Anna Rita Scorzini’s research while affiliated with Civil Protection Department of Italy and other places

This paper introduces INSYDE-content, a novel, probabilistic multi-variable synthetic flood damage model designed to analyze physical damage to household contents on a component-by-component basis. The model addresses a critical gap in current modeling tools, which often overlook the significance of household contents in overall damage assessments. Developed through an expert-based approach and grounded in the scientific and technical literature, INSYDE-content leverages desk-based data to characterize model features, including uncertainty treatment arising from incomplete input data. A sensitivity analysis and a benchmark test against observed losses demonstrate the robust performance of the model and highlight the contribution of different features to damage mechanisms affecting house contents. While in this study INSYDE-content is tailored for illustrative purposes to the hazard, vulnerability and exposure characteristics of Northern Italy, the model is highly adaptable, allowing for its application to different regional contexts through appropriate customization.

This study proposes a novel approach to improve the classification of severe building losses caused by river floods (i.e., identification of buildings with high flood damages). In addition to traditional variables reflecting flood hazard and building vulnerability, we investigate the impact of coping capacity variables (i.e., variables accounting for the preparedness and disaster response of the population and management authorities). These coping capacity variables are evaluated at three different scales: the building level (micro-scale), the census tract level (meso-scale), and the municipality level (macro-scale). Specifically, at the macro- and meso-scale these include: (i) the surprise effect (the ratio of the number of flooded buildings to the number of flooded buildings located in an official flood hazard area), (ii) the overwhelming effect (the fraction of flooded buildings compared to the total number of buildings within each census tract or municipalities), and (iii) flood rarity (the ratio of the peak discharge of the considered event to the 100-year flood peak). A binomial logistic regression model is used to classify flood losses based on field survey data from the extreme 2021 flood in eastern Belgium. Each variable is assessed for statistical significance, physical relevance, and multicollinearity. The results show that macro- and meso-scale coping capacity variables are insignificant in classifying building losses using the current dataset, suggesting that data on the building level are needed to reliably estimate building losses. Instead, the variables that contribute most to the classification are water depth, building footprint area, building finishing level and the heating system location. The performance of the classifier, measured by the AUC value, achieves an accuracy of 83%.

The sensitivity of the agricultural production system to short- and long-term climate variations significantly affects the availability and prices of food resources, raising relevant issues of sustainability and food security. Globally, productive systems have adapted to climate change, leading to increased yields over the past century. However, the extent to which these adaptations mitigate the impacts of short-term climate fluctuations, both extreme and ordinary, remains poorly studied. To evaluate the vulnerability of crop yield to short-term climate fluctuations and to determine whether it changes over time, we conducted a statistical analysis focusing on one of the main crops in the Abruzzo region (central Italy) as a case study: grape. The study involves correlation analysis between opportune climatic indices (SPI and SPEI) and grape yield data over the sixty-year period from 1952 to 2014, aimed at evaluating the impact of short-term climatic fluctuations—both extreme and ordinary—on crop yield. Our findings reveal an increasing correlation, mainly in the summer–autumn season, which suggests a rising sensitivity of the productive system over time. The observed increase is indicative of the Abruzzo grape production system’s adaptation to climate change, resulting in higher overall yields but not enhancing the response to short-term climatic fluctuations.

The sensitivity of the agricultural production system to short- and long-term climate variations significantly affects the availability and prices of food resources, raising relevant issues of sustainability and food security. To evaluate the vulnerability of crop yield to climate fluctuations and to determine whether it changes over time, we conducted a statistical analysis focusing on one of the main crops in the Abruzzo region (central Italy): grape. The study spans the four provinces of Abruzzo and involves the analysis of temperature, precipitation and grape yield over the sixty-year period from 1952 to 2014. Our findings reveal a climatic trend in the region, shifting from temperate to temperate-arid conditions, along with an increasing correlation between crop yield and climatic fluctuations. This suggests a rising sensitivity of the agricultural productive system to climate fluctuations over time. Despite progressively growing yields in the agricultural production system, the increase in sensitivity to climatic fluctuations indicates an inability to maintain high performance under unfavorable climatic conditions. On the other hand, yield fluctuations consistently pose a potential threat to market equilibria.

Short-term climate fluctuations can have a significant impact on the stability of food resource prices, thus threatening food security, even in cases where the crop production system shows good adaptation to climate change and/or increasing average yields over time. This paper illustrates, in detail, a statistical approach aimed at verifying whether the variation of the crop production system vulnerability to climate fluctuation exhibits a trend over time. These methods were applied to the case study of wheat grown in the Abruzzo region (Italy). The results show that, although the wheat crop yield still shows ongoing growth, the correlation between climate fluctuations and yield oscillations exhibits a systematic increase over the past sixty years. Such an increase in climate-related production fluctuations may represent a disturbing element for market equilibria and be potentially harmful for the various economic subjects involved at various scales, such as producers, distributors, investors/financial traders, and final consumers. The statistical approach illustrated provides a framework for monitoring climate impacts and also provides the basis for building up statistical forecasting models to support informed decision making in agricultural management and financial planning.

Coastal communities in various regions of the world are exposed to risk from tsunami inundation, requiring reliable modeling tools for implementing effective disaster preparedness and management strategies. This study advocates for comprehensive multi-variable models and emphasizes the limitations of traditional univariate fragility functions by leveraging a large, detailed dataset of ex-post damage surveys for the 2011 Great East Japan tsunami, hydrodynamic modeling of the event, and advanced machine learning techniques. It investigates the complex interplay of factors influencing building vulnerability to tsunami, with a specific focus on the hydrodynamic effects associated to tsunami propagation on land. Novel synthetic variables representing shielding and debris impact mechanisms prove to be suitable proxies for water velocity, offering a practical solution for rapid damage assessments, especially in post-event scenarios or large-scale analyses. Machine learning then emerges as a promising approach to tackle the complexities of vulnerability assessment, while providing valuable and interpretable insights.