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Over the past decade, the Adige river basin in the Eastern Italian Alps has experienced water scarcity during early spring and late summer, due to a combination of decreased snowmelt, less precipitation, and increasing water demand. This condition has caused tension and disputes between upstream and downstream water users, particularly between hydropower companies in the upstream region (Trentino/South Tyrol) and agricultural users in the downstream areas (Veneto region). The potential for water scarcity impacts to intensify and expand in the future remains a major concern with climate change leading to more frequent warm and snow droughts in the region. Informing the region's administration, institutions, communities, and businesses to manage water scarcity conditions, is essential to prepare and mitigate the potential future impacts. This work aims to explore decision-making options in drought conditions in the Adige river basin, along with the potential impacts of climate change, by exploiting hydrological models for the river basins and for the major reclamation consortium in the area. The study will focus on years with severe drought, such as the 2022 drought period, using simplified decision options and examining how the decisions to meet the water needs of hydropower agencies in the upstream part of the Adige river basin could impact agricultural water use in the downstream part. The analysis will then be repeated in similar conditions, but with the added element of climate change forcing and reduced glacier volumes in the Alps. This study will identify those needs that might not be fulfilled in certain drought scenarios providing valuable insights for decision-makers and supporting the development of effective strategies to prepare and better manage future water scarcity conditions in the region.

La Strategia di Transizione Climatica (STC) “Bosco Clima. Contrastare l'effetto domino dei cambiamenti climatici” è stata redatta nell’ambito del progetto di Fondazione Cariplo “F2C - Fondazione Cariplo per il Clima” dai partner di progetto e con il supporto tecnico-scientifico di Eurac Research. La STC-Bosco Clima si sviluppa in una porzione del territorio di competenza della Comunità Montana Valli del Verbano e dell’Ente Parco Campo dei Fiori, in provincia di Varese. L’area di progetto comprende il bacino del fiume Boesio (Valcuvia), il bacino del Margorabbia (Valtravaglia) e il versante meridionale del monte Campo dei Fiori. L’area si caratterizza per due massicci montuosi prealpini divisi dalle due valli principali in cui predominano le superfici boscate e in cui i centri urbani, di piccola e media dimensione, risultano concentrati in massima parte nei fondivalle, alle quote inferiori. La STC-Bosco Clima è nata dalla volontà degli enti che operano sul territorio - Comunità Montana Valli del Verbano, Ente Parco regionale Campo dei Fiori, Università degli Studi dell’Insubria, Società Schiaparelli - Centro Geofisico Prealpino, CAST e LIPU – di capitalizzare le esperienze pregresse in ambito di gestione forestale, idraulica, naturalistica e di sfruttare l’ottima collaborazione per lavorare intorno ad un progetto comune e condiviso di mitigazione e adattamento del territorio (fiumi, versanti montuosi e habitat di interesse per la biodiversità) ai cambiamenti climatici (CC). I fenomeni con cui si manifestano con maggior evidenza gli effetti dei CC nell’area di progetto si possono circoscrivere all’aumento sia degli eventi estremi di pioggia, vento e grandine sia dei casi di dissesto idrogeologico. Tali fenomeni determinano di riflesso una incidenza negativa sull’ambiente naturale e sulla biodiversità locale. L’elevata presenza di sistemi verdi, soprattutto forestali, esclude il territorio di progetto dal problema delle ondate di calore che invece interessano pericolosamente le aree urbane. La STC-Bosco Clima ha la finalità di pensare al futuro del territorio interessato in chiave resiliente e pro-ambientale partendo dalla risorsa chiave, il bosco, come strumento di mitigazione e adattamento ai CC. Il bosco è un habitat diventato fragile e attaccabile sia a causa di comportamenti umani diretti, quali l’abbandono colturale, sia a causa degli effetti dei CC. La STC-Bosco Clima, quindi, ha come obiettivo principale la risoluzione di problematiche riconducibili ai CC e legate al bosco e ai servizi ecosistemici ad esso collegati (qualità dell’aria e dell’acqua, assetto idrogeologico, biodiversità forestale) e di adattare il territorio ai futuri problemi che il CC genererà, promuovendo la cittadinanza attiva e l’informazione, strumenti rilevanti di cambiamento sociale.

Exposure is an essential component of risk models and describes elements that are endangered by a hazard and susceptible to damage. The associated vulnerability characterizes the likelihood of experiencing damage (which can translate into losses) at a certain level of hazard intensity. Frequently, the compilation of exposure information is the costliest component (in terms of time and labor) of risk assessment procedures. Existing models often describe exposure in an aggregated manner, e.g., by relying on statistical/census data for given administrative entities. Nowadays, earth observation techniques allow the collection of spatially continuous information for large geographic areas while enabling a high geometric and temporal resolution. Consequently, we exploit measurements from the earth observation missions TanDEM-X and Sentinel-2, which collect data on a global scale, to characterize the built environment in terms of constituting morphologic properties, namely built-up density and height. Subsequently, we use this information to constrain existing exposure data in a spatial disaggregation approach. Thereby, we establish dasymetric methods for disaggregation. The results are presented for the city of Santiago de Chile, which is prone to natural hazards such as earthquakes. We present loss estimations due to seismic ground shaking and corresponding sensitivity as a function of the resolution properties of the exposure data used in the model. The experimental results underline the benefits of deploying modern earth observation technologies for refined exposure mapping and related earthquake loss estimation with enhanced accuracy properties.

Landslides of the slide-type movement represent a potential threat to people and infrastructure in mountain areas all over the world. At regional scales, data-driven models are typically used to assess landslide susceptibility, i.e., to map where landslides are more or less likely to occur. Such assessments frequently serve as basic input for landslide risk assessment, applications in spatial planning, and landslide early warning. Data-driven landslide susceptibility models strongly rely on the quality of the landslide inventory data, and therefore, their explanatory power depends on various errors associated with the underlying landslide data collection process. Previous research has highlighted how ignoring systematic errors inherent in the landslide data can lead to erroneous model inference and landslide susceptibility maps with limited practical applicability. In this context, this study aims to counteract the challenge of spatial landslide-inventory biases (e.g., incomplete landslide mapping far from infrastructure) by testing several possibilities to consider information on Effectively Surveyed Areas (ESA). The concept of ESA was introduced in previous research as the areas which were explicitly surveyed during the preparation of the landslide inventory, and hence, landslides occurring outside these areas are not usually reflected in the inventory data. Consequently, accounting for ESA may lead to landslide susceptibility maps more suitable for practical applications. In this contribution, we carried out a comparative analysis of three different landslide susceptibility models that focus on different strategies to handle information on ESA. The analyses focused on the Italian province of South Tyrol (7,400 km²) and built upon landslide data from 2000 to 2020. This data relates to damage-causing and infrastructure-threatening landslide events and therefore ignores slope instability far from the elements at risk. The first tested strategy focused on accounting for the ESA during model fitting while averaging out its effect for the prediction into space. The second strategy builds upon the first strategy but additionally excludes easy-to-classify “trivial” terrain (e.g., alluvial plains, rock faces, water bodies). The third strategy does not account for the ESA during model fitting but considers its information during the preceding sampling process (i.e., the sampling of stable and unstable mapping units is based on an ESA mask). The workflow comprised the delineation of slope units, the derivation of the ESA, and the spatial aggregation of the predisposing factors (e.g., lithology, land cover, topographic indices). The subsequent exploratory data analysis was conducted to explore the association of each factor with landslide occurrence data. Generalized Additive Models were implemented to derive linear or non-linear relationships between shallow landslide occurrence and the predisposing factors and to assess relative variable importance. The results were validated through random cross-validation, spatial cross-validation, and geomorphic plausibility checks. The findings confirmed the importance and benefits of accounting for the ESA. The various results showed how not accounting for the ESA can lead to a misleading depiction of landslide susceptibility in certain areas. This study was framed within the PROSLIDE project, that has received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige.

The probabilistic assessment of risk due to landslides for Disaster Risk Reduction (DRR) purposes in terms of absolute and quantitative metrics (e.g., number of expected fatalities, economic damage) is still quite challenging. If, on the one side, landslide susceptibility models based on the combined statistical analysis of observed events and geomorphological predisposing factors can be efficiently implemented, they must be integrated by further hypothesis and information to capture the complexity of landslides hazard and be efficiently used for the assessment of risk. For instance, most susceptibility models are static and do not formally account for main triggering conditions (e.g., rainfall or seismic activity). Furthermore, they do not include any probabilistic information on the frequency/magnitude relationships of the related events, hence conveying relative and partial information. In this contribution, a simplified framework for probabilistic landslides risk assessment is presented and its application for multi-hazard risk assessment in Burundi is discussed. The proposed approach is based on the integration of multi-temporal susceptibility models accounting for monthly average precipitation patterns into a heterogeneous Poisson point process model. The occurrence process model is used to generate a large portfolio of events, each associated with a feature representing its magnitude whose distribution is modelled by a simple power law. These events can be combined with exposure and fragility/vulnerability information to obtain a probabilistic assessment of risk of different adverse consequences on people, assets and infrastructure. The proposed approach has been exemplified in the context of a multi-hazard risk assessment at national scale for Burundi and has proved successful in providing spatialised absolute and relative risk estimates that could be compared and combined with risk assessments related to other hazards (e.g., earthquakes and floods) with different characteristics and return periods. The practical implementation was based on the available data for the targeted region, which is limited and relies on several assumptions and hypothesis that are accompanied by a significant level of uncertainty. The results have been preliminarily assessed using the data provided by the IOM Emergency Tracking Tool (ETT) from the period 2018-2021. The results indicate that the framework is flexible and can be used to obtain actionable information on risk due to landslides at different temporal and spatial scales. Our findings further highlight the importance of addressing landslide risk from a larger, interdisciplinary perspective, fostering the systematic collection of risk-oriented data (e.g., event inventories including information on damage and loss) and the synergies among different actors involved in DRR and Climate Change Adaptation. The potential and limitations of the proposed approach for regional landslide risk and for multi-hazard risk assessment will be discussed. The described research activities have been carried out within the framework of an international project funded by the European Union, implemented by the International Organization of Migration (IOM) and coordinated by IDOM (Spain).