Landslides are heterogeneous natural processes. Given the significant variability of the different landslide types and in particular of their dimensional characteristics, style of movement and triggering mechanisms, multiple models and approaches have been developed in the literature. At present, no single model or modelling approach is applicable to all the different landslide types. This also limits the prediction of the spatial occurrence of landslides achieved through susceptibility models which aims at estimating the probability of the spatial landslide occurrence. Such models divide and classify a territory on the basis of its propensity to specific landslide types, using relationships linking the occurrence of landslides with their conditioning factors. In this work, we identify and apply a methodology for the combined modelling of susceptibility posed by different landslide types. The methodology initially envisages the modelling of the susceptibility for the individual landslide types using differentiated approaches, calibration, evaluation and application phases and subsequently their combination. In the modelling, the pixel is used as the base mapping unit, while the slope unit is used as a mapping unit for a subsequent spatial susceptibility aggregation. The result maps, here defined as "composite susceptibility maps", subdivide a territory according to its propensity to failure of one or more landslide types. The proposed model was applied as part of the Project "Paesaggi Sicuri", funded by the Italian Ministry of Cultural Heritages and Activities (MIBACT), in a portion of the UNESCO site "Porto Venere, Cinque Terre e Isole" characterized by the occurrence of different landslides types, namely slides, falls and flows. The derived composite susceptibility map allowed a more realistic analysis of the geo-hydrological vulnerability being able to consider jointly the different landslides types occurring in the study area.

As prioritized by the Sendai Framework, enhancing disaster preparedness is fundamental for the effective response, for taking actions in anticipation of events, and to ensure that the appropriate capacities are in place for effective response and recovery at all levels. Under this view early warning systems can be seen as irreplaceable tools to supporting the Civil Protection authorities in the preparedness and response phases. This is particularly relevant for the case of rainfall-induced slope failures that occur worldwide every year, claiming lives and causing severe economic disruption. Implementing early warning systems to forecast the occurrence of such geo-hydrological phenomena is difficult and challenging both from the scientific and technological side. Here we present a framework developed in Italy (Rossi et al., 2012) for the operation forecasting of rainfall induce landslides over large areas, which includes (i) criteria, tools and technological supports for the collection of rainfall induced landslide occurrences; (ii) scientific methods and tools for the analysis of the relation between rainfall and landslides occurrences and for the landslide rainfall threshold definition; (iii) operational early warning system procedures and technological supports for the rainfall induced landslide forecasting; (iv) interfaces for the query and analysis of the early warning system outputs; (v) criteria, tools and technological supports for the validation of the early warning system outputs. The main lessons learned in the last decade and the most critical issues experienced implementing such framework for the entire Italian territory (SANF system) and for different regions both in Italy and India (SARF systems, LANDSLIP LEWS system) are highlighted and discussed.

Landslides cause every year worldwide severe damages to the population. A quantitative knowledge of the impact of landsliding phenomena on the society is fundamental for a proper and accurate assessment of the risk posed by such natural hazards. In this work, a novel approach is proposed to evaluate the spatial and the temporal distribution of societal landslide risk from historical, sparse, point information on fatal landslides and their direct human consequence.s (Rossi et al., Accepted). The approach was tested in Italy, using a detailed catalogue listing 5571 fatalities caused by 1017 landslides at 958 sites across Italy, in the 155-year period 1861 - 2015. The model adopting a Zipf distribution to evaluate societal landslide risk for the whole of Italy, and for seven physiographic and 20 administrative subdivisions of Italy. The model is able to provide estimates of the frequency (and the probability) of fatal landslides, based on the parameters, namely (i) the largest magnitude landslide F, (ii) the number of fatal events E, and (iii) the scaling exponent of the Zipf distribution s, which controls the relative proportion of low vs. large magnitude landslides. Different grid spacings, g and circular kernel sizes, r were tested finally adopting g = 10 km and r = 55 km. Using such geometrical model configuration, the values of the F, E and s parameters were derived for each grid cells revealing the complexity of landslide risk in Italy, which cannot be described properly with a single set of such parameters. Based on such modeling configuration. This model configuration allowed to estimate different risk scenarios for landslides of increasing magnitudes, which were validated checking the anticipated return period of the fatal events against information on 130 fatal landslides between 1000 and 1860, and eleven fatal landslides between January 2016 and August 2018. Despite incompleteness in the old part of the record for the low magnitude landslides, and the short length and limited number of events in the recent period 2016 - 2018, the anticipated return periods are in good agreement with the occurrence of fatal landslides in both validation periods. Despite the known difficulty in modelling sparse datasets, the proposed approach was able to provide a coherent and realistic representation and new insight on the spatial and temporal variations of societal landslide risk in Italy.

The Italian territory is affected by rainfall-induced landslides, which can result in loss of lives and widespread damage. Empirical rainfall thresholds are widely used for predicting the occurrence of failures triggered by intense or prolonged rainfall at national and regional scale, even if they have empirical limitations for small geographical areas. This limitations depend on the size of the landslide catalogue available for the definition of statistically robust rainfall threshold over small areas (Peruccacci et al. 2012). Martinotti et al. 2017 theorized, designed and tested the Ensemble-Non-Exceedance Probability (E-NEP) algorithm, which exploits standard rainfall records obtained from rain gauges and a given rainfall threshold to quantitatively assess the landslide occurrence probability over time. Using the rainfall threshold defined by Peruccacci et al. 2017 for the entire Italian territory, they applied the E-NEP algorithm to analyse the landslides triggered during a time period of torrential rain between 1 and 6 September 2014 in the Gargano Promontory (Puglia) and found that the E-NEP metrics provided better diagnostics than the single metrics often used for landslide forecasting.

Effective infiltration (EI) is the amount of precipitation infiltrating into the soil and recharging the aquifers. EI is estimated using direct or indirect methods or using water balance models. Direct and indirect methods lead to biased EI estimates, since based on simplified schemas of groundwater bodies and of their recharge mechanisms. Water balance models include different processes and variables, but they are seldom applied due to the limited availability of the input data, particularly at regional scales. We propose a method for EI estimation over large areas based on a monthly water balance model exploiting open source software and free/open data. The model integrates procedures to estimate EI and other water balance components, accounting for the uncertainty of input data. The model is calibrated in the Central Apennines (Italy), where EI reference values are available from the literature, and later applied in the Alps, where regional EI estimates are missing.

As prioritized by the Sendai Framework, enhancing disaster preparedness is fundamental for the effective response, for taking actions in anticipation of events, and to ensure that the appropriate capacities are in place for effective response and recovery at all levels. Under this view early warning systems can be seen as irreplaceable tools to supporting the Civil Protection authorities in the preparedness and response phases. This is particularly relevant for the case of rainfall-induced slope failures that occur worldwide every year, claiming lives and causing severe economic disruption. Implementing early warning systems to forecast the occurrence of such geo-hydrological phenomena is difficult and challenging both from the scientific and technological side. Here we present a framework developed in Italy for the operation forecasting of rainfall induce landslides over large areas, which includes (i) criteria, tools and technological supports for the collection of rainfall induced landslide occurrences; (ii) scientific methods and tools for the analysis of the relation between rainfall and landslides occurrences and for the landslide rainfall threshold definition; (iii) operational early warning system procedures and technological supports for the rainfall induced landslide forecasting; (iv) interfaces for the query and analysis of the early warning system outputs; (v) criteria, tools and technological supports for the validation of the early warning system outputs. The main lessons learned in the last decade and the most critical issues experienced implementing such framework for the entire Italian territory (SANF system) and for different regions both in Italy and India (SARF systems, LANDSLIP LEWS system) are highlighted and discussed.

Gully erosion is a significant geo-hydrological phenomenon occurring worldwide. Such phenomena contribute significantly to the soil erosion on a catchment and to its morphological shaping. In so doing, it impacts all the processes acting in a hillslope. Being one of the principal processes of soil erosion, gully erosion will play an important role in worsening the effects of climate and land use changes in the near future. Therefore, the prediction of the spatial and temporal occurrence of these phenomena is an interesting problem both for the scientific world and more in general for the society. In this work we model gully erosion on different study areas, using a distributed pixel-based model. The LANDPLANER model was used for the purpose, mainly due to its ability to deal with scenario-based analysis and due to the limited requirements of input data in its basic configuration, which include a DEM and a land cover map to derive the parameters of the runoff through the Curve Nuumber method. The twofold erosion modelling schema integrated in LANDPLANER, namely a quasi-static topographic threshold approach and a dynamic simplified erosion index, were tested in some study areas, considering the local morphological, climatological and Land Use/Land Cover conditions. We propose a framework to derive such dynamical model input data to better characterize the spatial and temporal occurrence of gully phenomena. Open source (SENTINEL-2) and commercial satellite (WorldView 3) data are used for the purpose and with the main objectives to perform long term, seasonal, event and scenario-based modelling analyses. Modelling results were validated using geomorphological and gully phenomena inventories, using different spatial criteria and different performance metrics. We maintain that the tested modelling and validation approaches can be easily replicated and applied in other different study areas to better characterize the spatial and temporal occurrence of gully erosion phenomena.

The prediction of the location of gully heads in various environments is an important step towards predicting slope and catchment dynamics and to properly estimate soil erosion rates. So far, data collection and modelling of topographic thresholds for gully head prediction has mainly focused on forested areas, rangelands, pastures and cropland. However, such data for badlands are very scarce, even if such environments are most interesting to study gully processes, resulting from the complex interaction between soil degradation and erosion, and soil building processes. In this study we extend the database on topographical thresholds for gully head formation through data collection in badland areas and to improve the prediction of gully head development in a given landscape. For this purpose, we selected different badland sites in the Mediterranean that are characterized by different badland morphologies that developed in differed geo-environmental conditions. The analysis of the conditions under which gully heads developed allowed to refine a recently reported gully head threshold equation, and to illustrate how to use the updated model. This model shows that the resistance to gully head formation depends on slope gradient and drainage area at gully heads, land use in the gully catchment at the moment of gully development (expressed numerically by parameters derived from the Runoff Curve Number method), surface rock fragment cover, presence of joints, pipes, and factors/processes affecting soil detachment rate. This study improves our understanding of environmental conditions that control the development of gully heads in various badland types through a combination of field data collection of gully heads and modelling.

The Mediterranean region has been identified as one of the main climate change hotspots_ its sensitivity to global change is high and its evolution remains uncertain. The region experiences many interactions and feedbacks at the oceanic, atmospheric, and hydrological levels, while facing high anthropogenic activities. Analysing the water cycle over the Mediterranean region is of major importance to environmental and socio-economic aspects. The satellite monitoring of the Mediterranean water cycle represents one of the key challenges for the hydrological community. The presentation will show recent results on using satellite soil moisture and precipitation products for hydrometeorological prediction in the Mediterranean region, and particularly for the prediction of floods and landslides. Specifically, we will show the comparison of multiple satellite precipitation products for predicting flood in 100+ basins over the Mediterranean Basin by also using different hydrological models. Moreover, the assessment of satellite precipitation products for predicting the occurrence of landslides in Italy is carried out. Among the investigated satellite products, we have firstly considered state-of-the-art products such as TMPA (TRMM Multisatellite Precipitation Analysis), GPM (Global Precipitation Measurement), and H SAF (EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management). Secondly, we have tested the innovative products using the SM2RAIN algorithm for rainfall retrieval from multiple satellite soil moisture products including ASCAT (Advanced SCATterometer), SMOS (Soil Moisture and Ocean Salinity mission) and SMAP (Soil Moisture Active and Passive mission).

The definition of empirical rainfall thresholds for the prediction of landslide occurrence is conditioned by several issues. The most debated ones are_ the definition of objective and automatic procedures for threshold calculation; the evaluation and quantification of diverse uncertainties resulting from data and methods; the definition of validation procedures; the implementation of thresholds into landslide early warning systems. However, threshold reliability strongly depends on the quality and quantity of input data, e.g. rainfall time series and information on landslide occurrence. In particular, the temporal resolution of rainfall data influences the equation, the shape and the validity range of the thresholds. Recent studies have proved that the use of rainfall data with coarse temporal resolution causes a systematic underestimation of thresholds, due to the overestimation of the duration of the rainfall events responsible for the failures. Moreover, thresholds calculated using daily rainfall data are always lower and steeper than those defined with hourly data. These issues have relevant implications when the thresholds are implemented in warning systems for the operative prediction of landslide occurrence. In this work, we analyse how the rainfall temporal resolution influences the definition of rainfall thresholds, their validation and the uncertainty associated with them. For the purpose, we use hourly rainfall measurements collected by a network of 172 rain gauges and geographical and temporal information on 561 rainfall-induced landslides occurred in Liguria region (northern Italy) between October 2004 and November 2014. We use a comprehensive tool, already published, that automatically reconstructs the rainfall conditions responsible for the failures and calculates frequentist cumulated event rainfall-rainfall duration (ED) thresholds. To evaluate the influence of different temporal resolutions we cluster the rainfall data in different, increasing bins of 1, 3, 6, 12, and 24 hours (representing decreasing temporal resolutions). We apply the automatic tool to reconstruct rainfall conditions responsible for failures and to define ED thresholds. We find that the rainfall temporal resolution influences considerably the threshold definition. In particular, decreasing the rainfall temporal resolution, we obtain thresholds with a smaller intercept, a higher slope, a shorter range of validity, and higher uncertainties. On the other hand, it seems that the rainfall temporal resolution does not influence the validation procedure and the threshold performance indicators.