the influence region of each station. The displacement series are analysed and compared to the rainfall series to search for correlations and to evaluate the effects of climate drivers on slow moving landslides.

Sediment connectivity, defined as the degree to which a system facilitates the transfer of water and sediment through itself by means of coupling relationships between its components, has recently emerged as a paramount property of geomorphic systems. The growing interest of the earth sciences community in connectivity led this property becoming a key concept concerning sediment transfer processes analysis and one of the building blocks of modern geomorphology. The increasing availability of high-resolution Digital Elevation Models (DEMs) from different sources as LiDAR and Structure from Motion (SfM) paved the way to quantitative and semi-quantitative approaches for assessing sediment connectivity. A geomorphometric index of sediment connectivity (Borselli et al., 2008; Cavalli et al., 2013), based on DEM derivatives as drainage area, slope, flow length and surface roughness, has been developed along with related freeware software tool (SedInConnect). The index aims at depicting spatial connectivity patterns at the catchment scale to support the assessment of the contribution of a given part of the catchment as sediment source and define sediment transfer paths. The increasing interest in the quantitative characterization of the linkages between landscape units and the straightforward applicability of this index led to numerous applications in different contexts (Cavalli et al., 2020). This work presents and discusses the main applications of this sediment connectivity index. Being a topography-based index, it is focused on structural aspects of connectivity, and quality and resolution of DEMs may have significant impact on the results. Future development should consider process-based connectivity and incorporate temporal variability directly into the index. Moreover, this work demonstrate that, when carefully applied considering the intrinsic limitations of the mainly topographic-based approach, the index can rapidly provide a spatial characterization of sediment dynamics, thus improving the understanding of geomorphic system behavior and, consequently, hazard and risk assessment.