PEOPLEMassimiliano Pittore

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Massimiliano Pittore

Research Group Leader
Center for Climate Change and Transformation

T +39 0471 055 285

massimiliano.pittore@eurac.edu

https://orcid.org/0000-0003-4940-3444

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About

Physicist, PhD in Information Science and Systems Engineering with extensive experience in natural hazard and risk assessment, impact forecasting, data mining and processing.

Senior researcher and group leader of “Climate and Disaster Risk” group. He has come to Eurac Research in 2020 years ago after having previously worked at the German Research Centre for Geosciences GFZ-Potsdam, Germany, where he led the unit “Early Warning and Impact Forecasting” in the Section Seismic Hazard and Risk Dynamics. He has led the several international, cross-disciplinary research groups focusing on quantitative risk assessment, modelling of exposure and vulnerability, and use of remote sensing and artificial intelligence.

Main research interests include: probabilistic risk assessment, geospatial and geostatistic modelling, dynamic exposure modelling, impact forecasting and early warning, conceptual models and knowledge engineering.

Scientific Publications

Journal article2026

Recent and future variability in 1-day precipitation extremes in Trentino - South Tyrol (Eastern Italian Alps) based on observations (1956-2023) and climate model projections

Maines E, Crespi A, Pittore M, Zebisch M

Massimiliano Pittore, Marc Zebisch, Alice Crespi, Elena Maines

Precipitation extremes are some of the most impactful events in mountain environments due to their triggering potential for hazards, such as gravitational mass movements and floods, damaging human settlements and economic activities. Intensifying extreme precipitation has been documented for the Alps and projected for the future decades, although with substantial spatial variability. Understanding ongoing and future changes in heavy precipitation characteristics on a regional scale is crucial for informing risk-oriented applications and supporting local adaptation. This study presents a comprehensive assessment of 1-day precipitation extremes in Trentino – South Tyrol (eastern Italian Alps), based on past observations and climate model projections. We analyze observed trends and variability in intensity over the period 1956-2023 and assess changes under future global warming levels. A regional frequency analysis adopting a data-pooling approach is applied to estimate current and future occurrence probabilities of 1-day precipitation extremes. Increasing intensity and frequency are observed, with significant trends mostly in the northern part of the region, especially for autumn, summer and on an annual level. However, the long-term signal reveals decadal variations and a more pronounced increase over recent years. Greater intensity and frequency are also projected for the future throughout the region with major changes for higher warming levels and rarer events. Precipitation events like the one recorded during the exceptional Vaia Storm (October 2018) turn out to be more probable today than in earlier decades, and their recurrence interval is projected to further decrease by more than 50 % under the highest warming scenarios.

DOI

DOI: 10.1016/j.wace.2026.100880

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 Source

DOI: 10.1016/j.envsoft.2025.106442

OtherContribution2025

Correlation analysis between meteorological and insurance data to support innovative insurance products for agriculture

Pilotti F, Dall‘Amico M, Olaya Calderon L, Pittore M, Bombardelli S

Massimiliano Pittore, Liz Olaya Calderon

The development of innovative insurance products for agriculture is gaining increasing attention as climate change intensifies the frequency and severity of extreme weather events. Traditional agricultural insurance models, primarily based on historical yield data and indemnity-based mechanisms, struggle to keep pace with emerging climate risks such as prolonged droughts, heatwaves, and erratic precipitation patterns. Recognising this challenge, the IMPETUS project undertook a comprehensive analysis of agricultural insurance data through task 4.14. The research team examined eight years of comprehensive insurance records provided by ITAS, including both claims and policy data. The analysis proceeded in two phases: first, an exploratory examination of claims and policy patterns to identify loss trends and geographic concentrations of claims; second, a correlation analysis linking precipitation-related losses to a composite risk index that combined hazard factors (accumulated rainfall) with vulnerability factors (crop-specific qualitative assessments).

DOI Source

DOI: 10.5281/zenodo.15641224

Journal article2025

More intense heatwaves under drier conditions: a compound event analysis in the Adige River basin (Eastern Italian Alps)

Lemus-Canovas M, Crespi A, Maines E, Terzi S, Pittore M

Massimiliano Pittore, Alice Crespi, Stefano Terzi, Elena Maines

The Adige River basin has been affected several times in recent years by concurrent very hot and dry conditions. In summers 2015, 2017, and more recently in 2021–2022, severe hydrological droughts compounded and cascaded with wildfire and heatwave events. The chained effect of snow deficit in winter, higher-than-normal temperatures in early spring and heatwaves during summer caused multiple drought impacts. Despite the severe consequences, the role of observed climate change in exacerbating the intensity of the drivers leading to specific hot and dry events and their potential impacts in this region remains poorly understood.

A ranking of compound drought and heatwave events (CDHW) occurring in the Adige River basin between 1950 and 2023 was built using E-OBS precipitation and temperature observations. The ranking was based on a composite index considering both event intensity and the spatial extent of the affected area, derived from the Standardised Precipitation Index at a 6 month scale (SPI-6) and a heatwave definition based on daily maximum temperature (TX). The major 2022 CDHW event, which caused severe environmental and societal impacts in the Adige River catchment, stood out. Occurring in late spring (10–28 May 2022), it ranked fifth out of 119 events detected since 1950 and was the most intense CDHW event in the past 15 years. As one of the most recent and severe CDHW events, the 2022 event was selected for an in-depth characterisation and a climate change attribution analysis of both its meteorological drivers and hydrological impacts.

The changing characteristics of CDHW events similar to that of May 2022 were investigated through a flow-analogue attribution approach based on the reconstruction of its atmospheric conditions using ERA5 geopotential height at 500 hPa. By comparing May 2022 CDHW flow analogues from 1951–1980 (low anthropogenic forcing) and 1992–2021 (moderate-high anthropogenic forcing), we found that heatwaves comparable to the one in May 2022 are now significantly hotter – by 1–4 °C – than historical analogues and occur in a much drier context, characterised by pronounced precipitation deficits. These conditions, along with earlier snowmelt and persistent precipitation deficits, might have exacerbated river flow reductions and water stress in recent years. Also, shifts in the timing of a CDHW event were found to significantly influence the severity of its potential consequences.

However, extracting a reliable signal of future changes in the characteristics of CDHW events from climate projections remains challenging. Based on flow-conditioned analogues of the May 2022 event from 25 EURO-CORDEX simulations, more than half of the models failed to reproduce the observed sign of change in temperature and drought conditions. Unconditioned reconstructions showed closer agreement with observations, particularly for temperature patterns, but critical aspects such as the magnitude of the changes remained underestimated.

DOI Source

DOI: 10.5194/hess-29-6781-2025

Report2025

Reflection paper for EUCRA-2

Zebisch M, Casartelli V, Biesbroek R, Cocuccioni S, Kirkels F, Klein R, Mikaelsson M, Munck af Rosenschöld J,

Silvia Cocuccioni, Massimiliano Pittore, Marc Zebisch

Project: European Topic Centre on Climate change Adaptation and LULUCF

This reflection paper supports the early scoping of the second European Climate Risk Assessment (EUCRA-2). It builds on the direct experience and lessons learned by the authors, who were part of the core team that developed the first EUCRA (EUCRA-1). The paper was informed by a series of expert discussions between January and May 2025, including an in-person workshop at the EEA in Copenhagen. The document incorporates input from the Joint Research Centre (JRC), ECMWF, and insights from informal consultations by DG CLIMA. Aimed primarily at the EUCRA-2 Steering Group, it explores the policy context, scope, methodology, and potential chapter structure of EUCRA-2, while stressing continuity with EUCRA-1 and alignment with relevant international, EU and national initiatives. Although not a substitute for formal scoping by the EEA, DG CLIMA, and EUCRA-2 project partners, it offers preliminary reflections and suggestions to inform the future process.

DOI Source

DOI: 10.25424/cmcc-2y5q-z607

Journal article2025

Risk Storylines: A Community-Led Discussion between Disaster and Climate Science

Cocuccioni S, Pittore M, Zebisch M, Schneiderbauer S, Shepherd TG, Casartelli V, Crummy J, Pedde S

Silvia Cocuccioni, Massimiliano Pittore, Marc Zebisch, Stefan Schneiderbauer

Project: How to adapt to changing weather eXtremes and associated compound RISKs in the context of Climate Change, RETURN - multi-Risk sciEnce for resilienT commUnities undeR a changiNg climate

An international workshop brought together researchers and practitioners to exchange experiences in developing and applying climate and disaster risk storylines. Discussions focused on integrating quantitative and qualitative evidence and on expanding storylines to include nonclimatic risk drivers such as exposure, vulnerability, and their underlying drivers. The event featured a dynamic blend of high-level keynote talks, case studies, practical sessions, and poster presentations. Participants engaged in lively discussions, shared insights, and collaborated on hands-on activities aimed at developing actionable risk storylines

DOI Source

DOI: 10.1175/BAMS-D-25-0155.1

Journal article2024

Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds

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

Massimiliano Pittore, Alice Crespi

Shallow landslide initiation typically results from an interplay of dynamic triggering and preparatory conditions along with static predisposition factors. While data -driven methods for assessing landslide susceptibility or for establishing rainfall -triggering thresholds are prevalent, integrating spatiotemporal information for dynamic large -area landslide prediction remains a challenge. The main aim of this research is to generate a dynamic spatial landslide initiation model that operates at a daily scale and explicitly counteracts potential errors in the available landslide data. Unlike previous studies focusing on space-time landslide modelling, it places a strong emphasis on reducing the propagation of landslide data errors into the modelling results, while ensuring interpretable outcomes. It introduces also other noteworthy innovations, such as visualizing the final predictions as dynamic spatial thresholds linked to true positive rates and false alarm rates and by using animations for highlighting its application potential for hindcasting and scenario -building. The initial step involves the creation of a spatio-temporally representative sample of landslide presence and absence observations for the study area of South Tyrol, Italy (7400 km 2 ) within well -investigated terrain. Model setup entails integrating landslide controls that operate on various temporal scales through a binomial Generalized Additive Mixed Model. Model relationships are then interpreted based on variable importance and partial effect plots, while predictive performance is evaluated through various crossvalidation techniques. Optimal and user -defined probability cutpoints are used to establish quantitative thresholds that reflect both, the true positive rate (correctly predicted landslides) and the false positive rate (precipitation periods misclassified as landslide -inducing conditions). The resulting dynamic maps directly visualize landslide threshold exceedance. The model demonstrates high predictive performance while revealing geomorphologically plausible prediction patterns largely consistent with current process knowledge. Notably, the model also shows that generally drier hillslopes exhibit a greater sensitivity to certain precipitation events than regions adapted to wetter conditions. The practical applicability of the approach is demonstrated in a hindcasting and scenario -building context. In the currently evolving field of space-time landslide modelling, we recommend focusing on data error handling, model interpretability, and geomorphic plausibility, rather than allocating excessive resources to algorithm and case study comparisons.

DOI

DOI: 10.1016/j.gsf.2024.101822

Presentation2024

Combining Impact Chains with Forensic Analysis in the analysis of compound events: insights from the Vaia windstorm case study

Romagnoli F, Cocuccioni S, Olaya Calderon LJ, Pittore M, Schneiderbauer S, Atun F, Van Westen C, Sliuzas R,

Silvia Cocuccioni, Massimiliano Pittore, Liz Olaya Calderon, Stefan Schneiderbauer

Project: Increasing Preparedness And Resilience Of European Communities By Co-Developing Services Using Dynamic Systemic Risk Assessment

Understanding causal linkages and interactions between risk components is crucial to acquire a comprehensive knowledge of natural hazard impacts and improve risk mitigation measures. Despite several advancements in risk science and hazard modelling, the understanding of dynamics and interactions characterizing compound hazards and multi-risk is still limited. Taking stock of current limitations in risk science, our contribution will present a methodology to unravel the interactions among multiple risk components in a multi-risk context. Impact Chains (ICs) are conceptual models and analytical tools to systematize and visualize the interplay among hazard impacts, compound vulnerabilities, and exposure factors, streamlining the chain of cascading impacts characterizing compound events.

Within the EU Horizon PARATUS project we implemented ICs for the analysis of past events, combining it with Forensic Analysis. Forensic Analysis provided the framework to develop the event storyline and identify core elements of impact chains. The combination of these approaches helped identify past and present risk drivers, connecting them with hazard impacts.

To show the merits of this approach, we implemented it in the analysis of Vaia windstorm. The storm hit Italy in 2018 and was characterised by high intensity rainfall and intense wind gusts. Our contribution will present most relevant risk pathways, vulnerabilities and risk drivers and their reciprocal interconnections. The methodology showcases its potential for capturing the variety of risk components, stressing the relevance of connections between physical and social elements. The analysis of past events will provide relevant insights to improve future risk reduction measures, increasing disaster preparedness and resilience.

DOI

DOI: 10.1007/978-3-031-57357-6_40

Journal article2024

Leveraging observations and model reanalyses to support regional climate change adaptation activities: An integrated assessment for the Marche Region, Central Italy

Crespi A, Napoli A, Galassi G, Lazzeri M, Parodi A, Zardi D, Pittore M

Massimiliano Pittore, Alice Crespi

Acknowledging the increasing urgency of climate change, many local administrations, in Italy as well as abroad,
are currently elaborating their own adaptation strategy. A key step of this process is understanding the current
climate, past variability and ongoing trends. Combined with the analysis of vulnerable and exposed elements, it
supports the identification of key climatic impacts and risks for the territory and the elaboration of future scenarios. Several climatic datasets are available for this purpose, ranging from station observations to interpolated
products and to model reanalyses, each with its own features. The study aimed to shed light on these differences
and thus help practitioners make better, more informed decisions. Three gridded datasets, offering global, European and national coverage, were compared to derive a local characterization of mean climatic features, recent
trends and climate extremes for the Marche Region (Central Italy). The assessment was based on temperature and
precipitation variables from the global reanalysis ERA5-Land, the European observation dataset E-OBS, and the
high-resolution reanalysis dynamically downscaled for Italy VHR-REA_IT. The analysis showed that large-scale
products such as E-OBS and ERA5-Land can still represent a robust complement for adaptation planning.
However, important limitations in describing spatial and temporal patterns need to be properly accounted for in
the decision-making process. Only an integrative approach based on a multi-source data evaluation would
properly address the multi-faceted aspects of climate variability on a regional scale, derive a more comprehensive analysis of past and current conditions and better manage the underlying uncertainty

DOI

DOI: 10.1016/j.cliser.2024.100512

Conference proceedings article2024

Local Natural Hazards, Cascading to Cross-Regional Risks and Impacts Along the Main Transit Route from North and South in the Austrian Alps

Wenzel T, Marr P, Glade T, Höfler F, Preißler A, Adams M, Cocuccioni S, Pittore M,

Silvia Cocuccioni, Massimiliano Pittore

Project: Increasing Preparedness And Resilience Of European Communities By Co-Developing Services Using Dynamic Systemic Risk Assessment

Global environmental changes are widely visible in mountainous areas. These changes can influence the susceptibility of critical infrastructure to well documented and novel natural hazards, cascading as well as compounding risks and potentially threaten cross-border mobility. Within the scope of the EU Horizon project PARATUS, the risk in multiple application case study sites is evaluated regarding past events and future scenarios. Here, we focus on the Brenner Corridor (Austria–Italy), one of the main transit routes through the Alps for both road and rail traffic. Compounding events could potentially lead to a complete blockage of the Brenner Corridor. This demonstrates the importance of a better understanding of regional conditions to develop adjusted adaptation strategies with regard to extreme events, such as extreme wind, floods, rockfall, forest fires, landslides, or snow avalanches. In order to investigate the variety of possible challenges, participatory workshops, scenario modelling and impact chains are developed. Both historical disaster events and future scenarios are analysed. Preliminary results show the great variety of hazards and potential impacts resulting from changing environmental conditions, and the complex interaction of multiple hazards in the Brenner Corridor. Generally, stakeholders are very conscious of these challenges, have well-established concepts in place to face single hazards. However, especially the handling of compounding events and cross-border interaction still has room for improvement. The implementation of impact chains and scenario modelling can help practitioners to find adaptation and mitigation strategies for compounding and cascading hazards. Further work includes scenario modelling on different spatial and temporal scales.

DOI Source

DOI: 10.1007/978-3-031-57357-6_32

Journal article2024

Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy

Moreno M, Lombardo L, Crespi A, Zellner PJ, Mair V, Pittore M, van Westen C, Steger S

Massimiliano Pittore, Alice Crespi

Shallow landslides represent potentially damaging processes in mountain areas worldwide. These geomorphic processes are usually caused by an interplay of predisposing, preparatory, and triggering environmental factors. At regional scales, data-driven methods have been used to model shallow landslides by addressing the spatial and temporal components separately. So far, few studies have explored the integration of space and time for landslide prediction. This research leverages generalized additive mixed models to develop an integrated approach to model shallow landslides in space and time. We built upon data on precipitation-induced landslide records from 2000 to 2020 in South Tyrol, Italy (7400 km2). The slope unit-based model predicts landslide occurrence as a function of static and dynamic factors while seasonal effects are incorporated. The model also accounts for spatial and temporal biases inherent in the underlying landslide data. We validated the resulting predictions through a suite of cross-validation techniques, obtaining consistent performance scores above 0.85. The analyses revealed that the best-performing model combines static ground conditions and two precipitation time windows: a short-term cumulative precipitation of 2 days before the landslide event and a medium-term cumulative precipitation of 14 days. We demonstrated the model's predictive capabilities by predicting the dynamic landslide probabilities over historical data associated with a heavy precipitation event on August 4th and August 5th, 2016, and hypothetical non-spatially explicit precipitation (what-if) scenarios. The novel approach shows the potential to integrate static and dynamic landslide factors for large areas, accounting for the underlying data structure and data limitations.

DOI Source

DOI: 10.1016/j.scitotenv.2023.169166

Conference proceedings article2024

The Use of Impact Chains to Describe Complex Cause-Effect Relationships Within a Systemic Multi-sectoral and Multi-hazard Risk Assessment

Cocuccioni S, Romagnoli F, Pittore M, Armas I, Danila DT, Osaci G, Albulescu C, Göksu Ç,

Silvia Cocuccioni, Massimiliano Pittore

Project: Increasing Preparedness And Resilience Of European Communities By Co-Developing Services Using Dynamic Systemic Risk Assessment

The occurrence of hazards and of their resulting impacts is characterised by complex interactions. Impact Chains (ICs) are conceptual models to streamline the analysis of such complex cause-effect relationships and to provide a structured framework for the comprehensive assessment of the related risks and of their components. ICs describe through an intuitive and graphical description the complex chain of cascaded impacts induced by possibly compounded natural and anthropogenic hazards. They can be built around current risks situation and extended through the identification of factors and elements related to a potential future situation. Moreover, they can be adopted to describe past disaster events through their integration with disaster forensic analysis. They are usually co-defined with domain experts in a participatory process and can be supported by desktop-analysis integrating knowledge from empirical evidence and scientific literature. Within the EU Horizon Europe “PARATUS” project, ICs are applied in four Application Case Studies (including the Caribbean, Romania, Istanbul, and Alpine areas) characterised by different hazard interactions and impacts on different sectors. Such ICs are developed though a standardized participatory methodology together with the local or regional authorities. Moreover, the analysis also encompasses several Learning Case Studies, focussing on past disaster events. The resulting ICs will be shared through the PARATUS platform to support the practitioners in the definition, analysis and quantification of the impact of multi-hazard events. This contribution focuses on the ICs methodology adopted within PARATUS while other contributions present the preliminary impact chains resulting from this.

DOI Source

DOI: 10.1007/978-3-031-57357-6_38

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Journal article2025

More intense heatwaves under drier conditions: a compound event analysis in the Adige River basin (Eastern Italian Alps)

Lemus-Canovas M, Crespi A, Maines E, Terzi S, Pittore M

Massimiliano Pittore, Alice Crespi, Stefano Terzi, Elena Maines

DOI Source

DOI: 10.5194/hess-29-6781-2025

Massimiliano Pittore

About

Scientific Publications

Recent and future variability in 1-day precipitation extremes in Trentino - South Tyrol (Eastern Italian Alps) based on observations (1956-2023) and climate model projections

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

Correlation analysis between meteorological and insurance data to support innovative insurance products for agriculture

More intense heatwaves under drier conditions: a compound event analysis in the Adige River basin (Eastern Italian Alps)

Reflection paper for EUCRA-2

Risk Storylines: A Community-Led Discussion between Disaster and Climate Science

Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds

Combining Impact Chains with Forensic Analysis in the analysis of compound events: insights from the Vaia windstorm case study

Leveraging observations and model reanalyses to support regional climate change adaptation activities: An integrated assessment for the Marche Region, Central Italy

Local Natural Hazards, Cascading to Cross-Regional Risks and Impacts Along the Main Transit Route from North and South in the Austrian Alps

Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy

The Use of Impact Chains to Describe Complex Cause-Effect Relationships Within a Systemic Multi-sectoral and Multi-hazard Risk Assessment

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Scientific Publications

Recent and future variability in 1-day precipitation extremes in Trentino - South Tyrol (Eastern Italian Alps) based on observations (1956-2023) and climate model projections

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

Correlation analysis between meteorological and insurance data to support innovative insurance products for agriculture

More intense heatwaves under drier conditions: a compound event analysis in the Adige River basin (Eastern Italian Alps)

Reflection paper for EUCRA-2

Risk Storylines: A Community-Led Discussion between Disaster and Climate Science

Adopting the margin of stability for space–time landslide prediction – A data-driven approach for generating spatial dynamic thresholds

Combining Impact Chains with Forensic Analysis in the analysis of compound events: insights from the Vaia windstorm case study

Leveraging observations and model reanalyses to support regional climate change adaptation activities: An integrated assessment for the Marche Region, Central Italy

Local Natural Hazards, Cascading to Cross-Regional Risks and Impacts Along the Main Transit Route from North and South in the Austrian Alps

Space-time data-driven modeling of precipitation-induced shallow landslides in South Tyrol, Italy

The Use of Impact Chains to Describe Complex Cause-Effect Relationships Within a Systemic Multi-sectoral and Multi-hazard Risk Assessment