PEOPLEPiero Campalani

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Piero Campalani

Senior Researcher
Center for Climate Change and Transformation

T +39 0471 055 263

piero.campalani@eurac.edu

https://orcid.org/0000-0002-1032-9825

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About

Anwendungen der Fernerkundung und geografische Modellierung im Allgemeinen für eine bessere Erde.

Frühere Forschungs- und Berufserfahrungen umfassen:

Geostatistische Regressionen von Luftqualitätsindizes aus Satellitenbildern
Mehrdimensionale Geodatensatz-Datenbanken und Webstandards
Humanitäre Minenräumung GIS und autonome UGV-Navigation

Gegenwärtig beschäftigt er sich mit der Analyse, dem Management und der Verringerung von Naturgefahren (mehrere).

Externen Profilen

ORCID : https://orcid.org/0000-0002-1032-9825
Research Gate : https://www.researchgate.net/profile/Piero-Campalani
LinkedIn : https://www.linkedin.com/in/piero-campalani-b134a619/

Scientific Publications

Inbook2026

CyclOps: Leveraging Semantic Technologies for AI and Data Life Cycle Management and Governance

Caballero M, Queralt A, Calvanese D, Oikonomidis A, Muonagor C, Abella A, Campalani P, Cogrel B,

Piero Campalani

Artificial intelligence (AI) and data-driven applications are transforming industry and research across domains. Despite research advances, the management of the AI and data life cycle in organizations remains largely manual and ad hoc. At the same time, data spaces initiatives are emerging, providing new data-sharing opportunities but posing new challenges for organizations seeking to leverage all their potential. Tackling these challenges, this chapter describes the new framework proposed by the CyclOps project, which aims to enable interoperable and trustwor thy automatic management, governance, and maintenance of the entire data life cycle for large-scale volumes of data generated in heterogeneous distributed sources. This supports the development and deployment of AI-based applications across both busi ness and research contexts. CyclOps operationalizes the end-to-end data life cycle, placing at its core knowledge graphs, an established semantic formalism to represent data and metadata adhering to the FAIR Principles, while capturing relevant informa tion to improve reproducibility, traceability, and explainability of the AI results. This core layer is complemented with tools for the automation of data management tasks; distributed data processing; AI tools, algorithms, and models; data space interoper ability; and a human-centric interface. The chapter presents the key innovative prop ositions of CyclOps and its underlying technologies and illustrates them through selected use cases that highlight the general applicability of the approach.

DOI Quelle

DOI: 10.1007/978-3-032-10561-5_14

Database2025

Alpine-wide climate indices from reanalyses and EURO-CORDEX projections for different Global Warming Levels

Lehner S, Crespi A, Žun M, Haslinger K, Pistotnik G, Maines E, Vlahović Ž, Campalani P,

Piero Campalani, Alice Crespi, Elena Maines

This data set contains calculated and aggregated climate indices for Reanalysis and EURO-CORDEX data on Global Warming Levels (GWLs) for the Alpine Space.

DOI Quelle

DOI: 10.5281/zenodo.14704313

Journal article2025

Assessing Climate Impact on Heritage Buildings in Trentino—South Tyrol with High-Resolution Projections

Stella Blavier CL, Maines E, Campalani P, Huerto-Cardenas HE, Del Pero C, Leonforte F

Piero Campalani, Elena Maines

Climate variations impact the preservation of heritage buildings, necessitating a strategic understanding of potential effects to effectively guide preservation efforts. This study analyzes temperature- and precipitation-dependent climate-heritage indices in Trentino–South Tyrol using EURO-CORDEX regional climate models for the period 1971–2100 under RCP 4.5 and RCP 8.5 scenarios. The selected indices were calculated with climdex-kit and relied on bias-adjusted temperature and precipitation data with a 1 km spatial resolution. The obtained results indicate a geographically punctuated increase in biomass accumulation on horizontal surfaces, a slight decreasing trend in freeze–thaw events, an increase in growing degree days indicating a small, heightened insect activity, and a rise in heavy precipitation days. The Scheffer Index shows a significantly increased potential for wood degradation, particularly under the RCP 8.5 scenario, while the Wet-Frost Index remains consistently low. Finally, according to each identified hazard, adaptive solutions are suggested. These findings provide critical insights into future climate impacts on heritage buildings in the region, aiding stakeholders in planning targeted interventions. The study emphasizes the crucial role of integrating detailed climate data into heritage preservation strategies, advocating for the inclusion of future risk analysis in the “knowledge path” in order to enhance the resilience of buildings.

DOI Quelle

DOI: 10.3390/atmos16070799

Journal article2025

climdex-kit: an open software for climate index calculation, sharing and analysis towards tailored climate services

Campalani P, Crespi A, Pittore M, Zebisch M

Piero Campalani, Massimiliano Pittore, Marc Zebisch, Alice Crespi

The paper presents the open-source software climdex-kit which includes modules to compute, analyze and visualize climate indices based on the input data, target domain and temporal extent defined by the user. It is intended to ease the retrieval and interpretation of meaningful information for climate change studies and support the development of climate services for sectoral applications with flexible options for tailoring the expected outputs. It currently includes the computation of 38 indices based on temperature and precipitation, describing both mean and extreme climate conditions, and it is designed to work with multi-model ensembles of climate projections. The tool is written in Python and integrates utilities from the well-established Climate Data Operators (CDO) and NetCDF Operators (NCO) libraries. The specific capabilities for filtering, aggregating and visualizing insightful information out of the computed indices ensembles make this software rather unique in the still rich landscape of available akin libraries, and are thought to help users to produce tailored results improving the understanding and communication of future climate change. The package climdex-kit can be used directly in interactive Python shells or be integrated as a building block in more complex data processing workflows. New indices can be easily configured and the software can be re-used on the spatial and temporal domains required by the specific application. Moreover, with its utilities to publish the climate indices in open catalogues, this software can be a one stop shop for a FAIR and simplified computation and management of such complex multi-scenario settings. To show the functionalities of the package as well as its potential use for conducting regional climate assessments, the application of climdex-kit to an ensemble of climate model projections for the Trentino – South Tyrol region (north-eastern Italy) is presented. Examples of derived information for a selection of indices are reported and different visualization options discussed.

DOI Quelle

DOI: 10.1016/j.envsoft.2025.106442

Internet2025

Impact Chains: A Conceptual Modelling Approach for Climate Risk Assessment

Campalani P, Cocuccioni S

Piero Campalani, Silvia Cocuccioni

Impact Chains are a conceptual model based on cause-effect chains that include all major factors and processes leading to specific climate risks. After being first developed by Eurac Research for climate risk assessments (CRAs) in the European Alps around 10 years ago, they have now been applied in almost twenty national climate risk assessments worldwide (e.g., Burundi, Bangladesh, Thailand, Vietnam, Madagascar).

This training session will go through the terminology and concepts of climate risk and CRA in general, which will be preparatory for understanding the concepts of the Impact Chains and how they are built. After a brief outlook on the current research directions in progress at Eurac Research, the event will conclude with a simple “hands-on” example of building a demonstrative Impact Chain for a simple CRA scenario.

DOI Quelle

DOI: 10.5281/zenodo.15236360

Conference proceedings article2023

A data-driven approach to derive spatially explicit dynamic "thresholds" for shallow landslide occurrence in South Tyrol (Italy)

Steger S, Moreno M, Crespi A, Gariano SL, Brunetti MT, Melillo M, Peruccacci S, Marra F,

Piero Campalani, Massimiliano Pittore, Alice Crespi

Projekt: Integration von statischen und dynamischen Rutschungsursachen mittels datengetriebenen und physikalisch-basierten Methoden auf multiplen Maßstabsebenen - Erforschung neuer Möglichkeiten zur Vorhersage flachgründiger Rutschungen

When and where shallow landslides occur depends on an interplay of predisposing, preparatory, and triggering factors. At a regional scale, data-driven analyses are extensively used to assess landslide susceptibility based on “static” maps of predisposing conditions. In contrast, data-driven analyses focusing on landslide triggering factors often rely on non-spatially explicit approaches to derive empirical rainfall thresholds. So far, few attempts have been made to integrate the spatial and temporal analysis domains beyond a posterior combination of separately derived susceptibility models and rainfall thresholds.

This work focuses on the mountainous Italian province of South Tyrol (7400 km²) and proposes a novel data-driven landslide prediction model that jointly considers landslide predisposition and dynamic preparatory and triggering factors. The approach builds on a hierarchical generalized additive model, multi-temporal shallow landslide data from 2000 to 2020 and a range of environmental variables (e.g., daily rainfall, topography, lithology, forest cover). The model produces maps that portray the relative probability of landslide occurrence. These spatially explicit predictions change dynamically as a function of local predisposition, seasonality, and observed (or hypothesized) dynamic preparatory and triggering rainfall (i.e. cumulative rainfall amounts based on varying day-windows). Linking the model output to known measures of model performance, such as hit rate and false alarm rate, enables the creation of dynamic classified maps that can be interpreted in analogy to commonly used empirical rainfall thresholds. The approach also accounts for potential spatial and temporal biases in the landslide inventory by restricting the underlying data sampling to effectively surveyed areas and time periods and by including (and averaging out) bias-describing random effect variables. Our validation confirms the model's high generalizability and predictive power while providing insights into the interplay of predisposing, preparatory and triggering factors for shallow landslide occurrence in South Tyrol. Application possibilities of this novel approach are discussed.

The research leading to these results is related to the PROSLIDE project that received funding from the research program Research Südtirol/Alto Adige 2019 of the Autonomous Province of Bozen/Bolzano – Südtirol/Alto Adige.

DOI Quelle

DOI: 10.5194/egusphere-egu23-1353

Journal article2023

Assessing OpenStreetMap roads fitness-for-use for disaster risk assessment in developing countries: The case of Burundi

Campalani P, Pittore M, Renner K

Piero Campalani, Kathrin Renner, Massimiliano Pittore

Evidence shows how disasters caused by extreme weather events are surely becoming more frequent at all
latitudes, and are definitely representing an ever-growing risk in the floods- and landslides-prone territory of Burundi. The still present inequalities of its society and the vulnerable economy make this African country a very relevant and particularly complex case for hazards risk management. A fundamental step in this task is to correctly map the so-called exposure elements to risk, which most notably comprise the population and households, the critical infrastructure, and the transportation network. In this article, we assess the quality of the OpenStreetMap (OSM) roads database in Burundi at national level, with a perspective in its fitness-for-use in the assistance for disaster risk mitigation purposes, which were adopted in the context of a
recent multi-hazards risk assessment and mapping exercise on behalf of the International Organisation for
Migration. Despite not touching all aspects of volunteered geographic information data quality that are commonly found in literature for a comprehensive assessment, we believe the information provided in this study can be of help for stakeholders in their decision to use the OSM dataset in an otherwise data-scarce area.

DOI Quelle

DOI: 10.1515/geo-2022-0485

Journal article2023

Border-independent multi-functional, multi-hazard exposure modelling in Alpine regions

Pittore M, Campalani P, Renner K, Plörer M, Tagliavini F

Piero Campalani, Kathrin Renner, Massimiliano Pittore

Extreme hydrometeorological events such as the 2018 Vaia storm increasingly threaten alpine regions with multiple hazards often compounded and with cascading effects. Currently available risk assessment and prevention tools may therefore prove inadequate, particularly for transborder and vulnerable mountain areas, calling for comprehensive multi-hazard and transdisciplinary approaches. In particular, the exposed assets should not anymore be considered a sheer collection of static items, but the models should also reflect functional features. In this paper, we propose an integrated approach to multi-hazard exposure modelling including both static and functional components. The model is based on a homogeneous planar tessellation composed of hexagonal cells and a graph-like structure which describes the functional connections among the cells. To exemplify the methodology, a combination of static (buildings, protective forests), dynamic (population) and functional (road-based transport system) components has been considered together, targeting a ca. 10,000 km² region across Italy and Austria. A cell-based aggregation at 250 m resolution and an innovative graph-based simplification allow for a good trade-off between the complexity of the model and its computational efficiency for risk-related applications. Furthermore, aggregation ensures protection of sensitive data at a scale still useful for civil protection. The resulting model can be used for different applications including scenario-based risk analysis and numeric simulation, probabilistic risk assessment, impact forecasting and early warning.

DOI Quelle

DOI: 10.1007/s11069-023-06134-3

Presentation2022

Multi-hazard, cross-border storm risk assessment in the Alps. First insights from the TRANS-ALP project

Renner K, Campalani P, Crespi A, Dainese R, Enigl K, Haslinger K, Pittore M, Plörer M,

Alice Crespi, Marc Zebisch, Kathrin Renner, Massimiliano Pittore, Piero Campalani

Extreme hydrometeorological events such as late autumn and winter storms are being increasingly observed in southern Europe and particularly in the Alps, where they threaten environmental and socio-economic systems. An example is the 2018 Vaia (also known as Adrian) storm (Oct 28-Nov 04), which strongly affected Italy, Austria, France and Switzerland. Over the past decades several damaging storms strongly impacted (i.e., caused adverse consequences on assets, people, infrastructure or the environment) mostly those countries on the northern side of the Alps (e.g., Vivian 1990, Lothar 1999, Gudrun 2005, Kyrill 2007). The Vaia storm however affected the southern side, downing more than 8 million cubic meters of forests and causing extensive damage due to a combination of multiple compounded hazards including heavy rain, flooding and landslides, and strong winds. The event caused 12 fatalities and an economic loss exceeding 3 billion Euro. This storm has been considered exceptional yet could foreshadow multi-hazard phenomena whose frequency and intensity are likely to be influenced by climate change. In such conditions, currently available risk assessment and prevention tools may prove inadequate, particularly on a cross-border level and in vulnerable mountainous regions. Therefore, there is a need to provide decision makers and stakeholders with improved and harmonised tools and standardised frameworks to conduct efficient (climate) risk assessments for cross-border areas. Current and future impacts need to be systematically investigated to adopt prevention and disaster risk reduction measures for the mitigation of inherent risks. In its first year the TRANS-ALP project analysed the occurrence of severe weather events that can be classified as extreme and their specific features in the cross-border area between Austria and Italy (Trentino-Alto Adige/South Tyrol and Veneto). Furthermore, a systematic review of the mechanisms in place to collect impact, damage and loss data has been conducted to allow for a better conceptualisation of the different risk pathways that come into play in case of intense storms. Our findings indicate a noticeable increase of extreme weather conditions that can lead to adverse consequences, also from a systemic perspective, and a complex interplay of damaging factors and chained impacts that can extend for years after the occurrence of the generating events. The findings also highlight the importance of a comprehensive multi-hazard and transdisciplinary approach to storm risk assessment within a framework harmonising Disaster Risk Reduction (DRR) and Climate Change Adaptation (CCA) instances. In this contribution the first results and insights of the project will be presented and discussed.

The described research activities have been carried out within the framework of the DG-ECHO project TRANS-ALP funded by the European Union (Grant Agreement 101004843).

DOI Quelle

DOI: 10.5194/egusphere-egu22-12811

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Presentation2022

Multi-hazard, cross-border storm risk assessment in the Alps. First insights from the TRANS-ALP project

Renner K, Campalani P, Crespi A, Dainese R, Enigl K, Haslinger K, Pittore M, Plörer M,

Alice Crespi, Marc Zebisch, Kathrin Renner, Massimiliano Pittore, Piero Campalani

The described research activities have been carried out within the framework of the DG-ECHO project TRANS-ALP funded by the European Union (Grant Agreement 101004843).

DOI Quelle

DOI: 10.5194/egusphere-egu22-12811

Piero Campalani

About

Scientific Publications

CyclOps: Leveraging Semantic Technologies for AI and Data Life Cycle Management and Governance

Alpine-wide climate indices from reanalyses and EURO-CORDEX projections for different Global Warming Levels

Assessing Climate Impact on Heritage Buildings in Trentino—South Tyrol with High-Resolution Projections

climdex-kit: an open software for climate index calculation, sharing and analysis towards tailored climate services

Impact Chains: A Conceptual Modelling Approach for Climate Risk Assessment

A data-driven approach to derive spatially explicit dynamic "thresholds" for shallow landslide occurrence in South Tyrol (Italy)

Assessing OpenStreetMap roads fitness-for-use for disaster risk assessment in developing countries: The case of Burundi

Border-independent multi-functional, multi-hazard exposure modelling in Alpine regions

Multi-hazard, cross-border storm risk assessment in the Alps. First insights from the TRANS-ALP project

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Externe Inhalte

Scientific Publications

CyclOps: Leveraging Semantic Technologies for AI and Data Life Cycle Management and Governance

Alpine-wide climate indices from reanalyses and EURO-CORDEX projections for different Global Warming Levels

Assessing Climate Impact on Heritage Buildings in Trentino—South Tyrol with High-Resolution Projections

climdex-kit: an open software for climate index calculation, sharing and analysis towards tailored climate services

Impact Chains: A Conceptual Modelling Approach for Climate Risk Assessment

A data-driven approach to derive spatially explicit dynamic "thresholds" for shallow landslide occurrence in South Tyrol (Italy)

Assessing OpenStreetMap roads fitness-for-use for disaster risk assessment in developing countries: The case of Burundi

Border-independent multi-functional, multi-hazard exposure modelling in Alpine regions

Multi-hazard, cross-border storm risk assessment in the Alps. First insights from the TRANS-ALP project