Abstract. Hazard mapping is carried out in Italy according to the AINEVA guidelines, which require (i) data driven avalanche dynamic modelling to assess end mark and pressure, and (ii) assessment of maximum yearly three-day snow depth increase h72 for 30 to 300 years return period. When no historical avalanche data are present, model tuning and data based assessment of avalanche return periods are hardly feasible. Also when (very) short series of h72 are available, station based quantile estimation for such high return periods is very uncertain, and regionally based approaches can be used. We apply an index value approach for the case study avalanche of Rigopiano, where a 105 m3 snow mass hit the Rigopiano Hotel killing 29 persons on January 18th 2017. This area is poorly monitored avalanche wise, and displays short series (max 14 years) of snow depth measurements, no historical avalanche maps are available on the avalanche track, and no hazard maps have been developed hitherto. First, we tune the recently developed Poly-Aval dynamic avalanche model (1D/q2D) against the 18th January event data (release zone, release depth, end mark) from different sources. We then use snow data from 7 snow stations in Abruzzo (75 equivalent years of data) to tune a regionally valid distribution of h72. We then calculate the 30-years, 100-years, and 300-years runout zone and flow pressures, including confidence limits. We demonstrate that (i) properly tuned 1D/quasi2D models can be used for avalanche modeling even within poorly monitored area as here, and (ii) the use of regional analysis allows hazard mapping for large return periods, reducing greatly the uncertainty against canonical, single site analysis. Our approach is usable in poorly monitored regions like Abruzzo here, and we suggest that (i) avalanche hazard mapping needs to be pursued with regional approaches for h72, and (ii) confidence limits need to be provided for the proposed zoning.

<p>Flood risk in Italy is a wide-spread and never-ending issue. Traditional flood defense focused on making the river system “resistant” to flood events, possibly by flood-control structures including floodwalls, levees, dams and channels. These actions reduce the frequency of inundations, but they do not affect flooding effects, and associated impacts once the flood plain is inundated. In facts, structural flood defenses are designed and operated to accommodate floods not exceeding a given magnitude, as fixed by the original design. Thus, these engineering works are highly inefficient to cope with capacity-exceeding floods, the magnitude of which was fixed many years ago using poor data sets, and it is expected to increase with climate changes.</p><p>FLORIMAP (Smart FLOod RIsk MAnagement Policies), a project funded by Fondazione CARIPLO aims to revalue extreme floods distribution in the different homogeneous areas of northern Italy using regional approaches based upon recent data form the last three decades.</p><p>FLORIMAP will first cover open issues associated with the quantification of flood hazard and inundation risk, then it will assess human exposure and vulnerability, and combine these issues with strategies of communication and risk management, because risk communication is an important activity that can influence the flood risk management. Communication is the bridge between the technical and professional community, decision makers, elected officials, funding sources, and the public at large. The literature on risk communication and perception has highlighted that the understanding of the psychological perception of environmental risk is a crucial factor in order to foster the community resilience and to promote adaptive attitudes and behaviors.</p><p>Here, we present a preliminary assessment of updated extreme values distribution for the case study of Northern Italy hydrologically homogeneous regions. The results will be then compared against those obtained with previous dataset dating until 1970, to study the evolution of flood hazard and inundation risk under recent climate change. We then provide application of flood hazard, and risk for a case study area, and demonstrate modified hazard under recent climate change.</p><p>We then discuss implications for risk communication in the target areas, and provide suggestions for prosecution of the FLORIMAP project. </p>

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<p>Mass wasting is a major landform shaping process in mountainous and steep terrains, and Italy is among the most affected countries in Europe. Lombardia region has 130.450 landslides mapped, covering an area of 3.300 km<sup>2</sup> (i.e. 7.2% of the regional area). The 41% of landslides in Lombardia are rapid mass movements involving shallow soils, occurring mainly in the Alps and Fore-Alps. Many shallow landslides (SLs) result from infrequent meteorological events, inducing unstable conditions, or accelerate movements on otherwise stable slopes. In mountainous areas such as the Alps of Lombardia region, snowmelt concurs with rainfall intensity, and duration in setting the hydrologic conditions favorable to the occurrence of SLs. However, snowmelt contribution to SLs triggering is little investigated hitherto.  In regions experiencing seasonal snowmelt in spring and summer, melting water thereby could decrease the intensity and duration of rainfall needed for SL initiation, or even lead to LSs in dry weather conditions.  </p> <p>Under the umbrella of the project MHYCONOS, a project founded by Fondazione CARIPLO, we developed a robust, and parameter wise parsimonious model, that mimics the triggering mechanism of shallow landslides by accounting for the combined effect of precipitation duration and intensity in, and snowmelt at thaw. The model is applied to the case study of Tartano basin, paradigmatic of SLs in the Alps of Lombardia, where in July 1987 a SL event produced 30 fatalities.</p> <p>Our results show that about 37% of the Tartano Basin slopes display unstable condition, and more than 50% therein is influenced by soil moisture variation. Using a traditional (i.e. rainfall based) approach, occurrence of shallow landslides is predicted only during rainy periods, mainly October and November. In contrast, when including snow melt, the model mimics failures potentially also during April and May, when melting rate is the highest, and may increase triggering potential of rainfall. Currently, our efforts are aimed to conduct interviews and construct temporally based datasets, where occurrences of snow melt driven failures can be evidenced.</p> <p>Risk perception by population may change, and public authority may be prepared to implement emergency plans in order to prevent injuries, causalities and damages to infrastructures also during spring time, when shallow landslides may occur in response to fast snow melt, even during clear sky days, in lack of precipitation.</p> <p> </p>